Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction
Thyroid nodule malignancy assessment relies on ultrasound imaging, yet current risk stratification systems suffer from substantial interobserver variability, contributing to a high rate of unnecessary biopsies. We propose a radiomics-based framework combining quantitative feature extraction via PyRadiomics with a Generalized Additive Model (GAM) classifier, selected among six candidates for its favorable trade-off between discriminative performance and interpretability. To complement point predictions with uncertainty estimates, Cross-Conformal Prediction with label-conditional guarantees is applied, flagging ambiguous cases for clinical review rather than forcing a binary decision. Exper...
| Authors | Laura Testa, Pierpaolo Brutti, Umberto Ferraro Petrillo |
|---|---|
| Primary category | eess.IV |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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