# A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging

Sébastien Molière, Dimitri Hamzaoui, Guillaume Ploussard, Romain Mathieu, Gaëlle Fiard, Michael Baboudjian, benjamin granger, Morgan Roupret, Hervé Delingette, Raphaële Renard-Penna

► To cite this version:

Sébastien Molière, Dimitri Hamzaoui, Guillaume Ploussard, Romain Mathieu, Gaëlle Fiard, et al. A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging. European Urology Oncology, 2025, 8 (4), pp.1182-1202. 10.1016/j.euo.2024.11.001. hal-04827079

HAL Id: hal-04827079

https://hal.science/hal-04827079v1

Submitted on 7 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging

Sébastien Molière<sup>a,b,c,†,*</sup>, Dimitri Hamzaoui<sup>d,†</sup>, Guillaume Ploussard<sup>e,f</sup>, Romain Mathieu<sup>g</sup>, Gaëlle Fiard<sup>h</sup>, Michael Baboudjian<sup>i</sup>, Benjamin Granger<sup>j</sup>, Morgan Roupret<sup>k</sup>, Hervé Delingette<sup>l</sup>, Raphaele Renard-Penna<sup>m</sup>

**Article info**

**Article history:**
Received 29 August 2024
Received in Revised form
21 October 2024
Accepted 1 November 2024

**Associate Editor:** Morgan Roupret

**Keywords:**
Artificial intelligence
Deep learning
Diagnostic accuracy
Magnetic resonance imaging
Prostate cancer
Systematic review

**Abstract**

**Background and objective:** Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.

**Methods:** A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023. Studies were included if these presented and validated fully automated DL models for csPCa detection on MRI, with pathology confirmation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging.

**Key findings and limitations:** Twenty-five studies met the inclusion criteria, showing promising results in detecting and characterizing csPCa. However, significant heterogeneity in study designs, validation strategies, and datasets complicates direct comparisons. Only one-third of studies performed external validation, highlighting a critical gap in generalizability. The reliance on internal validation limits a broader application of these findings, and the lack of standardized methodologies hinders the integration of DL models into clinical practice.

**Conclusions and clinical implications:** DL models demonstrate significant potential in improving prostate cancer diagnostics on MRI. However, challenges in validation, generalizability, and clinical implementation must be addressed. Future research should focus

<footnote><sup>a</sup> Department of Radiology, Hôpital de Hautepierre, Hôpitaux Universitaire de Strasbourg, Strasbourg, France; <sup>b</sup> Breast and Thyroid Imaging Unit, Institut de cancérologie Strasbourg Europe, Strasbourg, France; <sup>c</sup> IGBMC, Illkirch, France; <sup>d</sup> Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; <sup>e</sup> Department of Urology, La Croix du Sud Hôpital, Quint Fonsegrives, France; <sup>f</sup> IUCT-O, Toulouse, France; <sup>g</sup> Department of Urology, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail), University of Rennes, Rennes, France; <sup>h</sup> Department of Urology, CNRS, Grenoble INP, TIMC-IMAG, Grenoble Alpes University Hospital, Université Grenoble Alpes, Grenoble, France; <sup>i</sup> Department of Urology, Assistance Publique des Hôpitaux de Marseille, Hôpital Nord, Marseille, France; <sup>j</sup> Public Health Department, INSERM, IPLESP, AP-HP, Pitie-Salpetriere Hospital, Sorbonne Universite, Paris, France; <sup>k</sup> Urology, GRC 5 Predictive Onco-Uro, AP-HP, Pitie-Salpetriere Hospital, Sorbonne University, Paris, France; <sup>l</sup> Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France; <sup>m</sup> Department of Radiology, GRC 5 Predictive Onco-Uro, AP-HP, Pitie-Salpetriere Hospital, Sorbonne University, Paris, France</footnote>

<footnote>† These authors contributed equally to this work.</footnote>

<footnote>* Corresponding author. Department of Radiology, Hôpital de Hautepierre, Hôpitaux Universitaire de Strasbourg, 5 Avenue Molière, 67200, Strasbourg, France. Tel. +33-677006487. E-mail address: sebastien.moliere@chru-strasbourg.fr (S. Molière).</footnote>
on standardizing methodologies, ensuring external validation and conducting prospective clinical trials to facilitate the adoption of artificial intelligence (AI) in routine clinical settings. These findings support the cautious integration of AI into clinical practice, with further studies needed to confirm their efficacy in diverse clinical environments.

**Patient summary:** In this study, we reviewed how artificial intelligence (AI) models can help doctors better detect and understand aggressive prostate cancer using magnetic resonance imaging scans. We found that while these AI tools show promise, these tools need more testing and validation in different hospitals before these can be used widely in patient care.

© 2024 The Author(s). Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

# 1. Introduction

Prostate cancer is a heterogeneous disease. Indolent prostate cancer has a high prevalence and very low mortality, while aggressive prostate cancer is the second cause of cancer death in men [1,2]. The number of new cases is expected to nearly double by 2040 due to an aging population and increased life expectancy [3].

Major clinical guidelines recommend prostate magnetic resonance imaging (MRI) prior to biopsies [4,5]. The Prostate Imaging Reporting and Data System (PI-RADS) is the preferred diagnostic approach for prostate MRI [6]. While MRI-driven workflows can help reduce unnecessary biopsies, these are challenged by low specificity and high inter-reader variability [7-9]. The Cochrane meta-analysis by Drost et al [10] on prostate MRI reported pooled sensitivity of 91% and specificity of 37% for detecting clinically significant prostate cancer (csPCa), using saturation transperineal biopsy as the reference standard. Distinguishing between indolent and aggressive components of prostate cancer within the same lesion is also particularly challenging. This coexistence occurs commonly, with 48% of all cancers and 76% of index lesions containing both indolent and aggressive components [11]. This complexity underscores the need for advanced diagnostic tools and techniques to enhance the accuracy and reliability of prostate MRI interpretations.

Artificial intelligence (AI) models have demonstrated promise in medical image analysis across several specialties [12,13] and multiple modalities, including MRI, ultrasound, and histopathology. These can assist in detecting and characterizing prostate cancer aggressiveness, improving patient care by addressing the growing demand for imaging [14]. Accurate AI-based detection, localization, and classification of lesions on MRI, combined with clinical variables such as prostate-specific antigen (PSA) density and biopsy history, can influence treatment planning significantly. For example, patients with csPCa accurately detected and localized on MRI can be targeted with MRI-ultrasound fusion biopsies, while patients with no cancer or with indolent cancer could safely avoid biopsy. Accurate selective identification of aggressive and indolent prostate cancer can also help guide treatment decisions regarding whether to perform radical prostatectomy, focal therapy, or active surveillance [15]. For the moment, however, the widespread adoption of AI systems for prostate cancer diagnosis is hin-

dered by limited scientific evidence on their efficacy [16,17].

Previous systematic reviews included a significantly larger number of machine learning architectures than deep learning (DL) architectures and were conducted before the widespread use of public databases such as PROSTATEx or PI-CAI [18-21]. Given the rise of DL in prostate MRI applications, we sought to explore how DL algorithms could aid in detecting prostate cancer on MR images. Our focus will be on crucial aspects such as data quality, ground truth, utilization of public databases, AI models, and validation. Additionally, we will discuss the challenges associated with implementing these AI-enabled diagnostic tools in clinical practice.

# 2. Methods

This systematic review is registered on PROSPERO (http://www.crd.york.ac.uk/PROSPERO; CRD42023447910) and follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) of Diagnostic Test Accuracy guidelines.

## 2.1. Eligibility criteria

Studies were considered eligible for inclusion based on the following PICO formulation:

1. Population: Human studies involving participants with prostate cancer, where multiparametric MRI (mpMRI) was used, and csPCa was confirmed by pathology (biopsy or prostatectomy)

2. Intervention: Research presenting or validating fully automated DL models aimed at detecting, localizing, or characterizing csPCa on MR images, without requiring a manual input (eg, user-dependent segmentation)

3. Comparison: Not required, as this review focuses on assessing the performance of DL models in isolation, without comparison with other diagnostic methods

4. Outcome: Studies reporting diagnostic performance metrics for the detection, localization, or characterization of csPCa, such as sensitivity, specificity, accuracy, or area under the receiver operating characteristic curve (AUROC)

Eligible studies were published between January 1, 2020, and September 11, 2023.
## 2.2. Exclusion criteria

The following types of studies were excluded:

1. Commentaries, editorials, letters, case reports, and reviews

2. Studies published solely as abstracts or without accessible full-text versions

3. Studies requiring radiologist input for model operation, such as manual lesion segmentation

4. Studies lacking a detailed description of the DL model or without providing diagnostic performance metrics

5. Studies with fewer than 140 patients in the test dataset

6. Models utilizing fewer than two MRI sequences as inputs

## 2.3. Search strategy

Following a preliminary nonsystematic literature review, a comprehensive electronic search of the Medline/PubMed, Embase, Web of Science, and ScienceDirect databases was conducted.

The search terms used were the following:

1. Prostatic Neoplasms OR Prostate Cancer OR Prostate

2. Magnetic Resonance Imaging OR MRI

3. Artificial Intelligence OR AI OR Deep Learning OR Radiomics OR Algorithm OR Computer-Aided OR CAD OR Neural Network

4. Diagnosis OR Detection OR Segmentation OR Characterization OR Sensitivity OR Specificity OR Accuracy OR ROC OR AUROC

5. Test OR Validation

The search strategies are detailed in the Supplementary material. In addition to the comprehensive electronic search, we conducted a nonsystematic review of gray literature, including preprints, which were primarily used to inform the discussion and provide additional context for recent developments in the field.

## 2.4. Study selection

The titles and abstracts of the identified studies were screened independently by two reviewers: a radiologist (S. M.) and an AI expert (D.H.). Full-text articles of studies deemed eligible by either reviewer were then assessed independently by both the reviewers (S.M. and D.H.). Any discrepancies between the two reviewers were resolved by a third coauthor (R.R.P.) through consensus.

## 2.5. Data extraction

Data extraction was carried out using a standardized extraction form by both the reviewers (S.M. and D.H.) independently. Any discrepancies between their extractions were resolved through consensus with the involvement of a third coauthor (R.R.P.).

Extracted data were categorized into the following types:

1. Study details: title, year of publication, country, and journal

2. Study design

3. MRI specifics: field strength, sequence types (T2-weighted [T2W] images, diffusion-weighted images, apparent diffusion coefficient [ADC], and contrast-enhanced images), and bi- or multiparametric nature

4. Imaging features and pre/postprocessing details

5. Neural network details

6. Training and test data: population characteristics, dataset type and size, labeling details, and training/testing split

7. Reference standard: pathological confirmation methods for cancer and criteria for noncancer cases

8. Task: detection, localization, or characterization of csPCa

9. Model specifics: prostate zone analysis, and input and output data

10. Validation data: independency criteria, population details, volume, and comparison tests; the criteria for an independent validation cohort were a different institution, or different MRI machine vendor, or different magnetic field strength, or different population, or different inclusion period [22]

11. Metrics: detection (sensitivity, specificity, accuracy, AUROC, precision recall, and average precision), localization (Dice coefficient and Hausdorff distance), and characterization (free-response receiver operating characteristic and AUROC)

## 2.6. Risk of bias assessment

Owing to identified data heterogeneity, no meta-analysis or statistical pooling was conducted to avoid biased assessments. Instead, a narrative synthesis was performed. The quality and validity of included studies were assessed independently by two reviewers (S.M. and D.H.), with discrepancies resolved by a third coauthor (R.R.P.), using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [23]. We employed an abbreviated version of the CLAIM checklist, selecting the most critical criteria relevant to the prostate cancer field, focusing on the studies' methodology and presentation of results (Table 1). The percentage of CLAIM fulfillment, reflecting adherence to these best practice guidelines, was calculated for each study and expressed as a percentage.

