Frontiers in oncology · 2026
MRI-based habitat radiomics for preoperative prediction of axillary pathological complete response in breast cancer after neoadjuvant therapy: a multicenter study.
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Ma Lei, Li Shunian, Cao Ziwei, Liao Jun, Xu Mengting, Lv Chuanjian, Xu Chunmiao, Tan Hongna
Аннотация
PURPOSE: To develop and validate a preoperative MRI-based habitat radiomics nomogram for noninvasive prediction of axillary pathological complete response (apCR) after neoadjuvant therapy (NAT) in node-positive breast cancer.
PATIENTS AND METHODS: This retrospective multicenter study included patients with histologically confirmed node-positive breast cancer from two institutions who underwent pretreatment breast MRI. Dynamic contrast-enhanced MRI was used for tumor segmentation and to define intratumoral habitat subregions based on enhancement heterogeneity. Radiomic features were extracted from whole tumors and habitat subregions, and radiomics and habitat signatures were integrated with clinicopathologic variables to construct a nomogram for preoperative prediction of apCR. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration assessment, and decision curve analysis (DCA).
RESULTS: A total of 336 women were included. In the training cohort, the radiomics, habitat, and nomogram models achieved AUCs of 0.723, 0.765, and 0.845, respectively. The nomogram consistently demonstrated the highest discriminative performance in the internal validation and independent external test cohorts, with AUCs of 0.755 and 0.754. Calibration analysis showed good agreement between predicted and observed apCR, and DCA indicated greater net clinical benefit for the nomogram.
CONCLUSION: The proposed MRI-based habitat radiomics nomogram showed promising performance for noninvasive, preoperative prediction of apCR after NAT and may assist individualized axillary risk stratification in patients with node-positive breast cancer.
PATIENTS AND METHODS: This retrospective multicenter study included patients with histologically confirmed node-positive breast cancer from two institutions who underwent pretreatment breast MRI. Dynamic contrast-enhanced MRI was used for tumor segmentation and to define intratumoral habitat subregions based on enhancement heterogeneity. Radiomic features were extracted from whole tumors and habitat subregions, and radiomics and habitat signatures were integrated with clinicopathologic variables to construct a nomogram for preoperative prediction of apCR. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration assessment, and decision curve analysis (DCA).
RESULTS: A total of 336 women were included. In the training cohort, the radiomics, habitat, and nomogram models achieved AUCs of 0.723, 0.765, and 0.845, respectively. The nomogram consistently demonstrated the highest discriminative performance in the internal validation and independent external test cohorts, with AUCs of 0.755 and 0.754. Calibration analysis showed good agreement between predicted and observed apCR, and DCA indicated greater net clinical benefit for the nomogram.
CONCLUSION: The proposed MRI-based habitat radiomics nomogram showed promising performance for noninvasive, preoperative prediction of apCR after NAT and may assist individualized axillary risk stratification in patients with node-positive breast cancer.