Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

Trial Title: Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

NCT ID: NCT06662708

Condition: Prostate Cancer
Prostate Intraductal Carcinoma
Prostate Cancer Aggressiveness
Prostate Cancer Stage
Pathology

Conditions: Official terms:
Prostatic Neoplasms
Carcinoma, Intraductal, Noninfiltrating
Aggression

Conditions: Keywords:
Articicial Intelligence
Whole Slide Image
mp-MRI
Prostate Cancer
Prediction Model

Study type: Interventional

Study phase: N/A

Overall status: Not yet recruiting

Study design:

Allocation: Randomized

Intervention model: Parallel Assignment

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

Masking: Triple (Participant, Care Provider, Outcomes Assessor)

Intervention:

Intervention type: Diagnostic Test
Intervention name: Accurate Prediction Artificial Intelligence Models
Description: Diagnostic Test: Accurate Prediction Artificial Intelligence Models Post-operative pathology, precise pre-operative diagnosis (including benign and malignant, invasive, grading, subtypes) or 3D lesion modelling will be predicted based on the AI predictive model in response to the information provided
Arm group label: Experimental group

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.

Criteria for 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;

Gender: Male

Minimum age: 30 Years

Maximum age: N/A

Healthy volunteers: Accepts Healthy Volunteers

Locations:

Facility:
Name: The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)

Address:
City: Nanjing
Zip: 210036
Country: China

Start date: December 1, 2024

Completion date: December 31, 2030

Lead sponsor:
Agency: Shao Pengfei
Agency class: Other

Collaborator:
Agency: Institute of Automation, Chinese Academy of Sciences
Agency class: Other

Source: The First Affiliated Hospital with Nanjing Medical University

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06662708