## 2.7. Definitions

AI for prostate cancer detection is used primarily to identify suspicious areas within prostate MRI scans without requiring prior lesion annotations from radiologists. Following Belue and Turkbey [24], we identified three nonexclusive AI tasks handled by algorithms:

1. Detection: Identifying suspicious regions within MRI scans, typically by creating bounding boxes.

2. Classification: Categorizing lesions to differentiate between csPCa and insignificant prostate cancer. These models classify either entire images or preannotated regions of interest, which can be, in general, delineated manually by the radiologist or automatically by a detection/segmentation algorithm. Classification can be in two
<table><caption>Table 1 - Adapted version of the CLAIM checklist</caption><thead><tr><th>Section/topic</th><th>CLAIM no.</th><th>Item</th></tr></thead><tbody><tr><td><strong>Methods</strong></td><td></td><td></td></tr><tr><td>Study design</td><td>5<br>6</td><td>Prospective or retrospective study<br>Study goal, such as model creation, exploratory study, feasibility study, noninferiority trial</td></tr><tr><td>Data</td><td>7<br>8</td><td>Data sources<br>Eligibility criteria: how, where, and when potentially eligible participants or studies were identified (eg, symptoms, results from previous tests, inclusion in registry, patient-care setting, location, dates)</td></tr><tr><td>Ground truth</td><td>13<br>14</td><td>How missing data were handled<br>Definition of ground truth reference standard, in sufficient detail to allow replication</td></tr><tr><td></td><td>16</td><td>Source of ground truth annotations; qualifications and preparation of annotators</td></tr><tr><td></td><td>17</td><td>Annotation tools</td></tr><tr><td></td><td>18</td><td>Measurement of inter- and intrarater variability; methods to mitigate variability and/or resolve discrepancies</td></tr><tr><td>Data partitions</td><td>20</td><td>How data were assigned to partitions; specify proportions</td></tr><tr><td>Model</td><td>21</td><td>Level at which partitions are disjoint (eg, image, study, patient, institution)</td></tr><tr><td>Training</td><td>22</td><td>Detailed description of model, including inputs, outputs, all intermediate layers and connections</td></tr><tr><td></td><td>25</td><td>Details of training approach, including data augmentation, hyperparameters, number of models trained</td></tr><tr><td></td><td>26</td><td>Method of selecting the final model</td></tr><tr><td></td><td>27</td><td>Ensembling techniques, if applicable</td></tr><tr><td>Evaluation</td><td>28<br>29</td><td>Metrics of model performance<br>Statistical measures of significance and uncertainty (eg, confidence intervals)</td></tr><tr><td></td><td>32</td><td>Validation or testing on external data</td></tr><tr><td><strong>Results</strong></td><td></td><td></td></tr><tr><td>Data</td><td>33<br>34</td><td>Flow of participants or cases, using a diagram to indicate inclusion and exclusion<br>Demographic and clinical characteristics of cases in each partition</td></tr><tr><td>Model performance</td><td>35</td><td>Performance metrics for optimal model(s) on all data partitions</td></tr></tbody><tfoot><tr><td colspan="3">CLAIM = Checklist for Artificial Intelligence in Medical Imaging.</td></tr></tfoot></table>

classes (csPCa vs non-csPCa) or multiple classes, such as the International Society of Urological Pathology score [25] or the Prostate Imaging Reporting and Data System (PI-RADS) score. These algorithms usually do not provide voxel-level predictions but rather predictions on entire images or regions of interest.

### 3. Segmentation: Precisely outlining the boundaries of identified lesions. Methods can be similar to those used for classification, but the change of scale (from lesion-wise to voxel-wise) distinguishes them.

Al solutions may combine different tasks, such as detection and segmentation, or detection and classification, to provide a fully automated workflow.

## 3. Results

### 3.1. Study selection and general characteristics

Figure 1 presents the PRISMA flow chart illustrating the study selection process. A total of 665 studies were screened initially by reviewing titles and abstracts, with

86 studies proceeding to a full-text review. Ultimately, 25 studies met the inclusion criteria and were included in the review.

Table 2 shows the geographic distribution of the studies, specific research questions, and study types. All included studies had a retrospective design; one was a retrospective analysis of a prospective cohort [26].

### 3.2. Study objectives and AI model focus

The included studies focused on developing Al solutions to enhance the detection and classification of prostate cancer at the pixel, lesion, image, or patient level. The most frequent study goal was the validation of original neural network architectures [27-31] or the use of attention mechanisms and advanced layers [32-34]. Other research questions included the impact of the training dataset size [35,36], the incorporation of domain knowledge [30,32,37], or the use of transfer learning [38]. One study examined the level of supervision in the training data [35]. Six studies focused on determining the optimal MRI sequences to input into the neural network and how to integrate multimodality data [28,29,34,39-41]. Three studies were designed as reader studies, comparing the performance of Al solutions with that of radiologists [42-44]. Lastly, three studies aimed to validate the generalizability of Al solutions across different institutions [36,45,46].

### 3.3. Risk of bias assessment

Risk of bias in clinical studies is evaluated to ensure the reliability and validity of the findings. In this review, we utilized the QUADAS-2 tool, a widely accepted method for assessing the quality of diagnostic accuracy studies. QUADAS-2 evaluates four key domains: patient selection, index test, reference standard, and timing. Each study is examined for potential biases that might affect the credibility of its results. This section, Table 3, and Figure 2 detail the findings of the QUADAS-2 evaluation for all included studies.

#### 3.3.1. Patient selection

Twenty-two studies (88%) exhibit a low risk of bias in patient selection, indicating clear and reproducible eligibility criteria. However, three studies [34,39,46] provided insufficient descriptions of their datasets, making it difficult to fully understand the included populations (eg, screening vs diagnostic, and consecutive vs selected patients).

Applicability concerns are high in two studies that included populations composed exclusively of csPCa patients undergoing prostatectomy [43,44] or one study including low-risk prostate cancer patients on active surveillance [26]. Applicability is uncertain when demographic details are absent or scarce [34,46].

#### 3.3.2. Index test

The index test refers to the Al model that processes prostate MR images. In most studies, no prespecified model cutoff value was used to calculate sensitivity and specificity, leading to a risk of overestimation of model performance [26,31,33-36,38-40,43-45,47,48].
<figure><img src="image_2.png" /><figcaption>Fig. 1 - PRISMA 2020 flow diagram. MRI = magnetic resonance imaging; PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-analyses.ª
Multiple references reporting on the same study or trial were merged together.</figcaption></figure>

### 3.3.3. Reference standard

The reference standard is the pathology used to confirm csPCa. Given the inclusion criteria for our review, 23/25 studies have a low risk of bias, indicating the use of pathology to confirm or exclude prostate cancer (targeted and systematic biopsies). However, in two studies, a subcohort relied solely on MRI interpretation to classify lesions as csPCa [32,36].

Two studies either lacked a clear definition of csPCa [43] or used a nonstandard definition due to the chosen methodology (eg, lesion containing at least 1% of pixels labeled as aggressive by an automatic labeling platform) [30].

### 3.3.4. Flow and timing

All studies demonstrated a low risk of bias in flow and timing, with pathology typically obtained within 6 mo following the prostate MRI.
<table><caption>Table 2 – Included studies: aims and study types</caption><thead><tr><th>Study</th><th>Reference</th><th>Country</th><th>Specific research questions</th><th>Study type</th></tr></thead><tbody><tr><td>Weißer (2024)</td><td>[35]</td><td>Germany</td><td>Level of supervision in training data, Size of training dataset</td><td>Retrospective</td></tr><tr><td>Netzer (2023)</td><td>[45]</td><td>Germany</td><td>Cross-institutional validation</td><td>Retrospective</td></tr><tr><td>Santhirasekaram (2021)</td><td>[27]</td><td>UK</td><td>Type of neural network architecture</td><td>Retrospective</td></tr><tr><td>Wang (2020)</td><td>[49]</td><td>China</td><td>Choice of MRI sequences</td><td>Retrospective</td></tr><tr><td>Sobecki (2021)</td><td>[37]</td><td>Poland</td><td>Incorporation of domain knowledge</td><td>Retrospective</td></tr><tr><td>Seetharaman (2021)</td><td>[51]</td><td>USA</td><td></td><td>Retrospective</td></tr><tr><td>Saha (2021)</td><td>[32]</td><td>The Netherlands</td><td>Use of attention mechanisms and advanced layers, incorporation of domain knowledge</td><td>Retrospective</td></tr><tr><td>Gutiérrez (2022)</td><td>[28]</td><td>Colombia</td><td>Choice of MRI sequences, type of neural network architecture, multimodality integration</td><td>Retrospective</td></tr><tr><td>Shao (2022)</td><td>[33]</td><td>China</td><td>Use of attention mechanisms and advanced layers</td><td>Retrospective</td></tr><tr><td>Zhao (2023)</td><td>[48]</td><td>China</td><td></td><td>Retrospective</td></tr><tr><td>Hu (2023)</td><td>[34]</td><td>China</td><td>Use of attention mechanisms and advanced layers, training strategy</td><td>Retrospective</td></tr><tr><td>Rajagopal (2023)</td><td>[46]</td><td>USA</td><td>Federated learning models</td><td>Retrospective</td></tr><tr><td>Bashkanov (2023)</td><td>[39]</td><td>Germany</td><td>Multimodality integration, advanced data augmentation techniques, training strategy</td><td>Retrospective</td></tr><tr><td>Karagoz (2023)</td><td>[38]</td><td>Turkey</td><td>Transfer learning feasibility</td><td>Retrospective</td></tr><tr><td>Kim (2023)</td><td>[40]</td><td>USA</td><td>Choice of MRI sequences</td><td>Retrospective</td></tr><tr><td>Sun (2023)</td><td>[41]</td><td>China</td><td>Choice of MRI sequences</td><td>Retrospective</td></tr><tr><td>Hu (2023)</td><td>[50]</td><td>China</td><td>Choice of MRI sequences</td><td>Retrospective</td></tr><tr><td>Jiang (2023)</td><td>[42]</td><td>China</td><td></td><td>Retrospective</td></tr><tr><td>Zhu (2022)</td><td>[29]</td><td>China</td><td>Type of neural network architecture, choice of MRI sequences</td><td>Retrospective</td></tr><tr><td>Li (2022)</td><td>[43]</td><td>China</td><td></td><td>Retrospective</td></tr><tr><td>Hosseinzadeh (2022)</td><td>[36]</td><td>The Netherlands</td><td>Size of training dataset, incorporation of domain knowledge, cross-institutional validation</td><td>Retrospective</td></tr><tr><td>Bhattacharya (2022)</td><td>[30]</td><td>USA</td><td>Type of neural network architecture, incorporation of domain knowledge</td><td>Retrospective</td></tr><tr><td>Cao (2021)</td><td>[44]</td><td>USA</td><td></td><td>Retrospective</td></tr><tr><td>Arif (2020)</td><td>[26]</td><td>The Netherlands</td><td></td><td>Retrospective analysis of a prospective cohort</td></tr><tr><td>Lapa (2020)</td><td>[31]</td><td>Portugal, UK</td><td>Type of neural network architecture</td><td>Retrospective</td></tr></tbody></table>

MRI = magnetic resonance imaging.

