radiology-reporting

Radiology Reporting-Guideline Compliance

Use this skill to make an imaging study reviewer-proof on reporting. Radiology and the RSNA family require the relevant EQUATOR checklist at submission, and imaging-AI / radiomics papers are now judged against a specific, version-sensitive stack of guidelines. This skill (1) identifies the study type, (2) selects the correct guideline(s), (3) audits the manuscript item-by-item, and (4) returns a submission-ready checklist plus a prioritised fix list.

Core stance

• The checklist is the contract. A reviewer maps your paper to a guideline; do the same first, in their seat.
• Report honestly. Mark each item PRESENT, PARTIAL, or MISSING. Never label something compliant to be agreeable. A MISSING flag you surface is cheaper than a reviewer finding it.
• Cite the location. Every PRESENT claim must point to a section / page / figure / supplement. If you cannot point to it, it is PARTIAL at best.
• Versions matter. Use the current version (CLAIM 2024 Update, TRIPOD**+AI** 2024, CLEAR 2023, METRICS 2024, STARD-AI 2025 for AI accuracy studies, CONSORT/SPIRIT 2025, PROBAST**+AI** 2025 — full list with sources in references/guideline-versions.md). Name the version you audited against.
• Provenance matters. Record the official guideline/checklist source, version/update date, access date, and any target-journal overlay. Treat remembered requirements as provisional.
• Reporting ≠ quality ≠ risk-of-bias. CLEAR (reporting) → METRICS / RQS (methodological quality) → PROBAST+AI / QUADAS-3 v1.2 (risk of bias). Different tools, different jobs; pick the right one(s).
• Don't invent the science. This skill audits reporting; it never fabricates the missing experiment, metric, or dataset. It tells the author what to add.
• Venue changes the stack, not the rigor. Radiology-family submissions stop at the guideline checklist; Nature-portfolio submissions add a Reporting Summary / Editorial Policy Checklist on top of the same guideline stack (→ nature-reporting-summary.md) — never treat the Reporting Summary as a replacement for CLAIM/TRIPOD+AI/CLEAR.

When to use

• "Which checklist does my study need?" / "What will Radiology require at submission?"
• "Audit this manuscript against CLAIM / TRIPOD+AI / STARD / CLEAR / METRICS / RQS."
• "Is my radiomics pipeline reported reproducibly (IBSI)?"
• "Fill in the CLAIM checklist with page numbers."
• "What's my risk-of-bias exposure under PROBAST+AI / QUADAS-3?"
• "Which checklist applies to a guideline/consensus, qualitative/mixed-methods, economic evaluation, implementation study or PPI report?"
• Pre-submission self-audit, or triaging a reviewer comment that cites a guideline.

Routing — pick the guideline(s) before auditing

Most imaging-AI papers need two or more of these (a reporting guideline and a quality/risk-of-bias tool).

