# Radiology Design

> Assess whether an imaging dataset can support a study and turn it into a complete, submittable design — from feasibility triage to clinical question, target population, endpoint/estimand, minimum-viable vs stronger methods, and a validation strategy (internal resampling, temporal, geographic, fully external, multi-center, federated). Use when the user has CT/MRI/PET/US/mammography/multimodal data but is unsure what to do, asks "can this topic be done?" / "能不能做" / "帮我设计课题" / "study design" / "what can I study with this data", or needs a multi-center / external-validation plan ("多中心", "external validation", "generalisability", "center effect", "scanner effect"). Produces a study blueprint with feasibility verdict, design options, validation plan, and the limiting constraint surfaced. Never invents cohort numbers or overstates what the data can support.

- Skill: `huang-sir1/radiology-design` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add huang-sir1/radiology-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/huang-sir1/radiology-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: huang-sir1 (https://skillmd.com/u/huang-sir1)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/huang-sir1/radiology-design

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# 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 **feasibility** —
can this data support a credible study at all? — and (2) converts a feasible idea into a
**complete, submittable design**: clinical question, population, endpoint, methods (minimum
viable → stronger), and the **validation strategy** that decides whether the work is
generalisable or single-center-anecdote.

## 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 is the spine.** Internal cross-validation alone is weak. State the validation
  type explicitly and design it **before** modelling; external/temporal/geographic validation
  is what separates _Radiology_-tier work from a desk reject.
- **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.
- **Pre-specify.** Primary endpoint, primary analysis, and the split scheme are decided before
  looking at results. Retro-fitting the question to the result is the cardinal sin.
- **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.

## When to open extra files

| File | Open when |
|---|---|
| [references/feasibility-triage.md](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](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](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](references/endpoints-and-estimands.md) | Choosing the clinical question, target population, endpoint, comparator, and clinical-use scenario |
| [references/ai-radiogenomics-12-24-roadmap.md](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.** Disease, modality(ies), n (patients and lesions), number of centers
   and scanners, label source and quality, presence of segmentation masks, clinical variables,
   follow-up time and event counts, pathology/molecular labels, time span. Mark every unknown.
2. **Feasibility triage** (feasibility-triage.md). Decide the realistic task ceiling and flag
   showstoppers (no reference standard, no external cohort, too few events, leakage-prone
   structure). Output a verdict: `Feasible as designed` / `Feasible with changes` / `Feasibility
   study only` / `Not yet — collect X first`.
3. **Define the question** (endpoints-and-estimands.md). Clinical question → target population →
   primary endpoint/estimand → comparator → intended clinical-use scenario.
4. **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.
5. **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.
6. **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.
7. **Name the binding constraint** and the sample-size / EPV question (hand the numbers to
   `radiology-stats`).
8. **Return** the blueprint + feasibility verdict + validation plan + the prioritised list of
   what to secure next.

## Output contract

1. **`Feasibility verdict`** — one of the four verdicts above, with the one-line reason.
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. **`Method options`** — minimum-viable vs stronger, with the trade-off and which reporting
   guideline each will be judged against (→ `radiology-reporting`).
5. **`Validation plan`** — split scheme, internal + external/temporal/geographic/multi-center
   design, and the honest definition of "external" for this data.
6. **`Roadmap`** — when relevant: staged 0-3, 3-6, 6-9, 9-12, 12-18, and 18-24 month milestones.
7. **`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`.
- 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`.
- Ethics/consent/data-sharing feasibility → `radiology-ethics`.
- Clinical-use scenario, reader study, prospective plan → `radiology-translation`.
- Turning this design into a funding proposal instead of / alongside a paper → `radiology-grant`.
- This skill plans research; it does not provide clinical or diagnostic recommendations.