Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Abstract

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

“…Common to many studies, frequent radiomic features used in GS predictions were based on texture (e.g., histogram, GLCM, NGTDM, and GLSZM), shape/morphological (e.g., volume and surface), and clinical markers (e.g., age and treatment modality). This is consistent with a recent survey that reports a median AUC value of 79% (IQR—interquartile range: 0.77–0.87) for PCa classifications [ 87 ].…”

“…MRI radiomics have demonstrated the potential to discern the PCa grade [ 23 , 24 , 86 , 87 , 88 ] or guide management approaches [ 45 , 89 ] from the abundance of clinical data acquired at each scan. However, reproducibility is a significant issue at different stages of the radiomics pipeline, with few studies investigating this question [ 41 , 78 ].…”

A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade

“…By extracting multiple imaging features, radiomics has the potential to evaluate the mpMRI data in a more objective way. In the context of PCa, the literature has shown evidence of the potential of radiomics in classifying PCa lesions [ 5 , 6 , 7 , 8 ], with promising performances in terms of sensitivity and specificity [ 9 ]. Nevertheless, current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice [ 9 , 10 ].…”

“…In the context of PCa, the literature has shown evidence of the potential of radiomics in classifying PCa lesions [ 5 , 6 , 7 , 8 ], with promising performances in terms of sensitivity and specificity [ 9 ]. Nevertheless, current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice [ 9 , 10 ]. This is due to the fact that most of the radiomics studies validated their approach by splitting their original dataset in training and validation subsets, while only a few studies performed a validation using an external set [ 11 , 12 , 13 ].…”

“…To our knowledge, few studies tested their model’s generalizability for PCa detection regarding tumor aggressiveness using multiple scanners [ 17 , 18 , 19 ]. Only a few studies have validated their methods using external datasets for PCa tumor grade prediction [ 9 ]. When radiomics models are being considered as decision support tools for clinical practice, the generalizability issue should be addressed.…”