Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

Shao Pengfei

Status

Not yet enrolling

Conditions

Prostate Intraductal Carcinoma

Prostate Cancer Stage

Prostate Cancer Aggressiveness

Pathology

Prostate Cancer

Treatments

Diagnostic Test: Accurate Prediction Artificial Intelligence Models

Study type

Interventional

Funder types

Other

Identifiers

NCT06662708

Shao Pengfei

Details and patient eligibility

About

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:

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.
whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants.

Participants will:

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

Full 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.

Enrollment

200 estimated patients

Sex

Male

Ages

30+ years old

Volunteers

Accepts Healthy Volunteers

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;