### 3.4. Quality assessment via CLAIM guidelines

The CLAIM guidelines provide a structured approach to assess the completeness and transparency of reporting in studies involving AI models. This evaluation aims to ensure that the critical aspects of the AI system, such as its development, validation, and performance, are reported thoroughly and transparently. In this review, we assessed the included studies using an abbreviated CLAIM checklist, focusing on key items such as study design, model description, data sources, and reporting of metrics. This section, Table 4, and Figure 3 summarize the CLAIM evaluation results, and highlight the overall quality and completeness of reporting in the reviewed studies.

The mean CLAIM score across all studies is 68% with a standard deviation of 14.7%, ranging from a minimum of 39% to a maximum of 91%. Certain items are present universally in all studies, including study design, study goal, data sources, and description of metrics.

More than 80% of the studies include a detailed description of the model (84%) and training approach (80%), definition of the ground truth reference standard (88%), details on data partitioning (84%), and performance on these partitions (88%), as well as information about the sources of funding (92%).

Information about the study population is reported less consistently: only 68% of the studies detail eligibility criteria, and 56% provide demographic and clinical characteristics. A flowchart of the participants is present in only 44% of the studies.

While the ground truth reference standard is described in 88% of the studies, the source of ground truth annotations and the qualification and preparation of annotators are reported only in 68% of the studies. Annotation tools are mentioned in only 48% of the studies. Inter- and intrarater variability are considered in only 32% of the studies.

Validation on an external cohort is performed in only 32% of the studies. Ensembling techniques are used in 32% of the studies. A failure analysis is included in 36% of the studies.

### 3.5. Data: databases, labeling, and ground truths

The dataset sources and labeling strategies varied across the included studies. A detailed summary of these characteristics can be found in Table 5, highlighting the differences in data origin, labeling methods, and ground truth standards used in each study.

#### 3.5.1. Dataset sources

Public prostate MRI databases, such as PROSTATEX [27,28,31,33,35,37,40,45,49] and PI-CAI [38], were used widely in selected studies, for both training and testing.

A majority of the studies (19 out of 25) utilized private databases, either alone or in combination with public databases. These private databases were employed for both training and testing or exclusively for testing as external datasets [33,45,50]. In these three studies, the PROSTATEX training set was used for testing purposes because its annotations are publicly available, unlike the annotations of the PROSTATEX testing set, which are not disclosed.
<table><caption>Table 3 – QUADAS-2</caption><thead><tr><th>Study</th><th>1. Patient selection: risk of bias</th><th>1. Patient selection: applicability concern</th><th>2. Index test: risk of bias</th><th>2. Index test: applicability concerns</th><th>3. Reference standard: risk of bias</th><th>3. Reference standard: applicability</th><th>4. Flow and timing</th></tr></thead><tbody><tr><td>Weißer (2024) [35]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Netzer (2023) [45]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Santhirasekaram (2021) [27]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Wang (2020) [49]</td><td>Low</td><td>Low</td><td>Unsure</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Sobecki (2021) [37]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Seetharaman (2021) [51]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Saha (2021) [32]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td></tr><tr><td>Gutiérrez (2022) [28]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Shao (2022) [33]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Zhao (2023) [48]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Hu (2023) [34]</td><td>Unsure</td><td>Unsure</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Rajagopal (2023) [46]</td><td>Unsure</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Bashkanov (2023) [39]</td><td>Unsure</td><td>Unsure</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Karagoz (2023) [38]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Kim (2023) [40]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Sun (2023) [41]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Hu (2023) [50]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Jiang (2023) [42]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Li (2022) [43]</td><td>Low</td><td>High</td><td>High</td><td>Low</td><td>Low</td><td>Unsure</td><td>Low</td></tr><tr><td>Hosseinzadeh (2022) [36]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>High</td><td>Low</td><td>Low</td></tr><tr><td>Bhattacharya (2022) [15,30]</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Unsure</td><td>Low</td></tr><tr><td>Cao (2021) [44]</td><td>Low</td><td>High</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Arif (2020) [26]</td><td>Low</td><td>High</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><td>Lapa (2020) [31]</td><td>Low</td><td>Low</td><td>High</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr></tbody><tfoot><tr><td colspan="8">QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies-2.</td></tr></tfoot></table>

Private databases were unicentric in ten studies and multicentric in nine studies (up to nine centers). Sizes of training dataset from private databases ranged from 78 to 2170 patients (median: 637, average: 724). Testing dataset sizes ranged from 41 to 1002 patients (median: 333, average: 365).

### 3.5.2. Ground truth

Cancer cases were confirmed by pathology; clinically significant cancer was defined as Gleason grade group ≥2.

Five studies relied on both transrectal ultrasound-guided systematic and targeted biopsies as the ground truth [29,35,42,45,50]. Four studies used transperitoneal MRI

<figure><img src="image_3.png" /><figcaption>Fig. 2 – QUADAS-2 evaluation. QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies-2.</figcaption></figure>
<table><caption>Table 4 - CLAIM scores</caption><thead><tr><th>Study</th><th>Study design goal</th><th>Study data sources</th><th>Study goal</th><th>Study data</th><th>Study eligibility criteria</th><th>How missing data were handled</th><th>Definition of ground truth data</th><th>Source of ground truth tools annotations: qualifications</th><th>Amputation</th><th>Measurement of inter- reader variability</th><th>How data level at which assigned partitions to are disjoint</th><th>Details of description of model</th><th>Details of training approach</th><th>Method of selecting the final model</th><th>Ensembling techniques</th><th>Metrics of model performances are well defined</th><th>Statistical measures of significance</th><th>Validation or testing on external data</th><th>Flowchart of participants/cases</th><th>Demographic metrics and clinical characteristics</th><th>Performance all data partitions</th><th>Performance on metrics</th><th>Failure analysis of incorrectly classified cases</th><th>Sources of funding, role of funders</th><th>Score (%)</th></tr></thead><tbody><tr><td>Weißer (2024)</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>83</td></tr><tr><td>[35]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Netzer (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>74</td></tr><tr><td>[45]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Santhirakaran (2021)</td><td></td><td>1</td><td>1</td><td>1</td><td>0*</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>48</td></tr><tr><td>Wang (2020)</td><td></td><td>1</td><td>1</td><td>1</td><td>0*</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>57</td></tr><tr><td>Sobecki (2021)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[49]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[37]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Seetharaman (2021)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>52</td></tr><tr><td>[38]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Guílerre (2022)</td><td></td><td>1</td><td>1</td><td>1</td><td>0*</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>61</td></tr><tr><td>[28]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Shao (2022)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>39</td></tr><tr><td>Zhao (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>57</td></tr><tr><td>Hu (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>61</td></tr><tr><td>Rajagopal (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>65</td></tr><tr><td>[46]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bashkanov (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>61</td></tr><tr><td>[39]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kangaraz (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>70</td></tr><tr><td>Kim (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0*</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>57</td></tr><tr><td>Sim (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>87</td></tr><tr><td>Hu (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>61</td></tr><tr><td>Jiang (2023)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>87</td></tr><tr><td>Zhu (2022)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>78</td></tr><tr><td>Li (2022)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>74</td></tr><tr><td>Hosseinzadeh (2022)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>87</td></tr><tr><td>Bhattacharya (2022)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>65</td></tr><tr><td>[15-30]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cao (2021)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>61</td></tr><tr><td>Arif (2020)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>78</td></tr><tr><td>Lapa (2020)</td><td></td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>48</td></tr></tbody></table>

CLAIM = Checklist for Artificial Intelligence in Medical Imaging.
ª Study using the PROSTATEX dataset, without further description.
<figure><img src="image_4.png" /><figcaption>Fig. 3 – Completion rate for each CLAIM item. CLAIM = Checklist for Artificial Intelligence in Medical Imaging.</figcaption></figure>

guidance exclusively for targeted biopsies [32,36,39,48]. In six studies, only pathological information from targeted biopsies was used as the ground truth [26,30,39,43,46,51], without incorporating data from systematic biopsies.

Radical prostatectomy served as the ground truth for
cancers in eight studies, either for the entire population
[33,44] or for selected cases that were not specified as a dis-
tinct subcohort [30,38,41,43,48,51]. Lastly, some studies
used different ground truths for different subcohorts. For
instance, these studies used imaging ground truth (PI-
RADS ≥4) for the training cohort and biopsies for the testing
cohort [32,36], or a similar combination of radical prostate-
cotomy and biopsies [30].

Normal cases were defined usually as normal MRI—PI-RADS ≤2 [35,38,39,45,48] or PI-RADS ≤3 [32], and systematic biopsies showing non-csPCa.

3.5.3. Lesion labeling on MRI

Lesion contouring was performed commonly by a junior
radiologist or nonmedical investigators, based on clinical
radiology reports, and supervised by an experienced urora-
diologist. In three studies, annotations were made by two or
more readers in consensus [29,36,41]. In one study, each
lesion was contoured twice by five independent radiolo-
gists, thus accounting for inter-reader variability [48]. In
two studies, lesion contouring was done prospectively, by

a radiologist and a pathologist during a multidisciplinary
meeting with available ground truth [33,50].

All these methods imply contouring lesions that are vis-
ible on MRI. Two studies from the same team utilized anno-
tations on whole-mount prostate histological slices
registered automatically to prostate MRI, eliminating the
need for manual segmentation on MRI and allowing con-
touring of lesions visible only on pathology [30,51].

In ten studies, lesion contouring was performed only on
T2W images and coregistered with other images (diffusion-
weighted imaging [DWI] and ADC) as needed. In two stud-
ies, contouring was done only on diffusion-weighted images
[41] or ADC map [29]. In three studies, contouring was con-
ducted separately on different sequences [35,42,43].

3.5.4. MRI-pathology mapping

Mapping MRI-visible lesions to their corresponding patho-
logical nature used primarily “cognitive” mapping in nearly
all studies. This involved either prospectively segmenting
lesions on MRI with the knowledge of lesion contours on
pathology slices or retrospectively attributing pathology
label on precontoured lesions using targeted biopsy results.

