radiomics-ml

name	radiomics-ml
description	Produce or audit a radiomics / tabular clinical-ML study — imaging or clinical features → any classical learner (penalised logistic [LASSO / ridge / elastic-net], SVM, k-NN, naive Bayes, LDA/QDA, decision tree, random forest, gradient boosting [XGBoost / LightGBM / CatBoost], shallow MLP, stacked ensembles) → a clinical outcome — so it clears the rigor bar reviewers expect: nested cross-validation (tuning never on the reported folds), dimensionality control for the features-far-exceed-events regime, feature selection inside the fold, feature-stability (ICC / test-retest) filtering, calibration, and external/temporal validation. The deterministic gate is learner-agnostic (it audits the pipeline, not the algorithm). Emits a pipeline manifest and the gate. The most common solo-doable clinical-ML workflow — no GPU, no engineer. Integrates scikit-learn / xgboost / lightgbm / catboost / pyradiomics; it does not reimplement them.
triggers	radiomics, radiomic features, pyradiomics, tabular ML, clinical prediction model, random forest, XGBoost, LightGBM, CatBoost, gradient boosting, tree ensemble, SVM, support vector machine, k-NN, KNN, naive Bayes, LDA, QDA, elastic net, ridge, LASSO, logistic regression, MLP, stacking, ensemble, clustering, k-means, PCA, UMAP, dimensionality reduction, feature selection, nested cross-validation, nested CV, ICC feature stability, SHAP, machine learning model, classical ML, clinical machine learning, feature stability, decision curve, calibration, TRIPOD, CLEAR, PROBAST
tools	Read, Write, Edit, Bash, Grep, Glob
model	inherit

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 (referenced in the emitted code); it does not reimplement them and never runs a model on real patient data.

When to use

• You have a radiomics or clinical/tabular feature table and want to build a random-forest / XGBoost clinical prediction model that will pass statistical review.
• You want to audit an existing radiomics/ML pipeline for the failure modes below.

When NOT to use

• Deep-learning imaging models → /architecture-zoo → /model-scaffold → /model-validation.
• Classical inferential statistics / a regression model as the estimand → /analyze-stats.
• Interpretability of a trained network → /explainability.
• Reimplementing scikit-learn / xgboost / pyradiomics → out of scope (this skill wires and audits them).

The failure modes (what the gate enforces)

1. No nested CV. Tuning and reporting on the same folds inflates performance. Use nested CV or a held-out test set.
2. High dimensionality, low events. Features ≥ events with no dimensionality reduction overfits — the classic radiomics trap. Apply LASSO / PCA / a stability + redundancy filter.
3. Selection outside the fold. Feature selection fit on the whole dataset leaks the held-out folds. Nest selection inside each training fold.
4. No feature stability. Radiomics features are unstable across acquisition/segmentation — filter to reproducible features (ICC / test-retest).
5. No calibration. A clinical prediction model needs calibration (slope/intercept + a flexible curve), not discrimination alone.
6. A single-cohort model needs external / temporal validation for a clinical claim.