Study type	Primary reporting guideline	Add for quality / risk-of-bias
AI/ML system in medical imaging (any task)	CLAIM 2024	TRIPOD+AI if it is a prediction model; DECIDE-AI for early clinical decision-support
Diagnostic/prognostic prediction model (incl. ML/DL)	TRIPOD+AI (2024) (+ TRIPOD-Cluster, TRIPOD+AI for Abstracts)	PROBAST / PROBAST+AI (2025) (risk of bias)
Radiomics (hand-crafted features → model)	CLEAR (2023) for reporting	METRICS (2024) and/or RQS / RQS 2.0 for quality; IBSI for feature reproducibility
Diagnostic accuracy (test vs reference standard)	STARD 2015; STARD-AI (2025) when the index test is AI	QUADAS-3 v1.2 when primary-study accuracy estimates are appraised in a review
DTA systematic review / meta-analysis	PRISMA-DTA (2018)	QUADAS-3 v1.2 per selected accuracy estimate; for within-study comparisons of two or more index tests, apply QUADAS-C alongside QUADAS-3 using the QUADAS-3 E&E adaptation guidance
Systematic review / meta-analysis (general)	PRISMA 2020	AMSTAR-2; ROBIS
Scoping review / evidence map	PRISMA-ScR (2018)	critical appraisal only if performed and justified; AMSTAR-2/ROBIS do not replace the reporting checklist
Systematic-review or meta-analysis protocol	PRISMA-P (2015)	protocol reporting only; registration/freeze and review conduct remain with radiology-systematic-review
Living systematic review	PRISMA 2020 + PRISMA-LSR (2024)	update surveillance, changed-study ledger, transition/retirement and tailored flow
Review of outcome measurement instruments	PRISMA-COSMIN for OMIs (2024)	only when measurement properties/instruments are the review object; use design-matched COSMIN appraisal
Observational (cohort/case-control/cross-sectional)	STROBE	(REMARK for tumour-marker prognostic studies)
Randomised trial of an imaging/AI intervention	CONSORT 2025 (+ CONSORT-AI 2020 extension)	protocol: SPIRIT 2025 (+ SPIRIT-AI 2020)
Imaging biomarker / quantitative imaging	QIBA Profile reporting + STARD/TRIPOD as applicable	IBSI; phantom/repeatability (QIBA)
Practice guideline / biomedical consensus	RIGHT 2017 / ACCORD 2024	AGREE II appraises guideline quality; recommendation methods stay with radiology-consensus-guideline
Qualitative / mixed methods	COREQ for interviews/focus groups or SRQR for broader qualitative work; add quantitative design guideline and integration reporting	method credibility/integration stay with radiology-qualitative-mixed-methods
Health economic evaluation	CHEERS 2022 (+ current CHEERS-AI when applicable)	economic estimand/model/uncertainty stay with radiology-health-economics
Implementation study	StaRI 2017 + design-specific guideline(s)	implementation determinants, strategies/outcomes and success stay with radiology-translation
Patient/public involvement report	GRIPP2 2017	actual involvement/decision impact stay with radiology-design

Open references/guideline-router.md for the full decision tree, including hybrid studies (e.g. a radiomics prediction model validated for diagnostic accuracy → CLEAR + TRIPOD+AI + STARD + IBSI) and Nature-portfolio venues (add the Reporting Summary on top of whichever stack applies).

When to open extra files

File	Open when
references/guideline-router.md	Choosing guideline(s); hybrid/edge-case study types; how guidelines stack; Nature-portfolio add-on; FUTURE-AI; TRIPOD-LLM
references/claim-2024.md	Auditing a medical-imaging AI paper against the CLAIM 2024 Update
references/tripod-ai-probast.md	Prediction-model reporting (TRIPOD+AI) and PROBAST / PROBAST+AI risk-of-bias
references/clear-metrics-rqs.md	Radiomics reporting (CLEAR) and quality scoring (METRICS, RQS / RQS 2.0)
references/ibsi-features.md	Making radiomic features reproducible/standardised (IBSI image processing + feature nomenclature)
references/stard-prisma-quadas.md	Diagnostic-accuracy reporting (STARD), DTA reviews (PRISMA-DTA), and current risk-of-bias/applicability assessment (QUADAS-3 v1.2; QUADAS-C alongside it for comparative accuracy)
references/prisma-general-scoping.md	General reviews (PRISMA 2020), protocols (PRISMA-P), scoping reviews/evidence maps (PRISMA-ScR), search reporting (PRISMA-S), living reviews (PRISMA-LSR), and measurement-instrument reviews (PRISMA-COSMIN for OMIs)
references/radiology-submission-map.md	Mapping checklist items to where they belong in a Radiology manuscript + submission logistics
references/nature-reporting-summary.md	Target is a Nature-portfolio journal — completing the Reporting Summary / Editorial Policy Checklist alongside the primary guideline stack
references/guideline-consensus-qualitative-economic-implementation.md	Guideline/consensus, qualitative/mixed-methods, health-economics, implementation or PPI reporting; distinguishes reporting from method/appraisal/success

Workflow

1. Classify the study. Determine task (classification / detection / segmentation / prediction / diagnostic accuracy / discovery), data provenance, whether a model is developed and/or validated, whether the endpoint is accuracy, prognosis, biology, implementation or economics, and whether the output is a guideline/consensus or qualitative/mixed-method report.
2. Select and verify guideline(s) from the routing table. State the official source, version/update date, and access date. If the study is hybrid, select the stack and say why each applies.
3. Load the relevant reference file(s) and audit every item. For each item record: Item ID | Requirement (short) | Status (PRESENT/PARTIAL/MISSING/NA) | Location | Fix | Owner | Closure evidence.
4. Prioritise fixes. Group into Blocker (will trigger major revision / desk reject), Should-fix (reviewer will likely ask), Polish. Tie each blocker to the specific reviewer risk.
5. Cross-check integrity hot-spots (see below) — the items reviewers weaponise most.
6. If the target is a Nature-portfolio venue, also complete the Reporting Summary / Editorial Policy Checklist (nature-reporting-summary.md) — additive, not a substitute.
7. Return the filled checklist + a one-screen executive summary + the prioritised fix list. Offer to draft the missing text/Methods sentences (hand off to radiology-writing).