Using systematic biopsy results to label an MRI-
contoured lesion is less straightforward due to the difficulty
in registering untargeted biopsy samples with the target
lesion. In such cases, segmenting the prostate gland by sex-
<table><caption>Table 5 - Included studies: dataset</caption><thead><tr><th>Ref.</th><th>PROSTATEX -2</th><th>PROSTATEX</th><th>PI-CAI DB</th><th>Private centers</th><th>No. of centers</th><th>Training set</th><th>Test set</th><th>Reference test for positive cases in the private dataset</th><th>Annotation in the private dataset</th><th>Annotators (experience)</th><th>Annotated sequences</th><th>MRI-pathology mapping in the private dataset</th><th>Software for annotation</th><th>Reference test for negative cases in the private dataset</th></tr></thead><tbody><tr><td>Weißer [35]</td><td>+</td><td>NA</td><td>+</td><td>1</td><td>794 (private)<br>204 (PROSTATEX)</td><td>695 (private)<br>(PROSTATEX)</td><td>Systematic biopsy with targeted TRUS/ TP biopsy</td><td>Lesion</td><td>Investigators under supervision of a radiologist (11, 19 yr exp)</td><td>T2W and DWI</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with systematic and targeted histopathology results</td><td>Medical Imaging Interaction Toolkit</td><td>Systematic TRUS biopsy PI-RADS ≤2</td></tr><tr><td>Netzer [45]</td><td>+<sup>a</sup></td><td>NA</td><td>+</td><td>2</td><td>806 (private)<br>140 (PROSTATEX)</td><td>500 (private)<br>(PROSTATEX)</td><td>Systematic biopsy with targeted TRUS/ TP biopsy</td><td>Lesion<br>Patient</td><td>Investigators under supervision and in consensus with a board-certified radiologist (11 yr exp)</td><td>NS</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with systematic and targeted histopathology results Subcohort of 100 csPCa-positive MRI and 100 csPCa-negative MRI were annotated only at patient level (=maximum Gleason score seen in biopsy)</td><td>Medical Imaging Interaction Toolkit</td><td>Systematic TRUS biopsy PI-RADS ≤2</td></tr><tr><td>Santhirasekaram [27]</td><td>+</td><td>NA</td><td>NA</td><td>0</td><td>204 (PROSTATEX)<br>142 (PROSTATEX)</td><td>NA<br>(PROSTATEX)</td><td>NA</td><td>Lesion</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Wang [49]</td><td>+</td><td>NA</td><td>NA</td><td>0</td><td>204 (PROSTATEX)<br>142 (PROSTATEX)</td><td>NA<br>(PROSTATEX)</td><td>NA</td><td>Lesion</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Sobecki [37]</td><td>+</td><td>NA</td><td>NA</td><td>0</td><td>204 (PROSTATEX)<br>142 (PROSTATEX)</td><td>NA<br>(PROSTATEX)</td><td>NA</td><td>Lesion</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Seetharaman [51]</td><td>NA</td><td>NA</td><td>+</td><td>1</td><td>102 (private)<br>29 + 293 (private)</td><td>NA<br>(private)</td><td>Radical prostatectomy or TRUS/TP targeted biopsy only</td><td>Lesion</td><td>Automated for RP</td><td>T2W</td><td>Automated MRI-pathology registration (Gleason mapping on pathology slices) Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results</td><td>NS</td><td>Negative MRI and biopsy</td></tr><tr><td>Saha [32]</td><td>NA</td><td>NA</td><td>+</td><td>2</td><td>1584 (private)<br>486 + 296 (private)</td><td>NA<br>(private)</td><td>"Internal" dataset (RUMC): PI-RADS ≥4 only "External" testing dataset (ZGT): Systematic TRUS ± TP targeted biopsy</td><td>Lesion</td><td>Trained students (6-18 mo exp) under the supervision of an experienced radiologist (7 yr exp).</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results</td><td>NS</td><td>"Internal" dataset: PI-RADS ≤3 "External" testing dataset: Systematic TRUS</td></tr><tr><td>Gutiérrez [28]</td><td>+</td><td>NA</td><td>NA</td><td>0</td><td>204 (PROSTATEX)<br>140 (PROSTATEX)</td><td>NA<br>(PROSTATEX)</td><td>NA</td><td>Lesion</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Shao [33]</td><td>+<sup>a</sup></td><td>NA</td><td>+</td><td>3</td><td>382 (private)<br>96 (private)<br>208 (PROSTATEX)</td><td>NA<br>(PROSTATEX)</td><td>Radical prostatectomy</td><td>Lesion</td><td>1 pathologist and 1 uroradiologist</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI</td><td>NS</td><td>Radical prostatectomy</td></tr></tbody></table>

Please cite this article as: Sébastien Molière, D. Hamzaoui, G. Ploussard et al., A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging, Eur Urol Oncol (2024). https://doi.org/10.1016/j.euo.2024.11.001
<table><caption>Table 5 (continued)</caption><thead><tr><th>Ref.</th><th>PROSTATEX -2</th><th>PROSTATEX PI-CAI DB</th><th>Private CAI DB</th><th>No. of centers</th><th>Training set</th><th>Test set</th><th>Reference test for positive cases in the private dataset</th><th>Annotation in the private dataset</th><th>Annotators (experience)</th><th>Annotated sequences</th><th>MRI-pathology mapping in the private dataset</th><th>Software for annotation</th><th>Reference test for negative cases in the private dataset</th></tr></thead><tbody><tr><td>Zhao [48]</td><td>NA</td><td>NA +</td><td>7</td><td>1094 (private)</td><td>97 + 248 + 300 (private)</td><td>Systematic TRUS biopsy with targeted TP biopsy Or Radical prostatectomy</td><td>Lesion</td><td>(exp NS)</td><td>T2W</td><td>with RP pathology slices</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with RP pathology slices Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results</td><td>ITK-SNAP</td><td>Systematic TRUS biopsy PI-RADS ≤2</td></tr><tr><td>Hu [34]</td><td>NA</td><td>NA +</td><td>2</td><td>307 (private)</td><td>75 (private) 112 (PROSTATEX v2)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Rajagopal [46]</td><td>NA</td><td>NA +</td><td>2</td><td>1416 (private)</td><td>346 (private)</td><td>Different among centers: UCLA: targeted TRUS biopsy only UCSF: systematic biopsy with targeted TRUS biopsy</td><td>Lesion</td><td>Board-certified radiologist</td><td>T2W</td><td>UCSF: cognitive fusion of targeted biopsy to a bounding box centered by MRI lesion Cognitive fusion of systematic biopsies to a prostate sextant UCLA: No mapping of targeted biopsy (the highest ISUP grade group is not linked to MRI lesion)</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Bashkanov [39]</td><td>NA</td><td>NA +</td><td>1</td><td>717 (private)</td><td>179 (private)</td><td>TP-transperineal biopsy; with targeted biopsy</td><td>Lesion</td><td>1 radiologist reviewed by expert radiologists (12-25 yr exp)</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results</td><td>Internal developed plugin for the 3D Slicer software</td><td>Gleason ≤6 at systematic biopsy or PI-RADS ≤2 or normal PSA level</td><td></td></tr><tr><td>Karagoz [38]</td><td>NA</td><td>NA +</td><td>9</td><td>1500 (PI-CAI) 200 (private fine-tuning data)</td><td>1000 (PI-CAI) 1002 (private)</td><td>Systematic + targeted TRUS biopsy Radical prostatectomy</td><td>Lesion</td><td>1 radiologist (3 yr exp), supervised by senior radiologist (>20 yr exp)</td><td>NS</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results Cognitive fusion of manual 3D lesion annotations on MRI with RP digitized pathology slices</td><td>Dedicated browser-based platform (https://matrix.md.ai)</td><td>Benign pathology result or MRI-negative patients (PI-RADS ≤2), no clinical/laboratory evidence of PCa</td><td></td></tr><tr><td>Kim [40]</td><td>NA</td><td>NA +</td><td>0</td><td>122 patients</td><td>140 patients</td><td>NA</td><td>Lesion</td><td>NA</td><td>NA</td><td>NA</td><td>NS</td><td>NS</td><td>Systematic biopsy; radiologist interpretation with PI-RADS inferior to a predefined threshold; other: PROSTATEX</td></tr></tbody></table>

(continued on next page)

Please cite this article as: Sébastien Molière, D. Hamzaoui, G. Ploussard et al., A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging, Eur Urol Oncol (2024), https://doi.org/10.1016/j.euo.2024.11001
<table><caption>Table 5 (continued)</caption><thead><tr><th>Ref.</th><th>PROSTATEX -2</th><th>PROSTATEX PI-CAI DB</th><th>Private centers</th><th>No. of centers</th><th>Training set</th><th>Test set</th><th>Reference test for positive cases in the private dataset</th><th>Annotation in the private dataset</th><th>Annotators (experience)</th><th>Annotated sequences</th><th>MRI-pathology mapping in the private dataset</th><th>Software for annotation</th><th>Reference test for negative cases in the private dataset</th></tr></thead><tbody><tr><td>Sun [41]</td><td>NA</td><td>NA +</td><td>1</td><td>1142 (private)</td><td>143 (internal test set); 200 (holdout dataset)</td><td>Systematic + targeted TRUS biopsy Radical prostatectomy</td><td>Lesion Sextant Anatomic zone</td><td>2 uroradiologists (4 and 30 yr exp) in consensus</td><td>DWI</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with pathology</td><td>ITK-SNAP</td><td>NS</td></tr><tr><td>Hu [50]</td><td>NA</td><td>NA +</td><td>1</td><td>2170 (private)</td><td>354 (private)</td><td>Systematic + targeted TRUS biopsy</td><td>Sextant</td><td>1 uroradiologist (>15 yr exp) and 1 GU pathologist</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results Cognitive fusion of systematic biopsies to a prostate sextant Calculation of the maximum GS between systematic and targeted biopsy to create a sextant map</td><td>NS</td><td>NS</td></tr><tr><td>Jiang [42]</td><td>NA</td><td>NA +</td><td>2</td><td>615 (private)</td><td>10 + 169</td><td>Systematic + targeted TRUS biopsy</td><td>Lesion</td><td>2 fellowship-trained radiologists (5 and 3 yr exp) rechecked by 2 subspecialists (10 and 15 yr exp)</td><td>T2W, DWI, and ADC</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with pathology</td><td>In-house software (Oncology Imaging Analysis version 2; Shanghai Key Laboratory of MRI, ECNU, Shanghai)</td><td>NS</td></tr><tr><td>Zhu [29]</td><td>NA</td><td>NA +</td><td>1</td><td>180 (private)</td><td>140 (private)</td><td>Systematic + targeted TRUS biopsy</td><td>Lesion Sextant</td><td>2 uroradiologists (7 and 13 yr exp) in consensus</td><td>ADC</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with pathology</td><td>ITK-SNAP</td><td>NS</td></tr><tr><td>Li [43]</td><td>NA</td><td>NA +</td><td>1</td><td>659 (private)</td><td>200 (private)</td><td>Targeted TRUS biopsy and RP for cancers</td><td>Lesion</td><td>2 radiologists (5 and 10 yr exp) under the supervision of a radiologist (15 yr exp)</td><td>T2W, DWI, and ADC</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with whole-mount RP pathology slices</td><td>ITK-SNAP</td><td>NS</td></tr><tr><td>Hosseinzadeh [36]</td><td>NA</td><td>NA +</td><td>2</td><td>From 50 to 1586</td><td>486 (center 1) + 296 (center 2)</td><td>Center 1: PI-RADS v2 Center 2: systematic TRUS biopsy with targeted TP biopsy</td><td>Lesion</td><td>2 investigators in consensus under the supervision of a radiologist (7 yr exp)</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with pathology</td><td>PI-RADS ≤3</td><td>NS</td></tr></tbody></table>

