Academic radiology · 2026
Habitat-Based Radiomic Quantification of Intratumoral Heterogeneity on Digital Breast Tomosynthesis for Predicting HER2 Status in Invasive Breast Cancer.
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Miao Yifan, Liu Jie, Gao Yiping, Chen Shuangqing
Аннотация
UNLABELLED: RATIONALE AND OBJECTIVE: This exploratory study aims to develop and validate a model on Digital Breast Tomosynthesis (DBT), using habitat‑based radiomics to quantify Intratumoral Heterogeneity (ITH) for non-invasive pre-operative prediction of HER2 status in invasive breast cancer.
MATERIALS AND METHODS: 444 people with invasive breast cancer were studied (2018.1-2025.6). They were classified as HER2-positive, HER2-low, or HER2-zero. After radiomics feature extraction and dimensionality reduction, we developed Intra, ITH, and combined models using multilayer perceptron (MLP) and logistic regression (LR) classifiers. Two classification tasks were performed: Task 1 is to differentiate HER2-positive versus HER2-negative individuals. Task 2, which is to differentiate individuals with HER2-low versus HER2-zero status.
RESULTS: The ITH model achieved good performance on the test set in Task 1(AUC, 0.934; 95% CI: 0.881, 0.988), while the combined model achieved a numerically higher AUC of 0.940 (95% CI: 0.894, 0.986). In Task 2, the ITH model performed comparably to the conventional Intra model, whereas the combined model produced the most favorable results on the test set (AUC, 0.849; 95% CI: 0.740, 0.958).
CONCLUSIONS: The combined model performed best at discriminating HER2-positive, HER2-low, and HER2-zero cases. Habitat-based radiomics quantifying ITH, thereby facilitating the prediction of HER2 status.
MATERIALS AND METHODS: 444 people with invasive breast cancer were studied (2018.1-2025.6). They were classified as HER2-positive, HER2-low, or HER2-zero. After radiomics feature extraction and dimensionality reduction, we developed Intra, ITH, and combined models using multilayer perceptron (MLP) and logistic regression (LR) classifiers. Two classification tasks were performed: Task 1 is to differentiate HER2-positive versus HER2-negative individuals. Task 2, which is to differentiate individuals with HER2-low versus HER2-zero status.
RESULTS: The ITH model achieved good performance on the test set in Task 1(AUC, 0.934; 95% CI: 0.881, 0.988), while the combined model achieved a numerically higher AUC of 0.940 (95% CI: 0.894, 0.986). In Task 2, the ITH model performed comparably to the conventional Intra model, whereas the combined model produced the most favorable results on the test set (AUC, 0.849; 95% CI: 0.740, 0.958).
CONCLUSIONS: The combined model performed best at discriminating HER2-positive, HER2-low, and HER2-zero cases. Habitat-based radiomics quantifying ITH, thereby facilitating the prediction of HER2 status.