Integrating MRI-based tumour staging within the TNM classification system in modern prostate cancer management

MRI has transformed prostate cancer diagnosis, risk stratification, and treatment planning. However, the tumour-node-metastasis (TNM) classification continues to rely exclusively on digital rectal examination (DRE) for clinical T-staging, despite DRE’s limited diagnostic accuracy and prognostic performance. This mismatch between modern MRI practice and outdated staging criteria undermines prognostication and treatment decision-making. In this review, we synthesise current evidence on DRE- and MRI-based local staging, discuss the limitations of the current TNM framework, and present a proposal for parallel reporting of MRI-based T-staging (mrT) alongside clinical T-staging (cT). We further outline the rationale for developing MRI-derived prognostic groups, benchmarked against pathology and long-term oncologic outcomes, and present a framework intended to support future evidence-based revisions of the official TNM classification. Critical relevance statement Clinical adoption of prostate MRI, TNM T-staging still relies on digital rectal examination, a method with limited sensitivity and prognostic value. This discordance leads to stage misclassification and suboptimal risk stratification. Parallel reporting of MRI-based T-staging and development of MRI-derived prognostic groups are essential to align staging with contemporary imaging practice, support evidence-based treatment decisions, and inform future revisions of the TNM classification system. Key Points Digital rectal examination (DRE) is inferior to MRI for detecting extraprostatic extension. MRI-based and DRE-based T-staging should be reported separately to evaluate stage migration, as MRI-based T-staging may shift patients into higher stages without reflecting true biological risk. Parallel reporting of MRI-based T-staging and development of MRI-derived prognostic groups are essential to align staging with contemporary imaging practice, support evidence-based treatment decisions, and inform future revisions of the TNM classification system.