ProsDectNet: Bridging the Gap in Prostate Cancer Detection via
  Transrectal B-mode Ultrasound Imaging
Sulaiman Vesal Indrani Bhattacharya Hassan Jahanandish Xinran Li Zachary Kornberg Steve Ran Zhou Elijah Richard Sommer Moon Hyung Choi Richard E. Fan Geoffrey A. Sonn Mirabela Rusu
Interpreting traditional B-mode ultrasound images can be challenging due to
image artifacts (e.g., shadowing, speckle), leading to low sensitivity and
limited diagnostic accuracy. While Magnetic Resonance Imaging (MRI) has been
proposed as a solution, it is expensive and not widely available. Furthermore,
most biopsies are guided by Transrectal Ultrasound (TRUS) alone and can miss up
to 52% cancers, highlighting the need for improved targeting. To address this
issue, we propose ProsDectNet, a multi-task deep learning approach that
localizes prostate cancer on B-mode ultrasound. Our model is pre-trained using
radiologist-labeled data and fine-tuned using biopsy-confirmed labels.
ProsDectNet includes a lesion detection and patch classification head, with
uncertainty minimization using entropy to improve model performance and reduce
false positive predictions. We trained and validated ProsDectNet using a cohort
of 289 patients who underwent MRI-TRUS fusion targeted biopsy. We then tested
our approach on a group of 41 patients and found that ProsDectNet outperformed
the average expert clinician in detecting prostate cancer on B-mode ultrasound
images, achieving a patient-level ROC-AUC of 82%, a sensitivity of 74%, and a
specificity of 67%. Our results demonstrate that ProsDectNet has the potential
to be used as a computer-aided diagnosis system to improve targeted biopsy and
treatment planning.