A deep learning framework to assess the feasibility of localizing prostate cancer on b-mode transrectal ultrasound images

Hassan Jahanandish; Sulaiman Vesal; Indrani Bhattacharya; Cynthia Xinran Li; Richard E. Fan; Geoffrey A. Sonn; Mirabela Rusu

doi:10.1117/12.3008819

2 April 2024 A deep learning framework to assess the feasibility of localizing prostate cancer on b-mode transrectal ultrasound images

Hassan Jahanandish, Sulaiman Vesal, Indrani Bhattacharya, Cynthia Xinran Li, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu

Author Affiliations +

Proceedings Volume 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 129320S (2024) https://doi.org/10.1117/12.3008819
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Prostate cancer is the second-most lethal cancer in men. Since early diagnosis and treatment can drastically increase the 5-year survival rate of patients to >99%, magnetic resonance imaging (MRI) has been utilized due to its high sensitivity of 88%. However, due to lack of access to MRI, transrectal b-mode ultrasound (TRUS)-guided systematic prostate biopsy remains the standard of care for 93% of patients. While ubiquitous, TRUS-guided prostate biopsy suffers from the lack of lesion targeting, resulting in a sensitivity of 48%. To address this gap, we perform a preliminary study to assess the feasibility of localizing clinically significant cancer on b-mode ultrasound images of the prostate as input and propose a deep learning framework that learns to distinguish cancer at the pixel level. The proposed deep learning framework consists of a convolutional network with deep supervision at various scales and a clinical decision module that simultaneously learns to reduce false positive lesion predictions. We evaluated our deep learning framework using b-mode TRUS data with pathology confirmation from 330 patients, including 123 patients with pathology-confirmed cancer. Our results demonstrate the feasibility of using b-mode ultrasound images to localize prostate cancer lesions with a patient-level sensitivity and specificity of 68% and 91% respectively, compared to the reported clinical standard of 48% and 99%. The outcomes of this study show the promise of using a deep learning framework to localize prostate cancer lesions on the universally available b-mode ultrasound images; eventually improving the prostate biopsy procedures and enhancing the clinical outcomes for prostate cancer patients.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Hassan Jahanandish, Sulaiman Vesal, Indrani Bhattacharya, Cynthia Xinran Li, Richard E. Fan, Geoffrey A. Sonn, and Mirabela Rusu "A deep learning framework to assess the feasibility of localizing prostate cancer on b-mode transrectal ultrasound images", Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography, 129320S (2 April 2024); https://doi.org/10.1117/12.3008819

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