Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population

Journal article

Purpose of review: To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value. Recent findings: There are many new biomarker-based models seeking to improve, optimise or replace clinical models. There is promising data on the value of MRI, radiomics, genomic classifiers and most recently artificial intelligence tools in refining stratification. Despite the extensive literature however, there remains uncertainty on where in pathways they can provide the most benefit and whether a biomarker is most useful for prognosis or predictive use. Comparisons studies have also often overlooked the fact that clinical models have themselves evolved and the context of the baseline used in studies that have shown superiority have to be considered. Summary: For new biomarkers to be included in stratification models, well designed prospective clinical trials are needed. Until then, there needs to be caution in interpretation of their use for day to day decision making. It is critical that users balance any purported incremental value against the performance of the latest clinical classification and multivariate models especially as the latter are cost free and widely available

Authors: Lophatananon, A; Byrne, MHV; Barrett, T; Warren, A; Muir, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Cancer
• Volume: 22
• Issue: 1
• Pages: 878-878
• Article number: 878
• Publication date: 2022-08-11
• DOI: 10.1186/s12885-022-09955-w
• EISSN: 1471-2407
• ISSN: 1471-2407
• Language: English
• Keywords: Medicine; Concordance; Cohort; Population; Surgical oncology; Prostate; Prostate cancer; Gynecology; Oncology; Cancer; Internal medicine