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

Abstract

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

“…We have attempted to provide an overview of the current state-of-the-art of AI applications for prostate MRI. Unlike other review papers [ 16 , 42 , 62 ] that focus on AI tools that have been developed, this review focuses on open datasets, commercially/publicly available AI, and grand challenges. However, since this is a rapidly growing field, a limitation of this review is that it will become outdated in a relatively short period of time, just like the review papers before it.…”

“…Furthermore, models require diverse, multicenter, multivendor data to achieve robust performance and generalization. However, most algorithms reported in literature thus far, use relatively small, single-center datasets [ 16 ]. The limited number and quality of publicly available datasets for prostate MRI, further aggravates this issue.…”

“…The datasets have been used extensively by researchers for the development of clinical applications, including segmentation of prostate tissue and detection/diagnosis of csPCa [ 16 , 42 ]. For segmentation-related applications, the PROMISE12 [ 22 ] and MSD [ 35 ] datasets were most commonly used for segmentation of the whole prostate gland and prostate zones, respectively ( e.g.…”

“…Many tools have been made available in the form of publications, codes, software plugins, or grand challenge algorithms for research or non-clinical purposes. A number of these tools have already been presented in other review articles [ 16 , 42 , 62 , 63 ]. However, access to these trained models is not always possible, and when it is possible, it is usually not easy for end users or researchers to implement.…”

Radiomics in prostate cancer: an up-to-date review

“…The CAD tools commonly used to classify resampled instances to analyze medical images from pictures databases are RF, SVM, 170 and ML algorithms. 171 , 172 Other classifiers used are naive Bayesian classifier (NBC) and k -nearest neighbor (KNN). 44 Lately, most of the AI methods used are convolutional neural networks(CNNs), because they have the potential to extract complex variables.…”