version: "1.0.0" name: radiology-radiomics description: "Design/audit radiomics preprocessing, IBSI extraction, stability, leakage-safe selection and validation."

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 defines; evidence verifies. Use IBSI to define and report image processing and

feature calculations (resampling, discretisation, filters, aggregation, software+version). Conformance improves semantic and implementation comparability, but does not by itself establish empirical repeatability across segmentations, scans, software builds, scanners or protocols. Pair it with implementation checks and task-matched phantom/test-retest/sensitivity evidence.

• 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 effective complexity to information. Thousands of features versus few independent

patients/events is unstable, but no universal EPV threshold grants adequacy. Let radiology-stats define a project-specific information/precision brief from the estimand, event/non-event support, effective degrees of freedom, shrinkage, calibration and resampling stability.

• 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

Workflow

1. Confirm the design (reuse radiology-design) — endpoint, independent unit/hierarchy,

cohorts, validation type and project-specific information/precision constraints.

1. Segmentation & stability — masks and reproducibility from radiology-annotation, which owns

the ICC stability metric and threshold provenance; this skill freezes and applies that filter inside training.

1. Preprocessing (preprocessing-ibsi.md) — resample, normalise, discretise (state bin

width/count), filters; record everything for IBSI.

1. Extraction (feature-extraction.md) — feature families, PyRadiomics (or equivalent) +

version, parameter file; produce a documented, versioned feature matrix.

1. Selection & modelling (selection-modelling.md) — selection inside CV/training only;

model complexity matched to the statistical brief; build the signature/score; pre-specify the primary analysis.

1. Validate — internal (nested CV/bootstrap) + external/temporal/geographic; report

discrimination, calibration, DCA (→ radiology-stats).

1. Freeze reproducibility artifacts (reproducibility-artifacts.md) — split/run manifest,

parameter file, feature dictionary/count trail, fitted pipeline, coefficients/signature equation, environment, and deviations.

1. 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.

1. `Parameters` — resampling, normalisation, discretisation (bin width/count), filters,

feature families, software+version (a PyRadiomics parameter file where applicable).

1. `Selection/modelling plan` — method, where it sits relative to the split, effective

complexity and the project-specific information/precision check.

1. `Validation plan` — internal + external; metrics incl. calibration/DCA.
2. `Leakage audit` — pass/fail per item with the fix.
3. `Reproducibility bundle` — run/split manifest, parameter file, feature dictionary and

count trail, fitted preprocessing/selection/model objects, coefficient/signature mapping, environment, and deviations actually available.

1. `Table handoff` — feature-stage counts, selected-feature dictionary, coefficients/weights,

and model-performance value keys for radiology-table.

1. `Methods paragraph` — CLEAR/IBSI-aligned prose (+ 待确认 for Chinese authors).

Quality bar

A good radiomics spec lets another lab identify and execute the same feature definition and then test whether the implementation and measurements agree within declared tolerances. It quantifies segmentation/acquisition sensitivity, keeps selection inside the split, and reports performance with calibration and CIs, never AUC alone.

Handoffs

• Series/phase/sequence qualification, acquisition/reconstruction, quantitative transforms,

artifacts, dose, phantom/test-retest and protocol drift → radiology-acquisition-qc; consume its frozen measurement passport before radiomics preprocessing.

• Mask SOP & feature-stability → radiology-annotation.
• IBSI/CLEAR/METRICS/RQS audit → radiology-reporting.
• Selection/CV statistics, calibration, DCA, multiplicity, sample size → radiology-stats.
• Parameter provenance, bin-width/spacing/interpolation/segmentation sensitivity, fair method

comparison, ablation and claim-level robustness → radiology-method-evaluation; this skill still owns radiomics configuration and execution.

• Deep features / deep-learning comparison → radiology-deep-learning.
• Biological interpretation of the signature → radiology-radiogenomics.
• Figures (feature heatmap, ROC, calibration, nomogram) → radiology-figure.
• Tables (feature audit, coefficient/signature, performance, subgroup/sensitivity) →

radiology-table.

• Reframing this pipeline as a funding proposal → radiology-grant.
• Full-project state, simulation labels, and cross-artifact consistency → radiology-pipeline.