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ANALYSIS REPORTFictional sample05.07.2026

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

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Key Points

  • 1Single-centre Cambridge cohort of 370 unscreened prostate cancer patients evaluating MRI integration into pre-treatment risk stratification; clinically meaningful reclassification rate shown, but retrospective MRI ascertainment, absent PI-RADS scoring, and limited model calibration reporting weaken the prognostic contribution.

Major Issues

Methods: MRI reporting was retrospective without standardised PI-RADS version 2.1 scoring across the study period; heterogeneous reporting criteria from different radiologists over multiple years introduce significant measurement variability in the MRI-derived predictors.
Results: Prognostic model performance improvement is reported with discrimination statistics (AUC) but calibration statistics (Hosmer–Lemeshow test, calibration slope, calibration plots) are absent; a model with improved discrimination may still be poorly calibrated for individual risk prediction.
Methods / Results: Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) — the standard statistics for prognostic reclassification studies — are not reported; the clinical utility of the 6.2% reclassification rate cannot be formally evaluated without these metrics.
Design: Single-centre design at a tertiary academic centre; patients referred to this level of care are likely more complex and higher-risk than the general unscreened population, limiting generalisability to district general hospital prostate cancer pathways.

Priority Action Plan

HIGH IMPACT

Problem

Report NRI, IDI, and calibration statistics (calibration slope + Hosmer–Lemeshow) for the MRI-enhanced prognostic model comparison.

Why it matters

These are the standard statistical metrics for prognostic reclassification studies; without them, the 6.2% reclassification claim cannot be formally evaluated for clinical utility.

Suggested fix

Report NRI, IDI, and calibration statistics (calibration slope + Hosmer–Lemeshow) for the MRI-enhanced prognostic model comparison.

HIGH IMPACT

Problem

Standardise MRI reporting documentation and describe PI-RADS version; report inter-reader agreement for MRI-derived variables.

Why it matters

Non-standardised MRI reporting is the primary threat to internal validity; documenting agreement statistics at least partially addresses this limitation.

Suggested fix

Standardise MRI reporting documentation and describe PI-RADS version; report inter-reader agreement for MRI-derived variables.

MEDIUM IMPACT

Problem

Conduct internal validation via bootstrap or cross-validation to correct for optimism in prognostic model performance statistics.

Why it matters

Single-centre model performance is invariably optimistic without internal validation; presenting uncorrected AUC as evidence of model improvement will be challenged by reviewers.

Suggested fix

Conduct internal validation via bootstrap or cross-validation to correct for optimism in prognostic model performance statistics.

Quick win: The central finding (MRI reclassifies 6.2% of unscreened prostate cancer patients to higher risk) is clinically important and novel. Strengthen the statistical reporting with NRI/IDI, calibration statistics, and internal model validation. Standardise the MRI reporting documentation. These additions will substantially improve the paper's credibility for radiology and urology journal reviewers.

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