Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
Conditions: Prostatic Neoplasms, Lymphatic Metastasis
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

                   
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Clinical Research Program 
 
Project name: Prospective Validation of Pathology-based Artificial 
Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate 
Cancer 
Bidding organization: Sun Yat-sen Memorial Hospital 
Version No.: V1.1 
Version date: January 31, 2025 
Project Manager: Tianxin Lin 
NCT number: NCT06253065 
 

                                   
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I. Summary of the research program  
Lymph node metastasis is a prevalent mode of spread in prostate cancer, and 
accurate postoperative pathological lymph node staging holds significant implications 
for subsequent treatment and prognosis. However, the existing approach to pathological 
assessment relies on manual image review by pathologists, leading to low diagnostic 
efficiency and potential missing diagnosis of micrometastases. In our prior study, we 
addressed this challenge by developing a deep learning-based AI diagnostic model 
(named ProCaLNMD) for prostate cancer lymph node metastasis detection. The model 
demonstrated promising results in retrospective validation. 
This study, framed as a diagnostic trial without intervention, seeks to enroll patients 
who have undergone radical prostatectomy with pelvic lymph node dissection for 
prostate cancer, possessing comprehensive clinical and pathologic data. The study is 
planned to enroll approximately 200 patients in our center, with 2,000 images of 
corresponding pathological lymph node sections from these patients, and gather the 
relevant clinicopathological data to prospectively validate the performance of the AI 
model. 
II. Background of the study 
Globally, prostate cancer ranks as the second most common malignant tumor in 
men, surpassed only by lung cancer. It stands out as the predominant malignant tumor 
within the male genitourinary system and holds the highest incidence in the male 
urogenital system1. Lymph node metastasis serves as a prevalent way of its spread, and 
the identification of regional lymph node metastasis plays a pivotal role in guiding 
subsequent postoperative treatment and determining prognosis. Despite its significance, 
achieving accurate pathologic lymph node staging remains a formidable challenge, 
particularly when dealing with micrometastases. The inherent characteristics of these 

                                   
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lesions, such as small diameters, minimal lesion involvement, and the limited resolution 
of the human eye, can contribute to missing diagnoses. Conventional pathology has 
demonstrated restricted sensitivity in detecting micrometastases, posing an increased 
risk of cancer progression and potential delays in implementing adjuvant therapy. While 
immunohistochemistry and molecular markers can enhance the detection of 
micrometastases, they are expensive and not routinely used2.  
In recent years, the widespread adoption of artificial intelligence in digital 
pathology diagnosis has been facilitated by advancements in whole slide imaging 
technology, coupled with increased computational power and data storage capacity3. 
This technology has the capability to extract image features from pathological images 
that may be missed by the human eye, enabling accurate disease diagnosis, prognosis 
assessment, and treatment predictions. Literature reports highlight the successful 
construction of a deep learning-based AI diagnostic model for lymph node metastasis 
in breast cancer. The diagnostic performance of this model has been shown to rival that 
of experts4. Our previous study involved the development and retrospective validation 
of a deep learning-based AI diagnostic model for pathological lymph node staging in 
prostate cancer. This model utilized postoperative resected lymph node pathological 
images, complemented by the construction of AI-assisted software for pathological 
lymph node reading. In retrospective validation, the AI system demonstrated strong 
performance, with diagnostic sensitivity comparable to senior pathologists. This 
capability holds promise in assisting physicians by reducing missed diagnoses of 
micrometastases and enhancing diagnostic accuracy and efficiency. However, there 
remains a need for a well-established prospective validation to assess the diagnostic 
proficiency and practical application of this model in a real clinical setting. 
III. Research purpose 
To evaluate the diagnostic performance and application value of the pre-developed 
AI diagnostic model for prostate cancer lymph node metastasis detection in real clinical 

                                   
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settings. 
IV. Research design  
The study is a prospective diagnostic trial and the flow chart of the study design is 
shown below: 
 
V. Research Program and Technical Route 
(i) Study population 
Approximately 200 patients with prostate cancer in our center will be prospectively 
enrolled, and their lymph node pathological slides will be collected after pelvic 
lymphatic dissection. 
1. Inclusion criteria 
(1) Patients with prostate cancer who underwent radical prostatectomy and pelvic 
lymph node dissection; 
(2) Complete clinical and pathological information. 
2. Exclusion criteria 
(1) Presence of other tumors with pelvic lymph node metastases; 

                                   
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(2) Low quality images; 
(3) Patients refusing to participate in this research. 
(ii) Data collection 
1. Collect clinical and pathological data of the enrolled patients; 
2. Digital whole slide images (WSI) of the patient's lymph node histopathological 
sections will be generated using a digital scanner. Low-quality images were eliminated 
from consideration. Approximately 10 lymph node pathology slides will be acquired 
from each patient who underwent radical prostatectomy with pelvic lymphatic 
dissection for prostate cancer, resulting in the collection of around 2000 pathologic 
lymph node images. 
(iii) AI algorithm models 
Capture the thumbnail of the Whole Slide Image (WSI) and delineate the 
organizational block region for classification. Employ a sliding window approach to 
traverse the tissue block region, generating patches of size 3*2028*2048 by moving 
from top to bottom and left to right. Input all the patches into the RegNetY_40 model, 
obtain the segmentation prediction probability for each patch, and convert the 
probabilities to 8-bit format (0~255). Set the probability threshold at 254, classifying 
pixels with a probability value equal to or greater than 254 as Tumor pixels. Calculate 
the count of tumor pixels within the WSI, using a number threshold of 0. If the number 
of pixels in the cancer category within the WSI is greater than 0, classify the WSI as 
positive (indicating the presence of tumor lymphatic metastasis); otherwise, classify it 
as negative (indicating the absence of tumor lymphatic metastasis). The flowchart is 
shown below:  

