Hand-crafted Radiomics Study Design
Use this skill to build (or audit) a hand-crafted radiomics study that is reproducible and leakage-free, from preprocessing through validation. Radiomics papers are desk-rejected for the same recurring reasons: non-standardised features, segmentation not characterised, and data leakage in selection/normalisation. This skill encodes the IBSI/CLEAR pipeline and the partition hygiene reviewers enforce.
Core stance
- IBSI or it isn't reproducible. Report image processing and feature definitions to IBSI standard (resampling, discretisation, filters, aggregation, software+version) — otherwise "feature X predicts Y" is irreproducible. (→ radiology-reporting/IBSI.)
- Discretisation is a decision, not a default. Fixed bin width vs fixed bin count changes every texture feature; state which, the value, and why; keep it consistent.
- Segmentation error propagates. Use reproducible masks and filter unstable features (ICC) before modelling (→ radiology-annotation).
- Selection lives inside training only. Feature selection, normalisation, imputation, and harmonisation are fit on training folds, never on the whole cohort — the classic leak.
- Match complexity to events. Thousands of features vs tens of patients overfits; respect EPV and validate honestly (→ radiology-stats).
- Report calibration + utility, not just AUC, for a clinical signature (→ radiology-stats).
- Integrity. Never invent feature counts, ICCs, or performance; mark what must be computed.
When to use
- "Design / review my radiomics pipeline (PyRadiomics, IBSI)." / "影像组学流程设计或审查。"
- "Bin width or bin count? what resampling/normalisation/filters?"
- "How do I select features without leakage?" / "LASSO/mRMR feature selection 怎么做才不泄漏？"
- "Build a radiomics signature/score and validate it."
- "Audit this radiomics Methods for leakage and IBSI compliance."
When to open extra files
| File | Open when |
|---|---|
| references/preprocessing-ibsi.md | Resampling, intensity normalisation, gray-level discretisation/bin width, filters, IBSI reporting |
| references/feature-extraction.md | Feature families, PyRadiomics settings, aggregation, software/version, parameter file, delta/longitudinal radiomics, test–retest/phantom repeatability |
| references/selection-modelling.md | Leakage-safe selection (variance/ICC/correlation/LASSO/mRMR), modelling, signature/score, EPV |
| references/leakage-audit.md | The radiomics-specific leakage checklist reviewers weaponise |
Workflow
- Confirm the design (reuse
radiology-design) — endpoint, unit (patient-level), cohorts, validation type, EPV. - Segmentation & stability — masks and reproducibility from
radiology-annotation; set the ICC stability filter (applied in training). - Preprocessing (preprocessing-ibsi.md) — resample, normalise, discretise (state bin width/count), filters; record everything for IBSI.
- Extraction (feature-extraction.md) — feature families, PyRadiomics (or equivalent) + version, parameter file; produce a documented, versioned feature matrix.
- Selection & modelling (selection-modelling.md) — selection inside CV/training only; model choice matched to EPV; build the signature/score; pre-specify the primary analysis.
- Validate — internal (nested CV/bootstrap) + external/temporal/geographic; report discrimination, calibration, DCA (→ radiology-stats).
- Audit leakage (leakage-audit.md) and write Methods to CLEAR/IBSI.
Output contract
Pipeline spec— preprocessing → extraction → selection → model → validation, each step with its parameters and the leakage control marked.Parameters— resampling, normalisation, discretisation (bin width/count), filters, feature families, software+version (a PyRadiomics parameter file where applicable).Selection/modelling plan— method, where it sits relative to the split, EPV check.Validation plan— internal + external; metrics incl. calibration/DCA.Leakage audit— pass/fail per item with the fix.Methods paragraph— CLEAR/IBSI-aligned prose (+ 待确认 for Chinese authors).
Quality bar
A good radiomics spec is one another lab could re-run from the parameters alone and get the same features — with segmentation error quantified, selection inside the split, and performance reported with calibration and CIs, never AUC alone.
Handoffs
- Mask SOP & feature-stability →
radiology-annotation. - IBSI/CLEAR/METRICS/RQS audit →
radiology-reporting. - Selection/CV statistics, calibration, DCA, multiplicity, sample size →
radiology-stats. - Deep features / deep-learning comparison →
radiology-deep-learning. - Biological interpretation of the signature →
radiology-radiogenomics. - Figures (feature heatmap, ROC, calibration, nomogram) →
radiology-figure. - Reframing this pipeline as a funding proposal →
radiology-grant.
