ProsDectNet: Bridging the Gap in Prostate Cancer Detection via Transrectal B-mode Ultrasound Imaging
CoRR(2023)
摘要
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.
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