Please cite this article as: Sébastien Molière, D. Hamzaoui, G. Ploussard et al., A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging, Eur Urol Oncol (2024). https://doi.org/10.1016/j.euo.2024.11.001
<table><caption>Table 5 (continued)</caption><thead><tr><th>Ref.</th><th>PROSTATEX -2</th><th>PROSTATEX PI-CAI DB</th><th>Private centers</th><th>No. of centers</th><th>Training set</th><th>Test set</th><th>Reference test for positive cases in the private dataset</th><th>Annotation in the private dataset</th><th>Annotators (experience)</th><th>Annotated sequences</th><th>MRI-pathology mapping in the private dataset</th><th>Software for annotation</th><th>Reference test for negative cases in the private dataset</th></tr></thead><tbody><tr><td>Bhattacharya [30]</td><td>NA</td><td>NA +</td><td>1</td><td>98 (private)</td><td>55 + 15 + 275 (private)</td><td></td><td>Cohort 1: RP (whole-mount sectioned in the same place as T2W MRI using 3D-printed molds) Cohort 2: targeted TRUS biopsy Cohort 3: normal</td><td>Lesion Sextant</td><td>Automatic for RP</td><td></td><td>Automated MRI-pathology registration (Gleason mapping on pathology slices) Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results</td><td></td><td>Cohort 3: negative MRI and negative biopsy</td></tr><tr><td>Cao [44]</td><td>NA</td><td>NA +</td><td>1</td><td>427 (private)</td><td>126 (private)</td><td></td><td>Whole-mount radical prostatectomy</td><td>Lesion</td><td>Uroradiology research fellows led by uroradiologists (3-19 yr exp)</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with whole-mount RP pathology slices (MRI nonvisible lesions were not included or contoured)</td><td>Osirix MD 10.0</td><td>No negative cases</td></tr><tr><td>Arif [26]</td><td>NA</td><td>NA +</td><td>1</td><td>78 (private)</td><td>155 (private)</td><td></td><td>Group 1: PI-RADS ≥3 and targeted TRUS biopsy positive Group 2: negative MRI and negative systematic biopsy</td><td>Lesion</td><td>NS</td><td>T2W</td><td>Cognitive fusion of manual 3D lesion annotations on MRI with targeted histopathology results</td><td>?</td><td>Group 2: negative MRI and negative systematic biopsy</td></tr><tr><td>Lapa [31]</td><td>+</td><td>NA</td><td>0</td><td>122 (private)</td><td>41 (private)</td><td></td><td>NA</td><td>Lesion</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table>

ADC = apparent diffusion coefficient; csPCa = clinically significant prostate cancer; 3D = three dimensional; DB = database; DWI = diffusion-weighted imaging; exp = experience; GS = Gleason score; GU = genitourinary; ISUP = International Society of Urological Pathology; MRI = magnetic resonance imaging; NA = nonapplicable; NS = nonspecified; PCa = prostate cancer; PI-RADS = Prostate Imaging Reporting and Data System; PSA = prostate-specific antigen; RP = radical prostatectomy; TP = transperitoneal; TRUS = transrectal ultrasound; T2W = T2 weighted; UCLA = University of California Los Angeles; UCSF = University of California San Francisco.

Please cite this article as: Sébastien Molière, D. Hamzaoui, G. Ploussard et al., A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging, Eur Urol Oncol (2024), https://doi.org/10.1016/j.euo.2024.11.001

<footnote>a Only the training set of PROSTATEX was used in these studies.</footnote>
<table><caption>Table 6 – Model performance in terms of AUROC values</caption><thead><tr><th>Study</th><th>Patient-level AUROC value</th><th>Lesion-level AUROC value</th></tr></thead><tbody><tr><td>Weißer (2024) [35]</td><td>0.83 (CI95 0.8–0.86; model trained with slice level annotation), 0.78 (CI95 0.74–0.81; model trained with patient level annotation)</td><td></td></tr><tr><td>Netzer (2023) [45]</td><td>0.80 (external institution), 0.87 (internal cohort), 0.82 (PROSTATEX test set)</td><td></td></tr><tr><td>Santhirasekaram (2021) [27]</td><td>0.94 (PROSTATEX test set)</td><td>0.892 (in-house test set)</td></tr><tr><td>Wang (2020) [49]</td><td>0.95 (PROSTATEX test set)</td><td>0.82 (model 1), 0.84 (model 2, with PI-RADS domain knowledge encoding)</td></tr><tr><td>Sobecki (2021) [37]</td><td></td><td>0.76 (prostatectomy GT), 0.75 (biopsy GT)</td></tr><tr><td>Seetharaman (2021) [51]</td><td></td><td></td></tr><tr><td>Saha (2021) [32]</td><td>0.882 ± 0.03 (PI-RADS GT), 0.862 ± 0.04 (histo GT)</td><td></td></tr><tr><td>Gutiérrez (2022) [28]</td><td></td><td>0.82</td></tr><tr><td>Shao (2022) [33]</td><td>0.801 ± 0.018</td><td></td></tr><tr><td>Zhao (2023) [48]</td><td></td><td>0.848 (CI95 0.773–0.923; SKH validation cohort), 0.885 (CI95 0.844–0.925; TZH validation cohort), 0.852 (CI95 0.804–0.900; CSH + SQH validation cohort)</td></tr><tr><td>Bashkanov (2023) [39]</td><td>0.878 (CI95 0.8–0.942)</td><td></td></tr><tr><td>Karagoz (2023) [38]</td><td>0.888 (PI-CAI test set), 0.886 (in-house test set), 0.870 (in-house test set with fine-tuning)</td><td></td></tr><tr><td>Kim (2023) [40]</td><td>0.9 (mpMRI model, held-out PROSTATEX training set), 0.84 (mpMRI model, PROSTATEX test set)</td><td></td></tr><tr><td>Sun (2023) [41]</td><td>0.865 (diffusion model), 0.875 (BP model)</td><td></td></tr><tr><td>Hu (2023) [34,50]</td><td>0.88 (CI95 0.84–0.91)</td><td>0.855 (CI95 0.825–0.833)</td></tr><tr><td>Jiang (2023) [42]</td><td></td><td>0.86 (CI95 0.81–0.91)</td></tr><tr><td>Zhu (2022) [29]</td><td></td><td></td></tr><tr><td>Li (2022) [43]</td><td>0.797</td><td></td></tr><tr><td>Hosseinzadeh (2022) [36]</td><td>0.875 (CI95 0.838–0.91)</td><td></td></tr><tr><td>Bhattacharya (2022) [15,30]</td><td></td><td>0.82 ± 0.31 (prostatectomy GT), 0.86 (SD 0.26; biopsy GT)</td></tr><tr><td>Arif (2020) [26]</td><td></td><td>0.65</td></tr><tr><td>Lapa (2020) [31]</td><td></td><td>0.573</td></tr></tbody></table>

AUROC = area under the receiver operating characteristic curve; BP = biparametric; CI95 = 95% confidence interval; CSH + SQH = Changshu People's Hospital + People's Hospital of Suqian; GT = ground truth; histo = histological; mpMRI = multiparametric magnetic resonance imaging; PI-RADS = Prostate Imaging Reporting and Data System; SD = standard deviation; SKH = Suzhou Kowloon Hospital; TZH = People's Hospital of Taizhou.

tant, rather than by sector, was often proposed to facilitate MRI-pathology mapping [29,30,41,50].

### 3.6. AI models: tasks, architectures, and inputs

#### 3.6.1. Task

Eleven studies (44%) [27,49,28,48,50,46,39,40,43,30,31] focused on the classification task at the lesion level and thee (12%) at the patient level [29,33,43]. Sixteen studies (64%) used segmentation methods [35,26,45,51,32,33,46,39,38,41,50,42,43,36,30,44], and six of these (24%) were combining segmentation and detection – by first detecting the lesion and then by segmenting within the corresponding area of interest.

#### 3.6.2. Architecture

The task drives the choice of the network used to find csPCa. For classification, the method used is often one of the networks classically used for this task, even outside of the medical field. Here, for example, we can find VGGNet [37,52], DenseNet [50,53], Xmas-Net [31,54], Inception networks [28,55,56] or EfficientNet [35]. For the semantic segmentation task, encoder-decoder architectures have been the most used methods thanks to their good performance with few training data. The basis encoder method is U-Net [26,57] and its variations, such as V-Net [43,58] and nnUnet

[39]. Those networks can also be cascaded, with the first one segmenting the whole gland or the prostate zones, and the second one using this information to segment the lesions [29].

It should also be noted that, whereas all models here are based on convolutional neural networks, one of these [27] also takes advantage of the recently developed transformer architecture.

Among the 25 studied networks, 14 (56%) are three-dimensional (3D) networks [32,33,35,38–43,45,46,48,50,51], six (24%) are 2D [26,27,36,37,47,49], and five (20%) are 2.5D [29–31,44,50]. The 3D models allegedly have better performance, as these are able to consider the prostate as a whole, but these models need more parameters and resources than 2D networks. The 2.5D model, which is a 2D model with consecutive slices given as an input and different feature maps, is used as a compromise between the two [59].

#### 3.6.3. Input sequence

All models used the T2W sequence as an input to the network. Nineteen (76%) of these models used biparametric MRI as an input (15 [60%] using a combination of T2W, ADC, and DWI [26,29,32,35,36,38,39,41–43,45,46,48,50] and four (16%) using only T2W and ADC [31,33,44,51]),
and the six others (24%) used mpMRI—adding dynamic contrast-enhanced imaging to the other sequences [27,28,30,37,40,49]. In addition, three networks (12%) used zonal segmentations as an input [27,30,51], and two others (8%) used other information (lesion location information [37] and histopathology whole slice [30]).

### 3.6.4. Output

Among the segmentation networks, 12 (48%) output a voxel probability map [29,30,32,33,36,38,39,41,43,48,50] and seven (28%) output a class map [27,28,31,37,40,46,49]. Regarding the classification networks, eight (32%) predict a class for each candidate lesion [27,28,31,37,40,48–50], whereas four networks (16%) predict a class for the whole patient [29,33,43,50]. Finally, two (8%) regress a score for the lesion [48,50].

## 3.7. Performance metrics

Table 6 summarizes the performances of the included studies.

### 3.7.1. Patient-level metrics and performances

The most common metric for evaluating patient-level performance was the AUROC of the receiver operating characteristic (ROC) curve. Additionally, studies employed sensitivity (true positive rate), specificity (true negative rate), positive predictive value, and negative predictive value, all of which must be calculated at a defined threshold. However, only nine studies used a predetermined threshold to calculate these metrics [27,30,32,37,41,42,46,50,51], while others typically extracted the best operating point from the ROC curve.

Although comparing AUROCs measured on different datasets is not ideal, several studies also reported AUROCs on the PROSTATEX testing dataset. On this public dataset, the patient-level AUROC ranged from 0.82 [45] to 0.95 [49].

### 3.7.2. Lesion-level metrics and performances

Similar to patient-level metrics, evaluation of lesion-level performances relied on sensitivity (or recall), specificity, positive predictive value, negative predictive value, and AUROC, but also precision/recall curve (precision being the proportion of true positive results in all positive predictions) and average precision that is derived from this curve. For lesion segmentation task, Dice coefficient was also used to measure the overlap between the predicted lesion areas and the ground truth.

Lesion-level AUROC was generally lower and more variable than patient-level AUROC, ranging from 0.573 [31] to 0.892 [49]. Two studies reported similar AUROC values in their testing cohorts based on both prostatectomy and biopsy ground truth [30,51].

