Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

NCT ID: NCT06662708

Last Updated: 2024-10-29

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 200 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-12-01
• Study Completion Date: 2030-12-31

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.

Conditions

Prostate Cancer; Prostate Intraductal Carcinoma; Prostate Cancer Aggressiveness; Prostate Cancer Stage; Pathology

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: TRIPLE

Study Groups

Experimental group

This group of patients will receive predictions assisted by artificial intelligence models.

Group Type: EXPERIMENTAL

Accurate Prediction Artificial Intelligence Models

Intervention Type: DIAGNOSTIC_TEST

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

Control Group

This group of patients will not receive predictions assisted by artificial intelligence models.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Accurate Prediction Artificial Intelligence Models

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

Intervention Type: DIAGNOSTIC_TEST

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;

• Minimum Eligible Age: 30 Years
• Eligible Sex: MALE
• Accepts Healthy Volunteers: Yes

Sponsors

Institute of Automation, Chinese Academy of Sciences

OTHER

Sponsor Role: collaborator

Shao Pengfei

OTHER

Sponsor Role: lead

Responsible Party

Shao Pengfei

Chief physician

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

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

Nanjing, Jiangsu, China

Countries

China

Central Contacts

Pengfei Shao, Professor

Role: CONTACT

spf032@hotmail.com

13851925825

Pan Zang, Postgraduate

Role: CONTACT

896381164@qq.com

18914730216

Other Identifiers

Shao Pengfei

Identifier Type: -

Identifier Source: org_study_id