                                   
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(iv) Indicators for evaluating the results of the study 
1. Accuracy: Ratio of the combined number of true-positive and true-negative cases 
predicted by the AI model to the total number of cases tested. 
2. Sensitivity: Ratio of positive cases identified by the AI model to the number of 
positive cases confirmed by the gold standard. 
3. Specificity: Ratio of negative cases identified by the AI model to the number of 
negative cases confirmed by the gold standard.  
(v) Informed consent 
The study, titled " Prospective Validation of Pathology-based Artificial Intelligence 
Diagnostic Model for Lymph Node Metastasis in Prostate Cancer," is a diagnostic trial 
devoid of interventions. Prior to the enrollment of each patient, and before they undergo 
the scheduled procedure, the researcher provides a comprehensive introduction to the 
protocol, purpose, procedures, associated costs, potential benefits, possible risks, 
confidentiality measures, and the patient's rights within the trial. Subsequently, an 
informed consent form is distributed to the patient, and enrollment in the study occurs 
only after the patient has signed the informed consent. 
(vi) Ethical requirements 
The study adhered to the guidelines outlined in the Good Clinical Research Practice 

                                   
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and the Declaration of Helsinki. Before the commencement of the trial, the protocol 
received approval from the hospital's Ethics Committee. In the event that a revision to 
the protocol becomes necessary during the study, the updated version must be 
resubmitted for review by the Ethics Committee. The investigator is required to await 
approval from the Ethics Committee before implementing any modifications to the 
protocol 
In the course of this study, clinical data and personal information from research 
participants will be gathered for scientific research purposes, potentially implicating the 
privacy rights of the patients. To safeguard confidentiality, participants and data 
analysts involved in the study will be required to sign a confidentiality agreement. This 
agreement ensures that personal information and disease-related details of patients will 
not be disclosed to any individuals or organizations unrelated to the study. Stringent 
measures will be implemented to manage the collected patient data in a unified manner, 
mitigating the risk of personal information leakage. 
VI. Security evaluation 
 
None. 
VII. Data collection and management 
1. Study subjects 
It is planned to prospectively enroll about 200 consecutive prostate cancer patients who 
need to undergo radical prostatectomy and pelvic lymph node dissection in our center 
from January 2024 to December 2025 for the validation of the AI model. 
1.1 Inclusion criteria 
(1) Patients with prostate cancer who underwent radical prostatectomy and pelvic 
lymph node dissection; 
(2) Complete clinical and pathologic information. 

                                   
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1.2 Exclusion criteria 
(1) Presence of other tumors that metastasize to the pelvic lymph nodes; 
(2) Low quality images; 
(3) Patients refusing to be enrolled. 
2. Data collection 
2.1 Clinical and pathologic data were collected from the enrolled patients; 
2.2 Digitally scanning the pathologic histological sections of the patient's 
postoperatively resected lymph nodes. Approximately 10 lymph node pathology slides 
will be acquired from each patient, resulting in the collection of around 2000 
pathological lymph node images. 
VIII. quality management program 
(i) Measures to improve consistency of observations 
1. Every researcher involved in this project must undergo pre-study training to ensure 
a comprehensive understanding of the research program and the specific implications 
of its indicators. This initiative aims to enhance both intra-observer and inter-observer 
consistency among the clinical research data collectors, ultimately ensuring the 
reliability of the clinical research conclusions. 
2. Each clinical research unit is expected to appoint an individual responsible for 
regularly monitoring the progress of the clinical research. This designated person will 
meticulously verify data and records to maintain the accuracy and integrity of the 
research findings. 
(ii) Measures to ensure subject compliance 
The subject profile must be strictly controlled for variables through the following 
measures: 

                                   
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1. Consistent involvement of physicians in clinical studies within each unit. 
2. Researchers are required to diligently elucidate the informed consent of enrolled 
patients. This ensures a comprehensive understanding of the trial study's processes 
and the associated obligations of cooperation, thereby fostering active participation 
in the trial.  
(iii) Monitoring of clinical trials 
Competent monitors, designated by the sponsor, are tasked with conducting 
routine monitoring activities both before, during, and after the trial. This ensures strict 
adherence to all aspects of the research protocol and meticulous scrutiny of the source 
material. 
IX. Pre-assessment of project risk benefits and risk control plan 
Potential Benefits: Expected to assist pathologists in reducing missed diagnosis of 
lymph node micrometastases; No potential risk. 
X. References 
1 
Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and 
Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71, 209-249, 
doi:10.3322/caac.21660 (2021). 
2 
Prado, K., Zhang, K. X., Pellegrini, M. & Chin, A. I. Sequencing of cancer cell subpopulations 
identifies micrometastases in a bladder cancer patient. Oncotarget 8, 45619-45625, 
doi:10.18632/oncotarget.17312 (2017). 
3 
Shao, D. et al. Artificial intelligence in clinical research of cancers. Brief Bioinform 23, 
doi:10.1093/bib/bbab523 (2022). 
4 
Ehteshami Bejnordi, B. et al. Diagnostic Assessment of Deep Learning Algorithms for 
Detection of Lymph Node Metastases in Women With Breast Cancer. Jama 318, 2199-2210, 
doi:10.1001/jama.2017.14585 (2017).