J Imaging Inform Med. 2026 Jun 15. doi: 10.1007/s10278-026-02055-2. Online ahead of print.
ABSTRACT
Breast cancer is a heterogeneous disease whose molecular subtypes differ in biological behavior, prognosis, and therapeutic response. This study investigated whether whole-breast radiomic features extracted from digital mammograms and showing statistically significant differences between HER2 + tumors and other molecular subtypes or healthy controls could also provide discriminatory information for exploratory HER2 + characterization. An automated whole-breast segmentation and feature-extraction workflow, without manual lesion-centered delineation, was applied to the breast region to capture broader parenchymal and microenvironmental texture patterns while reducing dependence on manual lesion annotation. Intensity-based, first-order, and second-order texture features were extracted from DICOM mammograms, followed by pairwise statistical testing, false discovery rate correction, effect-size assessment, Gaussian distribution analysis, normalized feature visualization, univariate AUC analysis, and classifier evaluation using logistic regression and linear support vector machines. First-order and intensity-based descriptors showed limited subtype-specific value, whereas second-order texture features provided more informative discriminatory patterns. Among the evaluated feature families, GLCM and NGLDM descriptors showed the most coherent evidence across statistical, visual, and classifier-based analyses, with NGLDM yielding the broadest set of statistically significant features. Classification performance was strongest and most balanced for HER2 + versus healthy controls, while discrimination between HER2 + and other malignant molecular subtypes was modest, context-dependent, and affected by sensitivity-specificity imbalance in several models. Therefore, the present findings more strongly support sensitivity to malignancy-related whole-breast texture alterations than reliable HER2-specific classification among malignant subtypes. Whole-breast mammographic radiomics should be interpreted as an exploratory and complementary source of candidate imaging biomarkers for future validation.
PMID:42298097 | DOI:10.1007/s10278-026-02055-2
