Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

October 27, 2024 updated by: Shao Pengfei

Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Estimated)

200

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Pan Zang, Postgraduate
  • Phone Number: 18914730216
  • Email: 896381164@qq.com

Study Locations

  • China
    • Jiangsu
      • Nanjing, Jiangsu, China, 210036
        • The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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;

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Triple

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Experimental group
This group of patients will receive predictions assisted by artificial intelligence models.
Diagnostic test: 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
No Intervention: Control Group
This group of patients will not receive predictions assisted by artificial intelligence models.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
Time Frame: From subject enrolment to initial post-surgery, usually 30-90 days.
'AUC' refers to the area under the ROC (Receiver Operating Characteristic) curve, which indicates the performance of the model in predicting immunohistochemistry-related pathological information of prostate cancer after surgery, and the AUC ranges from 0-1, with the larger value indicating the better prediction effect.
From subject enrolment to initial post-surgery, usually 30-90 days.
Predicting the performance of post-radical pathology by the 'AUC' comprehensive assessment model
Time Frame: From subject enrolment to initial post-surgery, usually 30-90 days.
'AUC' refers to the area under the ROC (Receiver Operating Characteristic) curve, indicating the level of performance of the model in predicting prostate cancer in the preoperative period, with AUC ranging from 0-1, with larger values indicating better prediction results.
From subject enrolment to initial post-surgery, usually 30-90 days.
'F1 Score' to assess performance of preoperative 3D modelling
Time Frame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
A reconciled average of the preoperative 3D modelling precision and recall assessed through the 'F1 score', which represents the match to the real situation.
From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Assess the amount of cost difference between the predictive model and the clinical approach by "economic cost savings"
Time Frame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
Compare the difference in costs incurred using a predictive model with those predicted using a clinical approach, the difference will be in yuan.
From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
"Diagnostic Time" evaluate the time taken to predict immunohistochemistry-related pathology in the postoperative period.
Time Frame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
The time spent postoperatively predicting or assisting the pathologist in obtaining immunohistochemistry-related pathology information is assessed by "Diagnostic Time" and will be measured in minutes.
From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shao Pengfei

Collaborators

Institute of Automation, Chinese Academy of Sciences

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

December 1, 2024

Primary Completion (Estimated)

January 1, 2030

Study Completion (Estimated)

December 31, 2030

Study Registration Dates

First Submitted

August 13, 2024

First Submitted That Met QC Criteria

October 27, 2024

First Posted (Actual)

October 29, 2024

Study Record Updates

Last Update Posted (Actual)

October 29, 2024

Last Update Submitted That Met QC Criteria

October 27, 2024

Last Verified

October 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Shao Pengfei

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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