/radiomics-ml

name: radiomics-ml
description: 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.
metadata:
  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"

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 → `/model-selection` → `/model-scaffold` → `/model-assessment`.
• Classical inferential statistics / a regression model as the estimand → `/analyze-stats`.
• Interpretability of a trained network → `/model-assessment`.
• 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.** Candidate features ≥ events overfits — the classic radiomics trap. Declaring `dimensionality_reduction` does not clear it: `n_features` already counts what is left after outcome-blind reduction. The gate's `p ≥ events` rule (events = the minority class) is a floor that catches the worst case, **not a sample-size criterion**: size the study with `/calc-sample-size` Test 12 (Riley criteria, `pmsampsize`), counting every **candidate** feature that reaches outcome-driven selection or fitting. At C = 0.75 and 35% prevalence that is about 48 patients per candidate parameter: 100 candidates need N = 4,755 (1,665 events), and the gate passes 100 features on 105 events. Reduce candidates **without the outcome** first (stability, redundancy, clinical prior); LASSO or other penalisation does not substitute for sample size, because the shrinkage it estimates is itself unstable at small n (Riley et al., *J Clin Epidemiol* 2021; Van Calster et al., *Stat Methods Med Res* 2020). 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. **No external validation.** A single-cohort model needs external / temporal validation for a clinical claim.

Not in the gate, because the manifest cannot show it: **rows of one patient on both sides of a split.** Lesion-level tables with row-wise folds let the model recognise the patient; on null synthetic data this took nested-CV AUROC from 0.48 to 0.99. Split by patient and check the fold table (Phase 2).

Workflow

Phase 1 — Extract features (integrate, don't reimplement)

For radiomics, extract with **pyradiomics** under reproducible, IBSI-aligned settings (fixed bin width, resampling, normalisation) — record them. For clinical/tabular data, assemble the feature table with a patient/subject ID and the outcome. One row per lesion or ROI is fine; the ID is what the folds are split by. See `references/radiomics_ml_guide.md`.

Phase 2 — Build the pipeline correctly

• **Feature stability** — with test-retest / multi-rater data, keep features with ICC ≥ 0.75.
• **Nested cross-validation** — outer folds estimate performance, inner folds tune; do **feature

selection and scaling inside each training fold** (never on the whole dataset). **The CV unit is the patient**: split both loops with `StratifiedGroupKFold(groups=patient_id)` so one patient's lesions never straddle folds, write the fold table (`patient_id,split`), and prove it with `/model-assessment`'s `check_split_leakage.py --splits cv_folds.csv --seed <seed> --strict` (skeleton in the guide §4).

• **Dimensionality** — reduce the candidate set without the outcome (ICC stability, |r| redundancy

filter, clinical prior; PCA fit inside the fold), then size the study for the candidates that remain with `/calc-sample-size` Test 12 (`pmsampsize`). LASSO selects inside the fold but does not mak