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.

By Can, Tran, Nguyen +6Nov 2, 2026

Score██████░░░░6.0

VerdictWorth a reader's time today.

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Abstract

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.

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

The editor's rubric

Heuristic review

Leverage4 / 5: A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).; 24% of this desk’s rubric
Magnitude2 / 5: Solid incremental gain on a meaningful problem.; 16% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 20% of this desk’s rubric
Novelty3 / 5: A genuinely new approach to an open problem.; 20% of this desk’s rubric
Trajectory3 / 5: A clear path to scale.; 10% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	████░4/5	24%	A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
Magnitude	██░░░2/5	16%	Solid incremental gain on a meaningful problem.
Evidence	███░░3/5	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	███░░3/5	20%	A genuinely new approach to an open problem.
Trajectory	███░░3/5	10%	A clear path to scale.
Stakes	██░░░2/5	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); breadth (general-purpose, many tasks); verification (multiple benchmarks, independent replication).

How the score was computed

rank-2026-10-18

Score██████░░░░6.0

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 6.0 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 6.0 / 10; Shrunk toward the desk prior by editor confidence (40%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 0%; Half-life decay since publication.

No attention signals recorded yet.

The record

Reviewed by heuristic-v10 on Nov 3, 2026, 05:48 UTC. Paper type: method.
Categories: cs.LG, q-bio.QM, stat.ME
BRIEF, No.2 in the Biology edition of November 3, 2026.