A Scientific Claim Stability Framework for Evaluating MRI Morphometry Pipelines
Authors: Duy-Cat Can, Dang-Duat Tran, Anh-Khang Nguyen, Quoc-Bao Tran, Trung-Hieu Do, Van-Tu Nguyen, Duy-Thanh Vu, Oliver Y. Chén, Hoang-Quynh Le
Organizations: Centre for Neural Artificial Intelligence, Lausanne University Hospital, Lausanne, Switzerland · Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland · VNU University of Engineering and Technology, Hanoi, Vietnam · Hanoi Medical University, Hanoi, Vietnam · National Geriatric Hospital, Hanoi, Vietnam · Institute of Diagnostic and Interventional Radiology, Bach Mai Hospital, Hanoi, Vietnam
MRI morphometry pipelines are often evaluated by measurement agreement or downstream accuracy, yet their scientific value depends on the conclusions they support. Two pipelines can produce similar feature-level results yet support different conclusions about aging, cognitive decline, or neurodegeneration. We introduce Scientific Claim Stability (SCS), a claim-centric evaluation framework that converts region-of-interest (ROI) morphometry tables into auditable claim cards and evaluates their support under changes in dataset, processing pipeline, atlas, feature family, and target. Across 10,370 MRI scans from three datasets and five cohorts, SCS generates 2,783 claim cards and evaluates 89 claim-family pairs. The evaluation distinguishes stable, transferable, and fragile claims. FreeSurfer 8-FastSurfer comparisons show high evidence concordance and low anatomical-support overlap under the primary covariate specification. Acquisition-adjusted analyses show that the magnitude of support disagreement depends on the model specification. Structured review by specialists in geriatrics, neuroscience, and radiology, alongside auxiliary ChatGPT and Gemini reviewers, assesses claim plausibility, clarity, faithfulness, usefulness, and caution. SCS provides a reproducible way to audit the evidential stability of morphometry claims without assuming a universal gold standard.