Clinical trial NCT06662708: Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer
Official title: Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Prostate Cancer
Prostate Intraductal Carcinoma
Prostate Cancer Aggressiveness
Prostate Cancer Stage
Pathology
Keywords: Articicial Intelligence
Whole Slide Image
mp-MRI
Prostate Cancer
Prediction Model
Lead sponsor: The First Affiliated Hospital with Nanjing Medical University
Sponsor class: OTHER
Collaborators: name: Institute of Automation, Chinese Academy of Sciences
First posted: 2024-10-29
Enrollment: 200
Sex: MALE
Minimum age (years): 30
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Inclusion of enrolled patients in an artificial intelligence predictive model that predicts postoperative pathology, precise preoperative diagnosis (including benign and malignant, invasive, grading, and subtypes) or 3D lesion modelling based on the information provided
Primary purpose: DIAGNOSTIC

Brief summary: The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are:

1. whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally.
2. whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants.

Participants will:

1. complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them.
2. Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.

Detailed description: Based on artificial intelligence technology, the prediction model is built by outlining the quantitative mapping correlation between annotated prostate cancer Whole Slide Images and MRI, and clarifying the common features. Firstly, the model can accurately diagnose the radical pathology of prostate cancer, which can be exempted from immunohistochemistry to obtain detailed pathological information; secondly, the established AI prediction model can accurately diagnose the benign/malignant, invasiveness, grade and subtype of prostate cancer by predicting the participant's MRI images before surgery or puncture, so that a personalised treatment plan can be formulated for the patient before operation or puncture. Finally, based on AI technology, the model learns from the MRI images and performs 3D reconstruction of the prostate and lesions before surgery/puncture, thus clarifying the exact location of the lesions and guiding puncture or surgical treatment.

Eligibility criteria: Inclusion Criteria:

* Patients with suspected PCa (elevated PSA or suspicious positive lesions on ultrasound or MRI results);

Exclusion Criteria:

* Previous treatment of the prostate in any form, including surgery, radiotherapy/chemotherapy, endocrine therapy, targeted therapy and immunotherapy;
* Patients with any item missing from the baseline clinical and pathological information;
* Patients with a history of other malignancies, serious comorbidities or other health problems;
* Unable to provide/sign an informed consent form;
* Patients who, in the judgement of the investigator, are deemed unfit to participate in this clinical trial;

Primary outcomes:
measure: Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
timeFrame: From subject enrolment to initial post-surgery, usually 30-90 days.
measure: Predicting the performance of post-radical pathology by the 'AUC' comprehensive assessment model
timeFrame: From subject enrolment to initial post-surgery, usually 30-90 days.
measure: 'F1 Score' to assess performance of preoperative 3D modelling
timeFrame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
Secondary outcomes:
measure: Assess the amount of cost difference between the predictive model and the clinical approach by "economic cost savings"
timeFrame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
measure: "Diagnostic Time" evaluate the time taken to predict immunohistochemistry-related pathology in the postoperative period.
timeFrame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
Other outcomes:
Locations:
city: Nanjing
country: China
facility: The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
state: Jiangsu
Study officials:
References: