radiology-design

Imaging Study Design & Feasibility

Use this skill at the front of the research chain: someone has imaging data (and maybe clinical/pathology/molecular labels) but no settled study. It (1) triages scientific feasibility, (2) keeps governance and data-access readiness as a separate verified state, and (3) converts a feasible idea into a claim-matched design: clinical question, population, endpoint, methods (minimum viable → stronger), and validation appropriate to the intended use.

Core stance

• Clinical question first, model second. A study is defined by the question and the decision it informs, not by the algorithm. "Build a model" is not a study.
• Match data to task, honestly. The same images support very different ceilings. Disease, modality, n, number of centers, label source, event count, and follow-up determine whether the realistic target is diagnosis, subtyping, staging, prognosis, treatment-response, recurrence, or segmentation — or only a feasibility study.
• Validation follows the claim and intended use. State the validation type explicitly and design it before modelling. Internal resampling may be sufficient for bounded feasibility or model-development claims; transport, temporal, geographic, or external claims require the corresponding untouched evaluation population.
• Surface the binding constraint. Almost every imaging study is limited by one number (matched n, event count, external-cohort size, or labelled cases). Name it up front; the design must respect it.
• Separate confirmation from exploration. Lock confirmatory endpoints, analyses, and split rules before inspecting their results. Exploratory analyses may generate hypotheses, but must be labelled, multiplicity-aware, and independently confirmed before confirmatory wording.
• Registration routes are not interchangeable. Trial registration, preregistration, protocol publication and Registered Reports answer different questions. Verify the target journal before claiming a Stage 1/IPA route.
• Design with affected people, not just data about them. Distinguish PPI from participation and engagement; record who shaped the question, burden, harms, thresholds and dissemination, and how their input changed the protocol. Do not invent involvement.
• Integrity. Never invent cohort numbers, event counts, or center counts; never claim a capability the data cannot support. If the honest answer is "not yet — do X first," say so.

When to use

• "I have [N] cases of [disease] [modality] — what can I actually study?" / "这批数据能不能做研究？"
• "Turn my data into a complete, submittable project." / "帮我把现有数据设计成一个完整课题。"
• "Is my data enough for diagnosis / prognosis / treatment-response / segmentation?"
• "Design a multi-center / external-validation / temporal-validation plan." / "多中心外部验证怎么设计？"
• "How do I show generalisability across scanners/hospitals?" / center, scanner, batch effects.
• Choosing between radiomics, deep learning, multimodal fusion, radiogenomics, or feasibility-first.
• "Can this be submitted as a Registered Report?" / "How should patients, technologists and radiologists shape this imaging study and its equity estimands?"

When to open extra files

File	Open when
references/feasibility-triage.md	Deciding if the data can support a study at all; what's the realistic task ceiling; what's missing
references/study-blueprints.md	Picking a design template (diagnostic accuracy, prediction/prognosis, treatment-response, segmentation, radiogenomics, reader study) and its minimum-viable vs stronger version
references/validation-strategy.md	Designing internal/temporal/geographic/external/multi-center/federated validation; center & scanner effects; what counts as "external"
references/endpoints-and-estimands.md	Choosing the clinical question, target population, endpoint, comparator, and clinical-use scenario
references/task-contract-router.md	Freezing a task-specific contract for detection/localization, segmentation/quantification, diagnosis/triage, prognosis, longitudinal response, reconstruction/enhancement, synthetic imaging or report/VLM studies
references/primary-imaging-study-protocol-and-preregistration.md	Building the primary-study protocol, SAP, preregistration/deviation and open-science lock before outcome or protected-test access
templates/imaging-study-protocol.md	A durable primary imaging-study protocol artifact is authorized
templates/imaging-statistical-analysis-plan.md	A durable imaging SAP artifact is authorized after estimand/unit/outcome locks are known
references/registered-reports-ppi-and-equity.md	Distinguishing trial registration/preregistration/Registered Reports, verifying a Stage 1 route, or recording patient/public/stakeholder impact and equity/access decisions
templates/ppi-equity-decision-ledger.md	A durable PPI/stakeholder/equity decision artifact is requested
references/modeling-route-decision.md	Choosing the modelling route (hand-crafted radiomics vs deep features vs end-to-end DL vs foundation adaptation) from n, events, annotation cost, compute, and timeline
references/weak-result-pivot.md	The headline result is weak (e.g. AUC ~0.7) or the study "has no story" — picking one defensible exit
references/ai-radiogenomics-12-24-roadmap.md	The user wants a 12-24 month plan for radiology AI/deep radiomics/radiogenomics, or asks how to turn data into a staged publication and translation program

Workflow

1. Inventory the data and authority state. Disease, modality(ies), n (patients and lesions), centers/scanners, label source and quality, masks, clinical variables, follow-up/events, pathology/molecular labels and time span. Separately record applicable ethics/consent or waiver, secondary-use authority, data/tissue access and sharing constraints. Mark every unknown; do not infer approval or access from the existence of data.
2. Define the provisional question (endpoints-and-estimands.md). Clinical question → target population → primary endpoint/estimand → comparator → intended clinical-use scenario. When organ/disease pathway, reference-standard, treatment-timeline, response/staging framework or acquisition-artifact context can change that definition, obtain a versioned clinical-context packet from radiology-clinical-domain before grading label quality or feasibility.
3. Run the participation/equity design gate when applicable. Open registered-reports-ppi-and-equity.md; separate involvement, participation and engagement, then record representation, decision authority, imaging burden/harms, access, equity estimands and protocol changes. Lack of PPI is reported honestly, not reconstructed after the fact.
4. Feasibility triage (feasibility-triage.md). Judge the reference standard against that specific target construct, then decide the realistic task ceiling and flag scientific showstoppers (unfit reference standard, inadequate events, leakage-prone structure, or validation that cannot support the proposed claim). Report two axes: SCIENTIFICALLY_FEASIBLE / FEASIBLE_WITH_CHANGES / FEASIBILITY_ONLY / NOT_SCIENTIFICALLY_FEASIBLE and GOVERNANCE_VERIFIED / GOVERNANCE_PENDING / GOVERNANCE_BLOCKED. A scientifically feasible project is not submittable while governance or access remains pending.
5. Freeze the task contract (task-contract-router.md), including target object, independent unit, reference standard, sampling, primary metric/CI, matching/aggregation, failure handling, workflow consequence, transport shift and claim ceiling.
6. Pick the blueprint (study-blueprints.md). Choose the design template and give a minimum-viable version (what's publishable now) and a stronger version (what would reach a higher tier), with the extra cost of each.
7. For program-level AI/radiogenomics planning, open ai-radiogenomics-12-24-roadmap.md and place the project on the staged route from cohort lock to baselines, fusion, external validation, and silent/reader/prospective evidence.
8. Design the validation (validation-strategy.md). Specify the split (patient-level), internal scheme, and the external/temporal/geographic/multi-center plan. State what is held out and what "external" honestly means here.
9. Define falsification and controls — counter-hypothesis, negative/control analysis, expected failure signal, and the sensitivity analysis that distinguishes artifact from signal.
10. Write the protocol/SAP lock (primary-imaging-study-protocol-and-preregistration.md), then name the binding constraint and sample-size/power/precision question (hand the numbers to radiology-stats).
11. Select the registration/publication route. Distinguish trial registration, preregistration, protocol publication and Registered Reports. For a Registered Report, verify current venue support and freeze Stage 1/IPA commitments before outcome/protected-test access.
12. Return the blueprint + feasibility verdict + validation plan + the prioritised list of what to secure next.

Output contract

1. Feasibility verdict — separate scientific-feasibility and governance-readiness states, each with its evidence or unresolved input.
2. Data read — the inventory, with the binding constraint surfaced and unknowns listed.
3. Study blueprint — clinical question, population, primary endpoint/estimand, comparator, clinical-use scenario; design type.
4. Task contract — target object, unit/hierarchy, reference standard, sampling, metric/CI, matching/aggregation, failure handling, workflow consequence, transport shift and claim ceiling.
5. Method options — minimum-viable vs stronger, with the trade-off and which reporting guideline each will be judged against (→ radiology-reporting).
6. Validation plan — split scheme, internal + external/temporal/geographic/multi-center design, and the honest definition of "external" for this data.
7. Falsification/controls — counter-hypothesis, negative/control analysis, failure signal, and sensitivity plan.
8. Protocol/SAP/preregistration lock — primary endpoint/estimand, analysis population, acquisition/accepted-image gate, reference standard, split/test access, primary model/comparator, missingness, multiplicity, sensitivity analyses, deviations and open-science plan.
9. Registration/Registered Report state — route distinctions, verified venue policy/status, Stage 1/IPA commitments, deviations and exact claim boundary.
10. PPI/stakeholder/equity ledger — represented groups, decision authority, input/impact, imaging burden/harms, access estimands, residual disagreement and reporting state.
11. Roadmap — when relevant: staged 0-3, 3-6, 6-9, 9-12, 12-18, and 18-24 month milestones.
12. Governance/access state — the verified ethics, consent/waiver, secondary-use and access evidence, or an explicit GOVERNANCE_PENDING/BLOCKED handoff to radiology-ethics.
13. Next actions — what to collect, label, or confirm before/while running it, in priority order. Questions only the author can answer go here.

Quality bar

A good design read sounds like a senior imaging-AI mentor who has reviewed for Radiology: it tells the author honestly whether the data can carry the ambition, designs the validation that will survive review, and surfaces the one constraint everything hinges on — without inflating a single-center retrospective dataset into a claim it cannot support.

Handoffs

• Frontier framing / is this direction novel & publishable → radiology-frontier.
• Disease/organ-specific pathway, reference standard, treatment timeline, imaging artifacts and live clinical-standard context → radiology-clinical-domain.
• Series/phase/sequence qualification, acquisition/reconstruction, quantitative transforms, artifacts, dose, phantom/test-retest and protocol drift → radiology-acquisition-qc.
• Sample size, EPV, power, Riley minimum sample size → radiology-stats.
• Hand-crafted radiomics pipeline design → radiology-radiomics.
• Deep-learning architecture & training design → radiology-deep-learning.
• Imaging × omics mechanism design → radiology-radiogenomics.
• ROI/mask annotation SOP → radiology-annotation.
• Which checklist the design must satisfy → radiology-reporting.
• Document-verified ethics/consent or waiver, secondary-use authority, tissue/data access and sharing constraints → radiology-ethics; unresolved authorization keeps governance pending or blocked regardless of scientific feasibility or target venue.
• Clinical-use scenario, reader study, prospective plan → radiology-translation.
• Turning this design into a funding proposal instead of / alongside a paper → radiology-grant.
• Selective-reporting/deviation or authorship-integrity audit → radiology-research-integrity; delivery RACI/site readiness → radiology-research-ops.
• Full-project state, decision lock, and downstream staleness → radiology-pipeline.
• This skill plans research; it does not provide clinical or diagnostic recommendations.