Integrity hot-spots (audit these even if not asked)

These are the recurring reasons imaging-AI/radiomics papers get rejected:

• Data leakage / partition hygiene. Train/validation/test split made at the patient level (not slice/lesion); no test-set tuning; preprocessing, feature selection, harmonisation, and normalisation fit on training data only; augmentation never crosses the split. (CLAIM, TRIPOD+AI, METRICS, CLEAR all probe this.)
• External / independent validation. Internal CV alone is weak. State the validation type (internal resampling, temporal, geographic, fully external) and cohort source.
• Reference standard & ground truth. Who labelled, how many readers, expertise, blinding, adjudication, and the reference standard's own accuracy. (STARD, CLAIM.)
• Class/prevalence & spectrum. Report disease prevalence; flag artificial 1:1 sampling; describe the clinical spectrum (STARD spectrum bias; QUADAS-3 Participants domain).
• Radiomics reproducibility. Software + version, image preprocessing (resampling, discretisation/bin width, intensity normalisation), segmentation method and inter-observer reproducibility (ICC), feature definitions IBSI-compliant, and scanner/protocol harmonisation (e.g. ComBat). (CLEAR, METRICS, IBSI.)
• Sample size / EPV. Events-per-variable, or a stated sample-size rationale (Riley et al. for prediction models). High-dimensional features vs. n is the classic overfitting trap.
• Metrics match the task & prevalence. AUC alone is insufficient; report calibration and clinical-utility (decision-curve) for prediction models; report CIs everywhere. (Hand off computation to radiology-stats.)
• Code / model / data availability. Statement present and specific. (Hand off to radiology-data.)

Output contract

Return, in this order:

1. Study classification — task, design, endpoint, and the selected guideline stack (with versions).
2. Guideline provenance — official source/checklist, version/update date, access date, target-journal overlay, and any item marked VERIFY_FROM_CURRENT_SOURCE.
3. Checklist — a table with Item | Status | Location | Fix | Owner | Closure evidence for every item of each selected guideline. Use NA only with a one-line justification. An owner is accountable for supplying the named closure evidence; assignment alone never closes an item.
4. Compliance summary — counts (PRESENT / PARTIAL / MISSING / NA) per guideline and an overall readiness read (e.g. "CLAIM 31/44 present; 4 blockers").
5. Prioritised fixes — Blocker / Should-fix / Polish, each tied to the reviewer risk and the manuscript location to edit.
6. Author input needed — questions only the authors can answer (e.g. "Was the test set sampled at patient level?").

If the user pastes only part of a manuscript, audit what is present and mark the rest Cannot assess — section not provided rather than guessing.

Quality bar

A good audit reads like a rigorous methods reviewer who is on the author's side: it finds the holes before submission, points to the exact item and location, and hands back the precise sentence the Methods needs — without ever inventing compliance the paper doesn't have.

Handoffs

• Missing statistics → radiology-stats (compute/report AUC CIs, DeLong, ICC, calibration, DCA).
• Missing Methods/Results prose → radiology-writing.
• Data/code availability wording, DICOM de-identification, Extended Data/Source Data → radiology-data.
• Radiogenomics-specific design/leakage → radiology-radiogenomics.
• Figure that proves an item (ROC, calibration, flow diagram) → radiology-figure.
• Table that proves an item (cohort, regression, performance, feature audit) → radiology-table.
• Explainability/uncertainty items for a DL model → radiology-deep-learning/interpretability-uncertainty.md.
• Checklist complete; want a full adversarial pre-submission read → radiology-prereview.
• Guideline/consensus recommendation development → radiology-consensus-guideline; qualitative/ mixed-method execution → radiology-qualitative-mixed-methods; economic model/HTA plan → radiology-health-economics; implementation evaluation → radiology-translation.
• Final upload package/checklist location audit → radiology-submission / radiology-pipeline.