Machine learning applications in prostate cancer magnetic resonance imaging

Abstract

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its 'black box' nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.

Keywords: Machine learning; Magnetic resonance imaging; Prostate; Prostatic neoplasms; Radiomics.

Fig. 1

Prostate multiparametric magnetic resonance imaging showing a neoplastic lesion of the right peripheral zone (arrows). The lesion is hypointense on axial (a) and coronal (l) T2-weighted images and demonstrates diffusion restriction on diffusion-weighted images (b values 0, 100, 500, 1000, and 1400 s/mm², from b to f, respectively), confirmed by the apparent diffusion coefficient map (g). Lesion enhancement is also evident on dynamic contrast-enhanced perfusion-weighted imaging (from h to k). PI-RADSv2 diagnostic category: 5

Fig. 2

Radiomic workflow pipeline for both machine learning and deep learning approaches for prostate magnetic resonance imaging. See the text for details