### 3.7.3. Prostate zone-level performances

Prostate cancers located in the transition zone (TZ) and peripheral zone (PZ) exhibit different imaging characteristics, in particular different PI-RADS scoring criteria. Models trained on datasets with an imbalanced distribution of TZ and PZ lesions may not perform optimally across all types

of prostate cancers. Therefore, the proportions of PZ/TZ lesions in the development and testing cohorts were extracted and are summarized in Table 7. In average, PZ lesions represented 58% of the development cohorts and 57% of the testing cohorts, while TZ lesions represented 31% of lesions in the development cohorts and 34% in the testing cohorts. Conversely, proportion of TZ lesions was much higher in the study of Zhao et al [48]: 45.6% in the development cohort and 57% in average in the test cohorts.

Three studies incorporated zonal anatomy a priori knowledge in the models [32,36,37], while Wang and Wang [49] developed specific models for the PZ and TZ.

Performances of the models based on lesion localization were detailed in three studies. Gutiérrez et al [28] proposed a model using ADC and Ktrans sequences resulting in AUROCs of 0.84 in the PZ and 0.75 in the TZ. Another model including T2W sequence increased performance in the TZ up to 0.83 but decreased performance in the PZ to 0.67. Sun et al [41] proposed a diffusion model (DWI and ADC as inputs) with an AUROC of 0.926 in the PZ and 0.898 in the TZ, while the addition of T2W as an input in a biparametric model (DWI, ADC, and T2W imaging as inputs) decreased the AUROC to 0.907 in the PZ and increased the AUROC to 0.903 in the TZ. In Zhu et al's [29] study, lesion sensitivity of both ADC and biparametric models was constantly higher than that of PI-RADS for the detection of prostate cancer in the TZ and approaching the high performance of PI-RADS for the PZ.

## 3.8. Comparison with radiologist

In 11 studies (44%) [29,32,35,36,41–45,48,51], model performance was compared directly with that of radiologists. Important methodological aspects must be considered, which are summarized in Table 8.

AI/radiologist comparison mostly relies on the binary classification of the model output (csPCa or non-csPCa) and a binarized radiological outcome based on PI-RADS assessment or Likert score. The thresholds used to classify a lesion as csPCa varied: PI-RADS ≥3 [29,41,42], Likert score ≥3 [44], PI-RADS ≥4 [32], and multiple thresholds in other studies [35,43,45,48]. Another important methodological aspect was the version of PI-RADS used. Five studies employed PI-RADS v2, while four studies used PI-RADS v2.1. When PI-RADS assessment was conducted during clinical routine, protocols varied between institutions, ranging from reading by a single reader [45] to double reading involving two readers in consensus [32,36]. In retrospective assessments, multiple independent readers were generally involved [41,43,44,48], except for one study using a single reader [29]. Two studies did not provide information about the PI-RADS assessment methodology [35,51].

Based on these different methodological choices, the results of AI/radiologist comparison varied greatly among studies. AI sensitivity generally approaches or matches that of radiologists, with some studies showing AI outperforming junior radiologists, but at this level of sensitivity, AI specificity varies widely, often slightly lower than radiologists, indicating more false positives. Table 9 summarizes the main results of AI and radiologists in detecting csPCa.
<table><caption>Table 7 – Zonal repartition of lesions in development and testing cohorts</caption><thead><tr><th>Study</th><th>Cohort size</th><th>PZ (%)</th><th>TZ (%)</th><th>Multizone (%)</th><th>AFS (%)</th><th>CZ (%)</th><th>Other (%)</th></tr></thead><tbody><tr><td rowspan="3">Weißer (2024) [35]</td><td>Training (n = 794)</td><td>76</td><td>21</td><td>3</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Test (n = 695)</td><td>75</td><td>17</td><td>8</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PROSTATEx test set (n = 204)</td><td>88</td><td>12</td><td>0</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Wang (2020) [49]</td><td>PROSTATEx training set (n = 330)</td><td>58</td><td>25</td><td>-</td><td>17</td><td>-</td><td>-</td></tr><tr><td>PROSTATEx test set (n = 208)</td><td>54</td><td>28</td><td>-</td><td>16</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Sobecki (2021) [37]</td><td>PROSTATEx training set-non-csPCa (n = 254)</td><td>61</td><td>28</td><td>-</td><td>9</td><td>1</td><td>-</td></tr><tr><td>PROSTATEx training set-csPCa (n = 76)</td><td>47</td><td>12</td><td>-</td><td>41</td><td>0</td><td>-</td></tr><tr><td rowspan="2">Seetharaman (2021) [51]</td><td>C1 test (n = 29)</td><td>56</td><td>26</td><td>15</td><td>3</td><td>0</td><td>-</td></tr><tr><td>C2 (n = 293)</td><td>67</td><td>33</td><td>0</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="4">Zhao (2023) [48]</td><td>Development (n = 1216)</td><td>37</td><td>45.6</td><td>17.4</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Test SKH (n = 97)</td><td>35.1</td><td>64.9</td><td>0</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Test TZH (n = 248)</td><td>14.9</td><td>49.2</td><td>35.9</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Test CSH + SQH (n = 300)</td><td>25.7</td><td>62</td><td>12.3</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Sun (2023) [41]</td><td>Development (n = 1428)</td><td>26.5</td><td>24.4</td><td>48.5</td><td>0.2</td><td>0.4</td><td>-</td></tr><tr><td>Test (n = 200)</td><td>39.5</td><td>37.6</td><td>22.9</td><td>0</td><td>0</td><td>-</td></tr><tr><td rowspan="2">Hu (2023) [34,50]</td><td>Benign (n = 236)</td><td>69</td><td>31</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Malignant (n = 250)</td><td>53</td><td>47</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Jiang (2023) [42]</td><td>Training (n = 912)</td><td>62</td><td>38</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Internal test (n = 449)</td><td>62</td><td>38</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>External test (n = 244)</td><td>55</td><td>45</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Zhu (2022) [29]</td><td>Test dataset (n = 200)</td><td>61.4</td><td>29.9</td><td>7.9</td><td>0.8</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Li (2022) [43]</td><td>Training dataset (n = 659)</td><td>63</td><td>29.2</td><td>7.2</td><td>0.6</td><td>-</td><td>-</td></tr><tr><td>Validation dataset (n = 80)</td><td>71.2</td><td>16.9</td><td>11.9</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Test dataset (n = 200)</td><td>61.4</td><td>29.9</td><td>7.9</td><td>0.8</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Hosseinzadeh (2022) [36]</td><td>Center 1 (n = 2438)</td><td>79.6</td><td>29.6</td><td>22</td><td>0.8</td><td>4.6</td><td>1.5</td></tr><tr><td>Center 2 (n = 296)</td><td>66.6</td><td>31.1</td><td>-</td><td>3</td><td>8.1</td><td>1.6</td></tr><tr><td>Arif (2020) [26]</td><td>n = 292</td><td>82.9</td><td>17.1</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

AFS = anterior fibromuscular stroma; CSH = Changshu People's Hospital; csPCa = clinically significant prostate cancer; CZ = central zone; PZ = peripheral zone; SKH = Suzhou Kowloon Hospital; SQH = People's Hospital of Suqian; TZ = transitional zone; TZH = People's Hospital of Taizhou.

Potential improvement due to AI assistance was analyzed explicitly in three studies. Li et al [43] directly compared the diagnostic performance of junior and senior radiologists with and without AI assistance, demonstrating significant improvements in both sensitivity and accuracy when using AI. Seetharaman et al [51] and Zhao et al [48] developed hybrid models that combined radiologist assessments with AI predictions, showing enhanced diagnostic performance. For example, Zhao et al [48] integrated a DL model with PI-RADS, achieving higher specificity than and a superior area under the curve for detecting csPCa to PI-RADS alone, with the added benefit of reducing unnecessary biopsies.

### 3.9. Generalizability of AI models

#### 3.9.1. MRI vendor and magnetic field strength

Out of 25 studies, 17 (68%) included images from a single MRI vendor [27-32,35-37,39,40,44,45,49-51,60], with Siemens being used in 13 of these studies (Siemens Healthineers, Erlangen, Germany) [27,28,31,32,35-37,39,40,44,45,49,50]. In the remaining eight studies, most of which were multicentric, images from at least two different vendors were available in the training and testing datasets. All studies included images acquired on 3 T MRI scanners, and a small fraction (5/25) also included images acquired on 1.5 T MRI scanners [35,37,38,41,45].

#### 3.9.2. Validation on independent cohorts

Only eight studies (32%) included validation on an independent cohort: testing on a public database while the algorithm was trained from a private database (three studies) [33,42,50] or from a different institution (three studies) [32,36,48] or from a different population (screening MRI

vs preoperative MRI, one study) [51] or from the same institution but different inclusion time (one study) [43].

### 4. Discussion

This systematic review highlights the advancements and challenges in employing DL models for the automatic detection, localization, and characterization of csPCa on MRI. Our review incorporated 25 studies, revealing diverse approaches, model types, and validation strategies, which reflect the ongoing efforts to integrate AI into clinical practice for prostate cancer diagnosis.

DL models have shown promise in improving the diagnostic accuracy of prostate MRI. Several studies demonstrated that DL could achieve high AUROC values in detecting and characterizing csPCa. Notably, Wang and Wang [49] reported AUROCs of 0.965 for the TZ and 0.891 for the PZ using a zone-specific neural network. Sun et al [41] presented a diffusion model with AUROCs of 0.926 for the PZ and 0.898 for the TZ, suggesting the potential of sequence-specific models to enhance diagnostic accuracy.

In this review, the timeframe of inclusion (2020-2023) ensures that only recent advancements in DL models are captured, reflecting the introduction of more sophisticated AI techniques, such as transformer-based models and advanced convolutional neural networks. The review focuses on modern developments utilizing large public datasets such as PROSTATEx and PI-CAI, which are pivotal for training and validating DL algorithms. Previous reviews, such as those by Castillo et al [18] and Sushentsev et al [21], primarily covered earlier machine learning approaches, making this review a timely update on the latest developments.
<table><caption>Table 8 – Methodology of AI/radiologist comparison</caption><thead><tr><th>Study</th><th>Radiologist assessment used for comparison</th><th>Number and qualification of radiologists realizing this assessment</th><th>Cutoff used for this assessment</th></tr></thead><tbody><tr><td>Weißer (2024) [35]</td><td>PI-RADS v2</td><td>NS</td><td>PI-RADS ≥3 and PI-RADS ≥4</td></tr><tr><td>Netzer (2023) [45]</td><td>PI-RADS v2</td><td>Single reader, during clinical routine</td><td>PI-RADS ≥3 and PI-RADS ≥4</td></tr><tr><td>Seetharaman (2021) [51]</td><td>Radiologist delineation of lesions</td><td>NS</td><td>NA</td></tr><tr><td>Saha (2021) [32]</td><td>PI-RADS v2</td><td>1 of 2 first readers (6, 24 yr experience) + independent review by 2 expert radiologists (5, 25 yr exp) in consensus, during clinical routine</td><td>PI-RADS ≥4</td></tr><tr><td>Zhao (2023) [48]</td><td>PI-RADS v2.1</td><td>1 of 5 first readers (3–5 yr experience) + independent review by 2 expert radiologists (5, 25 yr exp), retrospective evaluation</td><td>PI-RADS ≥3 and PI-RADS ≥4</td></tr><tr><td>Sun (2023) [41]</td><td>PI-RADS v2.1</td><td>2 independent uroradiologists (10, 12 yr exp) + senior radiologist (30 yr exp) in case of disagreement, retrospective evaluation</td><td>PI-RADS ≥3</td></tr><tr><td>Jiang (2023) [42]</td><td>PI-RADS v2</td><td>1 of 2 subspecialists (10, 15 yr exp), retrospective evaluation</td><td>PI-RADS ≥3</td></tr><tr><td>Zhu (2022) [29]</td><td>PI-RADS v2.1</td><td>Single uroradiologist (10 yr exp), retrospective evaluation</td><td>PI-RADS ≥3</td></tr><tr><td>Li (2022) [43]</td><td>PI-RADS v2.1</td><td>1 junior (5 yr exp) and 1 senior (10 yr exp) uroradiologist, retrospective evaluation; all PI-RADS = 3 were evaluated by a third uroradiologist (20 yr exp), retrospective evaluation</td><td>PI-RADS ≥4 and PI-RADS ≥3</td></tr><tr><td>Hosseinzadeh (2022) [36]</td><td>PI-RADS v2</td><td>1 of 2 first readers (6, 24 yr experience) + independent review by 2 expert radiologists (5, 25 yr exp) in consensus, during clinical routine</td><td>PI-RADS ≥4</td></tr><tr><td>Cao (2021) [44]</td><td>Likert score</td><td>Four board-certified uroradiologists, blinded to clinical information, retrospective evaluation</td><td>Likert score ≥3</td></tr></tbody></table>

AI = artificial intelligence; exp = experience; NS = nonspecified; PI-RADS = Prostate Imaging Reporting and Data System.

