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 → /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)

• No nested CV. Tuning and reporting on the same folds inflates performance. Use nested CV or a

held-out test set.

• High dimensionality, low events. Features ≥ events with no dimensionality reduction overfits —

the classic radiomics trap. Apply LASSO / PCA / a stability + redundancy filter.

• Selection outside the fold. Feature selection fit on the whole dataset leaks the held-out folds.

Nest selection inside each training fold.

• No feature stability. Radiomics features are unstable across acquisition/segmentation — filter

to reproducible features (ICC / test-retest).

• No calibration. A clinical prediction model needs calibration (slope/intercept + a flexible

curve), not discrimination alone.

• No external validation. A single-cohort model needs external / temporal validation for a

clinical claim.

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. 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).

• Dimensionality — with features ≥ events, use LASSO / a stability+redundancy filter / PCA.
• Model — pick from the full classical family for the task; a simple baseline (penalised logistic)

is mandatory alongside any complex learner:

• *penalised regression* — LASSO / ridge / elastic-net logistic (also the baseline)
• *margin / kernel* — linear or RBF SVM
• *instance-based* — k-NN
• *probabilistic / discriminant* — naive Bayes, LDA / QDA
• *trees & bagging* — decision tree, random forest, extra-trees
• *boosting* — XGBoost, LightGBM, CatBoost, HistGBM, AdaBoost
• *shallow neural* — MLP
• *meta* — stacking / voting ensembles
• *unsupervised (upstream)* — PCA / UMAP for reduction, k-means / hierarchical / GMM for phenotyping

The gate below is learner-agnostic — it audits the pipeline (nested CV, leakage, dimensionality,

calibration), so it applies identically to any of these. See the full method map in

docs/method_coverage_map.md.

• Report — discrimination and calibration (slope/intercept + flexible curve, via the

/analyze-stats calibration guide) and clinical utility (decision curve). SHAP for interpretation.

Phase 3 — Emit the pipeline manifest

{
  "task": "classification",
  "n_features": 1200, "n_samples": 300, "n_events": 110,
  "cv_scheme": "nested",
  "feature_selection_stage": "inside_cv",
  "dimensionality_reduction": true,
  "feature_stability": "icc",
  "calibration_reported": true,
  "external_validation": "temporal",
  "model": "xgboost"
}

Phase 4 — Gate the pipeline (deterministic)

python3 scripts/check_radiomics_ml.py --manifest pipeline_manifest.json --strict

Verdicts: NO_NESTED_CV, HIGH_DIM_LOW_EVENTS, SELECTION_OUTSIDE_CV (Major);

NO_FEATURE_STABILITY, NO_CALIBRATION, NO_EXTERNAL_VALIDATION (Minor). Complements

self-review's check_cv_leakage (which audits a finished manuscript's prose) at the pipeline-spec

level.

Integration

• /analyze-stats — calibration + clinical-utility (decision curve, NNT) guides for the reporting.
• /check-reporting — CLEAR (radiomics), TRIPOD+AI, PROBAST-AI item coverage.
• /self-review clinical_prediction_model probe audits the finished manuscript; this skill

*produces* the rigorous pipeline it looks for.

Anti-Hallucination

• Never fabricate features, performance metrics, or sample/event counts. Every value in the

manifest and every reported metric comes from the researcher's executed code — never invented. This

skill designs and audits the pipeline; it does not run a model on real patient data.

• Never report flat-CV performance as if it were nested or held-out. Tuning on the reported folds

is the optimism this skill exists to prevent (NO_NESTED_CV).

• Never report a radiomics/ML audit "pass" without running check_radiomics_ml.py. The rigor

verdict is reproduced deterministically, never asserted from prose.

• Integrate, don't reimplement. Reference scikit-learn / xgboost / pyradiomics; do not write a new

feature extractor or learner or claim results for one.

Reproducible challenge

scripts/check_radiomics_ml_challenge/ ships a synthetic weak/strong pipeline pair with a network-free

verify.sh wired into the skill's validation commands.