Investigation of Impact of AI on Prostate Cancer Workflow

Clinicaltrials.gov ID: NCT07084779

Status RECRUITING

Phase NA

Age 55 - 80 Years

Enrollments 150

Conditions

Prostate Cancer, Prostatic Neoplasm, Cancer of the Prostate, Neoplasms, Prostate

Summary

This study will enroll participants who are undergoing an MRI before a prostate biopsy due to suspected prostate cancer. The purpose of this study is to see if the use of Artificial Intelligence (AI) helps detect lesions on an MRI better than a radiologist not using AI. The AI Rad Companion (AIRC) Prostate MRI application is a software that uses measurements of the prostate and will be utilized in this study to help detect potential cancerous lesions. The AI software will assign the lesions a PI-RADS score, which is a way to measure the chance of the lesion being cancer.There are two parts to this study. The first part involves comparing the interpretation of prostate MRI images by a radiologist alone, a radiologist aided by AI, and AI alone. A systematic biopsy will be completed per standard of care. The radiologist may opt to include up to 2 additional AI-identified targets to biopsy in addition to those biopsied for standard of care.The second part of the study involves utilizing the MRI images from the first part of the study in addition to retrospective prostate MRI images. These de-identified images, along with Prostate Image Quality (PI-QUAL) scores, clinical data, and biopsy results will be sent to Siemens in order to aid in the development of methods to identify good or bad image quality in prostate MRI images.

Detailed Description

Prostate cancer is the most diagnosed cancer among men in the United States and the second most prevalent cancer in men worldwide (1,2). MRI can better triage patients to undergo biopsy, while providing information about tumors and treatment progress (4). However, there are challenges with MRI image quality, the interpretation of the images, and the potential for false positives and negatives. With the annual number of prostate exams rapidly increasing (5), there is a need for accurate and reliable prostate MRI interpretation. One proposed approach is to use an AI-based lesion detection software in addition to radiologists’ interpretations. The Siemens Healthineers (“Siemens”) business lines Digital & Automation (“D&A”) and Magnetic Resonance (“MR”) have developed the AI Rad Companion Prostate MRI (AIRC), which aids in the detection of potential prostate cancer. The goal of this study is to investigate the effects of the use of AIRC in improving the standard of care in prostate cancer detection and treatment. Participation in the study will be about an hour to review the consent form. All other study procedures (MRI, biopsy) will be standard of care.

Locations

1 location Found with status Recruiting

Status

RECRUITING

Contact Person

Andrei Purysko
216-445-9005
[email protected]

Principal Investigator

Andrei Purysko, MD

Eligibility Criteria

Inclusion Criteria:

* Plan of care is to undergo a biopsy of the prostate after a pre-biopsy MRI
* Age 55-80
* Prostate-specific antigen (PSA) between 3-10 ng/mL
* No prior diagnosis or treatment of prostate cancer

Exclusion Criteria:

* Pre-biopsy MRI is of low quality
* PI-QUAL score of 1 using PI-QUAL version 2

Study Plan

AI-aided MRI & Prostate Biopsy

DEVICE:
AI
Description:
Following the completion of a pre-biopsy prostate MRI, the radiologist will interpret the MRI. Once interpreted by the radiologist alone, the radiologist will interpret the scan while aided by AI. A systematic biopsy in conjunction with radiologist-identified targets will be completed per standard of care, with the optional inclusion of up to 2 AI-detected targets.nnWhen completing a biopsy per SOC, the prostate is divided into quadrants. In addition to noted targets, samples are taken systematically from each quadrant. If targets are detected by AI that were not identified by the physician when reviewing the MRI, these targets will be sampled. Sampling of these targets will not be in addition to the systematic sampling in each quadrant, but in place of up to two of the samples biopsied systematically