AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.

Name: Αlpha¹ Rigor Record — AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.
Item: AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.
Rating: 96
Author: Αlpha¹

Salim M, Liu Y, Sorkhei M, Ntoula D, Foukakis T, Fredriksson I, Wang Y, Eklund M, Azizpour H, Smith K, Strand F

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Αlpha¹ Rigor Review — 2.75★ (Weak), 0 critical, 4 high issues for “AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.”

Links back to this Rigor Record.

Αlpha¹ Editorial

This rigor review was examined and confirmed by Αlpha¹ Editorial · August 16, 2026

Αlpha¹ Rigor Review

Rigor Reviewed

2.75 / 5

Weak — serious problems found

Interventional Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern ×2−1★
ClaimsEfficacy rests on an unvalidated surrogate endpoint−0.5★
ClaimsTreatment effect not shown to be clinically meaningful−0.5★
ReportingData & code availability partially met−0.25★

Model-reported confidence: highReviewer agreement: highEngine: 7.39.0Source: full text

Problems surfaced5 types

Numerical inconsistencies1

Values that contradict each other or are impossible for the stated sample: recomputed p-values and test statistics, GRIM/GRIMMER checks on summary numbers, percentages against their own counts, totals against their parts, and estimates against their own confidence intervals.

Internal contradictions in the reported numbersAssessed

Overstated conclusions3

Conclusions that reach past what the paper's own results support — including a significance claim that no longer holds when the statistic is recomputed, and efficacy resting on an unvalidated surrogate endpoint.

Efficacy rests on an unvalidated surrogate endpointAssessed
Treatment effect not shown to be clinically meaningfulAssessed
Conclusions only partially backed by the presented evidenceAssessed

Reporting gaps1

Required detail the manuscript never states — study design, biological variables, ethics approval and consent, key resources, statistical reporting, data and code availability, and overall transparency.

Data/code availability incompleteAssessed

This Αlpha¹ Rigor Review uses AI Rigor Reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

This is a well-conducted and well-reported randomized controlled trial evaluating an AI-based selection tool for supplemental MRI in breast cancer screening. The paper has strong scientific premise, rigorous design, clear ethics reporting, and detailed methods. The main weaknesses are a vague data availability statement and minor reporting gaps (no explicit power analysis, no explicit reporting guideline, some p-values as thresholds).

Both reviewers classified the study as interventional (RCT), and this was adopted. The evaluation covered all eight dimensions; several sub-criteria were marked not applicable (e.g., blinding, power analysis for secondary endpoint, species/housing, antibodies/cell lines). The statistics verification recomputed only 3 tests (all consistent); the rest of the statistical results were not machine-verified and should not be assumed correct. The citation check found no retracted or non-existent references.

9 major claims checked against the paper's own evidence: 2 only partially supported (evidence backs part of the claim; gaps or caveats remain); the rest adequately supported.

Premise concern: surrogate not validated for clinical benefit; effect size not shown to be clinically meaningful.

Recomputed 3 tests: 3 consistent, 0 inconsistent; 3 via agent-written checks.

2 integrity concerns flagged (0 high).

Reporting

Eight Dimensions7 pass · 1 warn

The introduction cites multiple studies on interval cancers, MRI sensitivity in dense breasts, and the DENSE trial, establishing the problem of missed cancers and the potential of MRI. It acknowledges limitations of prior work (e.g., high cost of MRI, lack of qualified staff) and presents the AI tool as a solution to improve cost-effectiveness. The hypothesis that AI-based selection will be more effective than traditional density measures is clearly stated.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 95% conf

The paper establishes a clear scientific premise by citing prior work on interval cancers, MRI sensitivity, and the DENSE trial, and provides a logical rationale for using AI to select high-risk individuals for supplemental MRI.

Evidence

paraphrase[Introduction, paragraphs 1-3]

“Around 30 percent of cancers in screened women are diagnosed as so-called interval cancers... Contrast-enhanced MRI has been shown to be a more sensitive method for early detection of breast cancer compared with mammography.”

direct quote[Introduction, paragraph 1]

“However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited.”