DL enables end-to-end computer-aided detection systems, which can function independently of radiologist input. In contrast, feature-based radiomic approaches typically require manual region-of-interest delineation, making these dependent on the radiologist's expertise and ability to detect suspicious lesions. Additionally, studies have demonstrated that radiomic features, such as texture or enhancement parameters, can exhibit significant variability not only due to manual segmentations, but also across different scanners, imaging protocols, and quantification software, which can potentially limit their generalizability (for a review, see the study by Cho et al [61]). However, it is worth noting that selecting simpler, quantitative parameters may yield more robust results, as shown in the study by Couchoux et al [62], which used the second ADC percentile and the normalized wash-in rate for lesions in the PZ, along with the 25th ADC percentile for lesions in the TZ. This approach achieved results comparable with expert radiologists and demonstrated robust multicenter validation.

Despite promising results of DL for the detection and characterization of csPCa on prostate MRI, our study highlights several remaining challenges.

## 4.1. Heterogeneity in study design and validation

The included studies exhibited significant heterogeneity in terms of datasets, validation strategies, and performance metrics. This variability complicates direct comparisons and meta-analyses. Validation cohorts should encompass a diverse range of clinical scenarios, therefore necessitating

a sufficient number of cases. In this review, we set a threshold of 140 patients for the test dataset size, to ensure sufficient statistical power and reliability in evaluating the performance of AI models. This benchmark aligns with the size of well-established public datasets such as PROSTATEX, which includes a test set of similar magnitude, allowing for meaningful validation and generalization of results across diverse clinical settings.

Many studies relied on internal validation cohorts, which may not fully capture the model's performance across various clinical settings. External validation on independent cohorts remains limited.

## 4.2. Data quality and ground truth

All the included studies relied on retrospective data, which inherently carries biases. For this review, we chose to include only studies with pathological confirmation of prostate cancers, rather than those relying on a radiological ground truth such as PI-RADS. This decision is grounded in the fundamental goal of prostate MRI: to detect csPCa. The reference standard for csPCa, whether targeted versus systematic biopsies or prostatectomy specimens, varied across studies, potentially affecting the reported diagnostic accuracy. Targeted biopsies, while highly specific, may miss some significant lesions that systematic biopsies could detect. Conversely, prostatectomy specimens offer a comprehensive pathology but are available only for patients undergoing surgery, representing a subset of the broader patient population.
<table><caption>Table 9 - Results of AI/radiologist comparison</caption><thead><tr><th>Study ID</th><th>Comparison level</th><th>AI sensitivity (%)</th><th>AI specificity (%)</th><th>AI AUROC</th><th>Radiologist sensitivity (%)</th><th>Radiologist specificity (%)</th><th>Radiologist AUROC</th><th>Authors' conclusion</th></tr></thead><tbody><tr><td>Weißer (2024) [35]</td><td>Patient</td><td>90 (SLA)/88 (PLA)/88 (nnUnet)</td><td>51 (SLA)/39 (PLA)/58 (nnUnet)</td><td>0.828 (SLA)/0.776 (PLA)</td><td>97 (≥3)/90 (≥4)</td><td>17 (≥3)/55 (≥4)</td><td>NS</td><td>No statistical difference between sensitivities at PI-RADS 3 or 4</td></tr><tr><td>Netzer (2023) [45]</td><td>Patient</td><td>95</td><td>43 (PROSTATEx test set)</td><td>0.82 (PROSTATEx test set)</td><td>100 (≥3)/99 (≥4)</td><td>6 (≥3)/47 (≥4)</td><td>NS</td><td>AI reaches performance comparable with radiologist assessment</td></tr><tr><td>Seetharaman (2021) [51]</td><td>Patient</td><td>57</td><td>83</td><td>NS</td><td>71</td><td>100</td><td>NS</td><td>AI approach performances of radiologist</td></tr><tr><td>Saha (2021) [32]</td><td>Patient</td><td>75.31</td><td>85.83</td><td>NS</td><td>91.11</td><td>77.18</td><td>NS</td><td>AI and radiologist perform statistically similar</td></tr><tr><td>Zhao (2023) [48]</td><td>Patient</td><td>98.6</td><td>35</td><td>0.852</td><td>100</td><td>45.3</td><td>0.863</td><td>No difference between AI and radiologist</td></tr><tr><td>Sun (2023) [41]</td><td>Patient</td><td>90</td><td>85</td><td>0.875</td><td>95</td><td>89</td><td>0.920</td><td>Comparable results between AI and radiologist at patient level</td></tr><tr><td>Jiang (2023) [42]</td><td>Patient</td><td>NS</td><td>NS</td><td>0.86</td><td>95.2 (junior)/77.4 (subspecialist)</td><td>41.2 (junior)/87.1 (subspecialist)</td><td>0.80 (junior)/0.86 (subspecialists)</td><td>AI has slightly lower performance than subspecialists, but outperform junior readers</td></tr><tr><td>Zhu (2022) [29]</td><td>Patient</td><td>98.6 (BP model)</td><td>64.8</td><td>NS</td><td>98.6</td><td>66.2</td><td>NS</td><td>No difference between AI and radiologist</td></tr><tr><td>Li (2022) [43]</td><td>Patient</td><td>71 (AI)/79 (junior with AI) / 84 (senior with AI)</td><td>69 (AI)/72 (junior with AI)</td><td>0.797</td><td>59 (junior)/69 (senior)</td><td>70 (junior)/77 (senior)</td><td>NS</td><td>Improved performance with AI assistance</td></tr><tr><td>Cao (2021) [44]</td><td>Lesion</td><td>90% (index lesion)/90% (clinically significant lesions)</td><td>NS</td><td>NS</td><td>8.4% higher than AI (Likert ≥4)</td><td>same false-positive detections per patient</td><td>NS</td><td>AI has slightly lower (not statistically significant) performance than radiologists</td></tr></tbody></table>

AI = artificial intelligence; AUROC = area under the receiver operating characteristic curve; BP = biparametric; NS = nonspecified; PI-RADS = Prostate Imaging Reporting and Data System; PLA = patient-level annotation; SLA = slice-level annotation.

### 4.3. Generalizability and robustness

Models trained on single-center or single-vendor data may not generalize well across different clinical environments. The inclusion of multicentric datasets with diverse imaging protocols is essential to improve model robustness and applicability. Studies such as those by Gutiérrez et al [28] and Hosseinzadeh et al [36], which incorporated multivendor data, showed more promising results in terms of generalizability.

### 4.4. Incorporation of domain knowledge

Integrating domain-specific knowledge, such as zonal anatomy and PI-RADS criteria, can enhance model performance. Wang and Wang [49] developed separate models for the PZ and TZ, demonstrating the benefits of this approach. Similarly, Sobecki et al [37] and Saha et al [32] utilized PI-RADS-based optimization to improve diagnostic accuracy.

### 4.5. Clinical integration and workflow

For AI models to be adopted in clinical practice, these must demonstrate not only high diagnostic accuracy, but also ease of integration into existing workflows. Prospective studies evaluating the real-world impact of AI on clinical decision-making are sparse. One study included in our review [43] has directly compared AI-assisted readings with unaided radiologist performance, indicating improvements in diagnostic accuracy. Two other studies [48,51] have shown that hybrid models incorporating radiologists' PI-RADS assessment and AI prediction performed better than PI-RADS or AI alone.

To advance the field and facilitate the clinical adoption of AI in prostate cancer diagnosis, we propose the following recommendations:

1. **Standardization of study designs and reporting:** Adoption of standardized reporting guidelines, such as CLAIM, can improve the transparency and reproducibility of AI research. In particular, clear definitions of clinical endpoints, consistent use of performance metrics, and detailed descriptions of training and validation datasets are essential.

2. **Emphasis on external validation:** Several factors hinder the translation of internally validated AI models into clinical practice. Technical limitations such as differences in image resolution, contrast, pulse sequences, or MRI hardware (manufacturer and magnetic field strength) can affect performance. Equally important are variations in clinical settings—such as prostate cancer screening, preoperative imaging, or follow-up of known lesions—each associated with differing disease prevalence. Population bias (age, ethnicity, and cancer prevalence) also impacts prostate morphology, disease incidence, and aggressiveness, contributing to variability and limiting model generalizability. Therefore, future studies must prioritize external validation with independent, multicentric (at least three to five institutions) datasets that include a wide range of MRI vendors and protocols. Ensuring an
adequate sample size (at least 100–200 cases per con-
tributing center) helps achieve sufficient statistical
power and reliability.

3. Incorporation of clinical context: AI models should be designed with a specific clinical use case in mind, whether for detection, localization, or characterization of csPCa. Integration with clinical data, such as PSA levels and patient demographics, may enhance model performance and clinical utility.

4. Exploration of multimodal approaches: Combining MRI with other imaging modalities or clinical biomarkers could improve the diagnostic accuracy and robustness of AI models.

5. **Prospective clinical trials:** Rigorous prospective trials are needed to minimize biases inherent in retrospective studies and evaluate the impact of AI on clinical outcomes, including diagnostic accuracy, biopsy decision-making, and patient management.

The recently published PI-CAI study [63], which included over 10 000 MRI examinations from multiple centers and compared AI performance with that of a large cohort of radiologists using PI-RADS, provides compelling evidence that AI systems can match or even surpass radiologists in detecting csPCa. Despite obvious limitations such as its retrospective design, the absence of a fully independent test cohort (with 80% of test MRI scans originating from the same institutions that contributed to the training cohort), and the homogeneity of MRI vendors (93.4% of scans from a single manufacturer), the results of this study strongly reinforce the conclusions of our review regarding the potential of AI in improving prostate cancer detection.

