A Scientific Claim Stability Framework for Evaluating MRI Morphometry Pipelines

MRI morphometry pipelines are often evaluated by measurement agreement or downstream accuracy, yet their scientific value depends on the conclusions they support. This creates a gap: two pipelines may look similar at the feature level while supporting different conclusions about aging, cognitive decline, or neurodegeneration. To bridge this disparity, here we propose Scientific Claim Stability (SCS), a reusable claim-centric evaluation framework that converts region-of-interest (ROI) morphometry tables into interpretable claim cards and tests whether each claim remains supported under realistic perturbations. To demonstrate the efficacy of SCS, we evaluate it on 10,370 MRI scans from three datasets and five cohorts, generating 2,783 claim cards and evaluating 89 claim-family pairs across dataset, pipeline, atlas, feature-family, and target shifts. SCS identifies claims that are stable, transferable, or fragile, and reveals high-concordance fragile-support cases where evidence vectors remain similar while supporting regions change. To assess claim plausibility, clarity, faithfulness, usefulness, and caution level, we further implement and compare an expert (including geriatrics, neuroscience, and radiology) review workflow and an LLM (including ChatGPT and Gemini) review workflow. Our results suggest claim-level evaluation as a practical standard for auditing MRI morphometry pipelines without requiring a universal gold standard.