The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

Abstract

The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

“…Given the recent approval and addition of the PSMA PET/CT scan to the PC clinical care pathway, the progression to PSMA PET/CT-derived radiomics in risk stratification and personalized management of PC is a logical and potentially transformative advancement. Much of the groundwork for radiomic-related machine learning models has been established with years of investigation via multiparametric MRI imaging [1,2,24,36] and, given this, it is unsurprising that all the articles reviewed herein were accomplished within the last three years following FDA approval of the 68-Ga-PSMA PET/CT in PC patients [4]. Keeping in mind that image interpretation and segmentation is limited by interobserver variability, the use of radiomic models has the potential to enhance diagnostic performance.…”

“…A clear focus of PSMA PET/CT-derived radiomics has been in prediction of surgical pathology [6,[26][27][28][29][30][31], treatment outcomes [2,3,7,12,30,[32][33][34][35], progression, or survival [32,34,35]. This is in stark contrast to several recent reviews of mpMRI-derived radiomic models [1,2,36], which have concentrated on the initial diagnosis and staging of PC. While this is perhaps partially due to differences in indication between mpMRI imaging versus PSMA PET/CT scans, it may also be indicative of anticipated clinical utility and potential integration into patient management.…”

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

“…Hence, there are more studies on MRI radiomics than CT for PCa identification and prediction [10,[17][18][19][20][21][22][36][37][38]. However, a review on MRI radiomics for PCa risk stratification published in 2023 showed that only three studies used MRI to predict biochemical failure after receiving RT, with two reporting the AUC values of their models [55][56][57][58]. In Dinis Fernandes et al's study, their model achieved an AUC value of 0.63 [57].…”