**5. Conclusions**

AI has the potential to revolutionize the diagnosis of pro-
tate cancer by improving the accuracy and consistency of
MRI interpretations. However, significant challenges
remain, particularly regarding validation, generalizability,
and clinical integration. Addressing these challenges
through standardized methodologies, robust validation,
and prospective clinical trials will be critical to realizing
the full potential of AI in prostate cancer diagnostics.

**Author contributions:** Sébastien Molière had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Molière.

Acquisition of data: Molière, Hamzaoui, Renard-Penna.

Analysis and interpretation of data: Molière, Hamzaoui, Renard-Penna.

Drafting of the manuscript: Molière.

Critical revision of the manuscript for important intellectual content:
Renard-Penna, Delingette, Ploussard, Mathieu, Fiard, Baboudjian, Gran-
ger, Roupret.

Statistical analysis: Molière.

Obtaining funding: None.

Administrative, technical, or material support: None.

Supervision: Renard-Penna.

Other: None.

**Financial disclosures:** Sébastien Molière certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: R. Renard-Penna has received consulting fees from Incepto. These fees were provided for consulting services unrelated to the research presented in this article. The authors declare that this financial relationship did not influence the study design, data collection, analysis, interpretation, or the preparation of the manuscript.

**Funding/Support and role of the sponsor:** None.

**Appendix A. Supplementary material**

Supplementary data to this article can be found online at
https://doi.org/10.1016/j.euo.2024.11.001.

**References**

[1] Hamdy FC, Donovan JL, Lane JA, et al. Fifteen-year outcomes after monitoring, surgery, or radiotherapy for prostate cancer. N Engl J Med 2023;388:1547-58.

[2] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63.

[3] Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin 2024;74:12-49.

[4] Mottet N, Van Den Bergh RCN, Briers E, et al. EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 2021;79:243-62.

[5] Ploussard G, Fiard G, Barret E, et al. French AFU Cancer Committee guidelines-update 2022-2024: prostate cancer-diagnosis and management of localised disease. Prog En Urol 2022;32:1275-372.

[6] Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate Imaging Reporting and Data System version 2.1: 2019 update of Prostate Imaging Reporting and Data System version 2. Eur Urol 2019;76:340-51.

[7] Van Der Leest M, Cornel E, Israël B, et al. Head-to-head comparison of transrectal ultrasound-guided prostate biopsy versus multiparametric prostate resonance imaging with subsequent magnetic resonance-guided biopsy in biopsy-naïve men with elevated prostate-specific antigen: a large prospective multicenter clinical study. Eur Urol 2019;75:570-8.

[8] Rouvière O, Puech P, Renard-Penna R, et al. Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naïve patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study. Lancet Oncol 2019;20:100-9.

[9] Brembilla G, Dell'Oglio P, Stabile A, et al. Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1. Eur Radiol 2020;30:3383-92.

[10] Drost FJH, Osses DF, Nieboer D, et al. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev 2019;4:CD012663.

[11] Johnson DC, Raman SS, Mirak SA, et al. Detection of individual prostate cancer foci via multiparametric magnetic resonance imaging. Eur Urol 2019;75:712-20.

[12] Bulten W, Kartasalo K, Chen PHC, et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med 2022;28:154-63.

[13] Lång K, Josefsson V, Larsson AM, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI):
a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 2023;24:936-44.

[14] James ND, Tannock I, N'Dow J, et al. The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet 2024;403:1683-722.

[15] Bhattacharya I, Khandwala YS, Vesal S, et al. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol 2022;14:17562872221128791.

[16] Van Leeuwen KG, Schalekamp S, Rutten MJCM, Van Ginneken B, De Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 2021;31:3797-804.

[17] George RS, Htoo A, Cheng M, et al. Artificial intelligence in prostate cancer: Definitions, current research, and future directions. Urol Oncol 2022;40:262-70.

[18] Castillo JM, Arif M, Niessen WJ, Schoots IG, Veenland JF. Automated classification of significant prostate cancer on MRI: a systematic review on the performance of machine learning applications. Cancers 2020;12:1606.

[19] Syer T, Mehta P, Antonelli M, et al. Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: a systematic review and recommendations for future studies. Cancers 2021;13:3318.

[20] Roest C, Fransen SJ, Kwee TC, Yakar D. Comparative performance of deep learning and radiologists for the diagnosis and localization of clinically significant prostate cancer at MRI: a systematic review. Life (Basel) 2022;12:1490.

[21] Sushentsev N, Da Silva NM, Yeung M, et al. Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review. Insights Imaging 2022;13:59.

[22] Rouviere O, Jaouen T, Baseilhac P, et al. Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: how accurate are they when tested on independent cohorts?— A systematic review. Diagn Interv Imaging 2023;104:221-34.

[23] Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2020;2:e200029.

[24] Belue MJ, Turkbey B. Tasks for artificial intelligence in prostate MRI. Eur Radiol Exp 2022;6:33.

[25] Van Leenders GJLH, Van Der Kwast TH, Grignon DJ, et al. The 2019 International Society of Urological Pathology (ISUP) consensus conference on grading of prostatic carcinoma. Am J Surg Pathol 2020;44:e87-99.

[26] Arif M, Schoots IG, Castillo Tovar J, et al. Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI. Eur Radiol 2020;30:6582-92.

[27] Santhirasekaram A, Pinto K, Winkler M, Aboagye E, Glocker B, Rockall A. Multi-scale Hybrid transformer networks: application to prostate disease classification. In: Syeda-Mahmood T, Li X, Madabhushi A, et al, editors. Multimodal learning for clinical decision support. Vol. 13050. Lecture notes in computer science. Springer International Publishing AG; 2021. Pp. 12-21.

[28] Gutiérrez Y, Arevalo J, Martinez F. An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer. Phys Med Biol 2022;67:225004.

[29] Zhu L, Gao G, Zhu Y, et al. Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network. Front Oncol 2022;12:958065.

[30] Bhattacharya I, Seetharaman A, Kunder C, et al. Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework. Med Image Anal 2022;75:102288.

[31] Lapa P, Castelli M, Goncalves I, Sala E, Rundo L. A Hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI. Appl Sci (Basel) 2020;10:338.

[32] Saha A, Hosseinzadeh M, Huisman H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med Image Anal 2021;73:102155.

[33] Shao L, Liu Z, Liu J, et al. Patient-level grading prediction of prostate cancer from mp-MRI via GMINet. Comput Biol Med 2022;150:106168.

[34] Hu J, Shen A, Qiao X, et al. Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of Gleason grade group from MRI. Med Phys 2023;50:2279-89.

[35] Weißer C, Netzer N, Görtz M, et al. Weakly supervised MRI slice-level deep learning classification of prostate cancer approximates full voxel- and slice-level annotation: effect of increasing training set size. J Magn Reson Imaging 2024;59:1409-22.

[36] Hosseinzadeh M, Saha A, Brand P, Slootweg I, de Rooij M, Huisman H. Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge. Eur Radiol 2022;32:2224-34.

[37] Sobecki P, Jóźwiak R, Sklinda K, Przelaskowski A. Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images. PeerJ 2021;9:e11006.

[38] Karagoz A, Alis D, Seker ME, et al. Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study. Insights Imaging 2023;14:110.

[39] Bashkanov O, Rak M, Meyer A, et al. Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI. Comput Methods Programs Biomed 2023;239:107624.

[40] Kim H, Margolis DJA, Nagar H, Sabuncu MR. Pulse sequence dependence of a simple and interpretable deep learning method for detection of clinically significant prostate cancer using multiparametric MRI. Acad Radiol 2023;30:966-70.

[41] Sun Z, Wu P, Cui Y, et al. Deep-learning models for detection and localization of visible clinically significant prostate cancer on multi-parametric MRI. J Magn Reson Imaging 2023;58:1067-81.

[42] Jiang KW, Song Y, Hou Y, et al. Performance of artificial intelligence-aided diagnosis system for clinically significant prostate cancer with MRI: a diagnostic comparison study. J Magn Reson Imaging 2023;57:1352-64.

[43] Li D, Han X, Gao J, et al. Deep learning in prostate cancer diagnosis using multiparametric magnetic resonance imaging with whole-mount histopathology referenced delineations. Front Med 2022;8:810995.

[44] Cao R, Zhong X, Afshari S, et al. Performance of deep learning and genitourinary radiologists in detection of prostate cancer using 3-T multiparametric magnetic resonance imaging. J Magn Reson Imaging 2021;54:474-83.

[45] Netzer N, Eith C, Bethge O, et al. Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability. Eur Radiol 2023;33:7463-76.

[46] Rajagopal A, Redekop E, KemiSETTI A, et al. Federated learning with research prototypes: application to multi-center MRI-based detection of prostate cancer with diverse histopathology. Acad Radiol 2023;30:644-57.

[47] Gutierrez Y, Arevalo J, Martinez F. Multimodal contrastive supervised learning to classify clinical significance MRI regions on prostate cancer. Annu Int Conf IEEE Eng Med Biol Soc 2022;2022:1682-5.

[48] Zhao L, Bao J, Qiao X, et al. Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study. Eur J Nucl Med Mol Imaging 2023;50:727-41.

[49] Wang Y, Wang M. Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network. Phys Med 2020;80:92-100.

[50] Hu L, Fu C, Song X, et al. Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique. Cancer Imaging 2023;23:6.

[51] Seetharaman A, Bhattacharya I, Chen LC, et al. Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging. Med Phys 2021;48:2960-72.

[52] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. http://arxiv.org/abs/1409.1556.

[53] Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K. DenseNet: implementing efficient ConvNet descriptor pyramids. http://arxiv.org/abs/1404.1869.
[54] Liu S, Zheng H, Feng Y, Li W. Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013428.

[55] Szegedy C, Wei Liu, Yangqing Jia, et al. Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2015. p. 1-9.

[56] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2016. Pp. 2818-26.

[57] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention - MICCAI 2015. Vol. 9351. Lecture notes in computer science. Springer International Publishing; 2015. Pp. 234-41.

[58] Milletari F, Navab N, Ahmadi SA. V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE; 2016. p. 565-71.

[59] Zhang KS, Schelb P, Netzer N, et al. Pseudoprospective paraclinical interaction of radiology residents with a deep learning system for

prostate cancer detection: experience, performance, and identification of the need for intermittent recalibration. Invest Radiol 2022;57:601-12.

[60] Arif M, Schoots IG, Castillo JMT, Roobol MJ, Niessen W, Veenland JF. Computer aided diagnosis of clinically significant prostate cancer in low-risk patients on multi-parametric MR images using deep learning. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020). IEEE International Symposium on Biomedical Imaging. IEEE; 2020. p. 1482-5. https://www.webofscience.com/wos/woscc/summary/28afcbae-1bf4-442d-9b31-bcc7ca0e0c8a-98da9d61/date-ascending/1.

[61] Cho HH, Kim CK, Park H. Overview of radiomics in prostate imaging and future directions. Br J Radiol 2022;95:20210539.

[62] Couchoux T, Jaouen T, Melodelima-Gonindard C, et al. Performance of a region of interest-based algorithm in diagnosing International Society of Urological Pathology grade group ≥2 prostate cancer on the MRI-FIRST Database-CAD-FIRST study. Eur Urol Oncol 2024;7:1113-22.

[63] Saha A, Bosma JS, Twilt JJ, et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 2024;25:879-87.