Extracapsular extension (ECE) is detected in approximately one-third of newly diagnosed prostate cancer (PCa) cases at stage T3a or higher and is associated with increased rates of positive surgical margins and early biochemical recurrence following radical prostatectomy (RP). This study presents the development of AutoRadAI, an end-to-end, user-friendly artificial intelligence (AI) pipeline designed for the identification of ECE in PCa through the analysis of multiparametric MRI (mpMRI) fused with prostate histopathology. The dataset consists of 1001 patients, including 510 pathology-confirmed positive ECE cases and 491 negative ECE cases. AutoRadAI integrates comprehensive preprocessing followed by a sequence of two novel deep learning (DL) algorithms within a multi-convolutional neural network (multi-CNN) strategy. The pipeline exhibited strong performance during its evaluation. In the blind testing phase, AutoRadAI achieved an area under the curve (AUC) of 0.92 for assessing image quality and 0.88 for detecting the presence of ECE in individual patients. Additionally, AutoRadAI is implemented as a user-friendly web application, making it ideally suited for clinical applications. Its data-driven accuracy offers significant promise as a diagnostic and treatment planning tool. Detailed instructions and the full pipeline are available at https://autoradai.anvil.app and on our GitHub page at https://github.com/PKhosravi-CityTech/AutoRadAI.

PK is listed as the inventor on a provisional patent filed by the City University of New York (application number 63/643,711) related to the technology described in this study. IH has received consulting fees from NOOR SCIENCES INC. as a consultant and payment for presentations from Fairtility as a speaker unrelated to this study. AH has received grants or contracts from the National Cancer Institute and consulting fees from Intuitive Surgical and Teleflex. SSV holds stock or stock options and has other financial or non-financial interests as the Chief Innovation Officer at Promaxo Inc.

https://github.com/PKhosravi-CityTech/AutoRadAI

https://autoradai.anvil.app/

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the AdventHealth Institutional Review Board (IRB), Orlando, FL, under protocol number 3009855250. The IRB approved 06/15/2023.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All data produced in the present study are available upon reasonable request to the authors. Clinical information such as pathology and age can be accessed through our GitHub repository at https://github.com/PKhosravi-CityTech/AutoRadAI. The MR images analyzed in this study are not publicly available due to privacy and security concerns and the sensitivity of medical data. These datasets are proprietary to the contributing institutions and are only available to researchers involved in institutional review board (IRB)-approved research collaborations with these centers.

https://github.com/PKhosravi-CityTech/AutoRadAI