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.
التثبيت
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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."
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.
