Prostate Cancer Inference via Weakly-Supervised Learning using a Large Collection of Negative MRI

Abstract

Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continues to gain clinical acceptance as the preferred imaging technique for non-invasive detection and grading of PCa. However, the machine learning-based diagnosis systems for PCa are often constrained by the limited access to accurate lesion ground truth annotations for training. The performance of the machine learning system is highly dependable on both quality and quantity of lesion annotations associated with histopathologic findings, resulting in limited scalability and clinical validation. Here, we propose the baseline MRI model to alternatively learn the appearance of mp-MRI using radiology-confirmed negative MRI cases via weakly supervised learning. Since PCa lesions are case-specific and highly heterogeneous, it is assumed to be challenging to synthesize PCa lesions using the baseline MRI model, while it would be relatively easier to synthesize the normal appearance in mp-MRI. We then utilize the baseline MRI model to infer the pixel-wise suspiciousness of PCa by comparing the original and synthesized MRI with two distance functions. We trained and validated the baseline MRI model using 1,145 negative prostate mp-MRI scans. For evaluation, we used separated 232 mp-MRI scans, consisting of both positive and negative MRI cases. The 116 positive MRI scans were annotated by radiologists, confirmed with post-surgical whole-gland specimens. The suspiciousness map was evaluated by receiver operating characteristic (ROC) analysis for PCa lesions versus non-PCa regions classification and free-response receiver oper-ating characteristic (FROC) analysis for PCa localization. Our proposed method achieved 0.84 area under the ROC curve and 77.0% sensitivity at one false positive per patient in FROC analysis.

Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI

“…In contrast, medical imaging modalities in radiology and nuclear medicine exhibit much lower inter-sample variability, where the spatial content of a scan is limited by the underlying imaging protocols and human anatomy. In agreement with recent studies [2][3][4], we hypothesize that variant architectures of U-Net can exploit this property via an explicit anatomical prior, particularly at the task of csPCa detection in bpMRI. To this end, we present a probabilistic population prior P , constructed using radiologically-estimated csPCa annotations and CNN-generated prostate zonal segmentations of 700 training samples.…”

“…Meanwhile, machine learning models can adapt several techniques, such as reference coordinate systems [11,12] or anatomical maps [2], to integrate domain-specific priori into CNN architectures. In recent years, the inclusion of zonal priors [4] and prevalence maps [3] have yielded similar benefits in 2D CAD systems for prostate cancer.…”

A review of artificial intelligence in prostate cancer detection on imaging

“…23,40,45 To test generalizability, some studies trained AI models using one group of patients, and tested on a different group, including patients with different disease distributions 50,51 or different label types. 40,59 Due to the difficulty in acquiring large data sets of pathology-confirmed cancer labels to train AI models, a study 67 trials (see Section "Challenges in AI for PCa" for more details).…”

End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction

“…Parallel to recent studies in medical image computing (Gibson et al, 2018;Dalca et al, 2018;Wachinger et al, 2018;Cao et al, 2019b) on infusing spatial priori into CNN architectures, we hypothesize that M 1 can benefit from an explicit anatomical prior for csPCa detection in bpMRI. To this end, we construct a probabilistic population prior P, as introduced in our previous work (Saha et al, 2020).…”

“…The study demonstrated that the inclusion of PZ and TZ segmentations can introduce an average increase of 5.3% detection sensitivity, between 0.5-2.0 false positives per patient. In a separate study, Cao et al (2019b) constructed a probabilistic 2D prevalence map from 1055 MRI slices. Depicting the typical sizes, shapes and locations of malignancy across the prostate anatomy, this map was used to weakly supervise a 2D U-Net for PCa detection.…”