Slice-level vs. volumetric radiomics for molecular subtyping of breast cancer: impact of patient-level data partitioning on model performance.

Introduction: Molecular subtyping of breast cancer, particularly the discrimination between Luminal A and Basal-like tumours, is critical for guiding treatment decisions and establishing prognosis. Radiomics offers a promising complementary, non-invasive imaging biomarker to support biopsy-based subtyping; however, two methodological gaps limit the validity of existing studies: the absence of controlled 2D-vs.-3D feature comparisons, and the systematic underreporting of patient-level data leakage in multi-slice pipelines. Methods: To address both limitations, this study benchmarks slice-level (2D) and volumetric (3D) radiomic pipelines for the classification of Luminal A vs. Basal-like tumours on the TCGA-BRCA cohort, evaluating seven classifiers combined with two feature selection strategies under a Stratified Group Results: The 3D pipeline achieved a mean AUC of 0.682 vs. 0.631 for the 2D pipeline. In the 3D configuration, the Support Vector Machine with SelectKBest-MI feature selection attained the highest performance (AUC = 0.768 ± 0.066), while the 2D configuration was led by Logistic Regression (AUC = 0.769 ± 0.109), albeit with substantially higher fold-to-fold variability. Six out of seven classifiers yielded higher AUC scores under the 3D configuration, although a paired significance test on fold-level AUC values did not reach statistical significance ( Discussion: These results establish a reproducible, leakage-free baseline for non-invasive breast cancer subtype classification through radiomics, with 3D radiomics offering improved robustness rather than superior peak performance. Results are derived from a single cohort (TCGA-BRCA) and external validation is required before clinical generalizability can be claimed.