Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging

Abstract

Purpose While multi‐parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per‐pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. Methods We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole‐mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per‐pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. Results Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. Conclusions Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.

Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations

“…Castillo et al ( 27 ) systematically reviewed the performance of machine learning applications in PCa classification based on MRI, and found that only one paper (27 publications) compared the performance of radiologists with or without DL model assistance, and presented that evaluation should be performed in a real clinical setting since the ultimate goal of these models is to assist the radiologists in diagnosis. Seetharaman et al ( 28 ) developed a SPCNet model accurately detected aggressive PCa. In our study, we evaluated the DL model in an independent test dataset to assess its clinical application value and to compare it with junior and senior radiologists.…”

Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities

“…They improved the accuracy by 20% compared to other methods with similar approaches (accuracy 80.97%). Seetharaman et al [ 99 ] introduced the Stanford Prostate Cancer Network (SPCNet) to learn features specific to each sequence of MRI mapped with histopathology images, achieving a AUC of 0.86–0.89 to detect aggressive cancers and 0.75–0.85 to detect clinically significant lesions.…”

Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI

“…Multiparametric MRI (mpMRI) scans interpreted by expert prostate radiologists provide the best non-invasive diagnosis [15], but is a limited resource that cannot be leveraged freely. Computer-aided diagnosis (CAD) can assist radiologists to diagnose csPCa, but present-day solutions lack standalone performance comparable to that of expert radiologists [16]- [20].…”