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radiomics-ml

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Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.

skills/radiomics-ml/SKILL.md

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Nov 10, 2026
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MIT

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# Radiomics / Classical-ML Skill

## Purpose

Radiomics + tree-ensemble studies (features → random forest / XGBoost → a clinical outcome) are the
**most common solo-doable clinical-ML workflow** — no GPU, no engineer — and the **most commonly
over-optimistic**: hundreds-to-thousands of features on tens of patients, hyperparameters tuned on the
same folds the performance is reported from, features selected on the whole dataset, unstable features
never filtered, and discrimination (AUC) reported without calibration. This skill produces the pipeline
correctly and audits an existing one, so the clinical result survives review (Lambin 2017; CLEAR;
TRIPOD+AI; PROBAST-AI).

It sits beside the imaging-DL lane: where `/model-scaffold` builds a deep network, **radiomics-ml**
covers the feature-based classical-ML path. It **integrates** scikit-learn / xgboost / pyradiomics
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