radiomics-ml

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.