radiology-radiomics

name

radiology-radiomics

description

Design and audit a hand-crafted radiomics study end-to-end to Radiology (RSNA) / CLEAR / IBSI standard — image preprocessing (resampling, intensity normalisation, gray-level discretisation/bin width, filters), IBSI-compliant feature extraction (PyRadiomics or equivalent), reproducibility/stability filtering, leakage-safe feature selection, modelling, and internal/external validation. Use when the user plans or reviews a radiomics pipeline, mentions PyRadiomics, IBSI, feature extraction, bin width, gray-level discretisation, LASSO feature selection, radiomics signature/score, or "影像组学/放射组学". Produces a reproducible pipeline spec, runnable parameter settings, a leakage audit, and Methods text. Never fabricates feature counts or performance.

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

1. Confirm the design (reuse radiology-design) — endpoint, unit (patient-level), cohorts, validation type, EPV.
2. Segmentation & stability — masks and reproducibility from radiology-annotation; set the ICC stability filter (applied in training).
3. Preprocessing (preprocessing-ibsi.md) — resample, normalise, discretise (state bin width/count), filters; record everything for IBSI.
4. Extraction (feature-extraction.md) — feature families, PyRadiomics (or equivalent) + version, parameter file; produce a documented, versioned feature matrix.
5. Selection & modelling (selection-modelling.md) — selection inside CV/training only; model choice matched to EPV; build the signature/score; pre-specify the primary analysis.
6. Validate — internal (nested CV/bootstrap) + external/temporal/geographic; report discrimination, calibration, DCA (→ radiology-stats).
7. Audit leakage (leakage-audit.md) and write Methods to CLEAR/IBSI.

Output contract

1. Pipeline spec — preprocessing → extraction → selection → model → validation, each step with its parameters and the leakage control marked.
2. Parameters — resampling, normalisation, discretisation (bin width/count), filters, feature families, software+version (a PyRadiomics parameter file where applicable).
3. Selection/modelling plan — method, where it sits relative to the split, EPV check.
4. Validation plan — internal + external; metrics incl. calibration/DCA.
5. Leakage audit — pass/fail per item with the fix.
6. 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.