European journal of radiology · 2026
Predicting axillary lymph node metastasis in clinical T1/2 stage breast cancer using iodine map-derived multi-region radiomics and multi-modality imaging characteristics.
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Zeng Fang, Chen Cong, Lin Hailong, Yan Tingting, Yang Zheting, Chen Minyan, Lin Zhenhu, Chen Suping, Wang Chuang, Xue Yunjing
Аннотация
PURPOSE: To explore the feasibility of an integrative model incorporating multi-region radiomic features extracted from chest dual-energy CT (DECT)-based iodine maps, clinical parameters, and CT and ultrasonography (US) features of axillary lymph node (ALN), to preoperatively predict ALN metastasis (ALNM) in clinical T1/2 stage breast cancer.
METHOD: This retrospective study enrolled 197 patients with breast cancer who underwent preoperative contrast-enhanced DECT from March 2021 to May 2022. Radiomic features were extracted from venous-phase iodine maps based on three regions of interests (ROIs): ALN, tumoral and peritumoral regions (2.5 mm around the tumor). Clinical information, CT and US parameters were recorded and evaluated. Eight predictive models were built: 1) A clinical model; 2) CT features-based model; 3) US-based model; 4) tumor-based radiomic model; 5) peritumor-based radiomic model; 6) ALN-based radiomic model; 7) multi-ROIs radiomics model; 8) integrative model. The ALNM prediction performances and clinical usefulness were assessed.
RESULTS: Radiomic signatures derived from ALN, tumor, peritumoral and multi-ROIs achieved AUCs of 0.860, 0.709, 0.747 and 0.890 in the training cohort, and 0.860, 0.676, 0.663, and 0.890 in the testing cohort, respectively. The integrative model incorporating tumor location, T-stage, Ki-67 index, hilus structure, shortest nodal diameter, intranodal vascular pattern, and multi-region radiomic features, demonstrated further increased AUCs of 0.923 and 0.914, with good calibration and clinical benefit.
CONCLUSIONS: The model integrating clinical parameters, DECT- and US- reported ALN features, as well as iodine map-derived multi-region radiomic features, could serve as a potential tool to preoperatively predict ALN status.
METHOD: This retrospective study enrolled 197 patients with breast cancer who underwent preoperative contrast-enhanced DECT from March 2021 to May 2022. Radiomic features were extracted from venous-phase iodine maps based on three regions of interests (ROIs): ALN, tumoral and peritumoral regions (2.5 mm around the tumor). Clinical information, CT and US parameters were recorded and evaluated. Eight predictive models were built: 1) A clinical model; 2) CT features-based model; 3) US-based model; 4) tumor-based radiomic model; 5) peritumor-based radiomic model; 6) ALN-based radiomic model; 7) multi-ROIs radiomics model; 8) integrative model. The ALNM prediction performances and clinical usefulness were assessed.
RESULTS: Radiomic signatures derived from ALN, tumor, peritumoral and multi-ROIs achieved AUCs of 0.860, 0.709, 0.747 and 0.890 in the training cohort, and 0.860, 0.676, 0.663, and 0.890 in the testing cohort, respectively. The integrative model incorporating tumor location, T-stage, Ki-67 index, hilus structure, shortest nodal diameter, intranodal vascular pattern, and multi-region radiomic features, demonstrated further increased AUCs of 0.923 and 0.914, with good calibration and clinical benefit.
CONCLUSIONS: The model integrating clinical parameters, DECT- and US- reported ALN features, as well as iodine map-derived multi-region radiomic features, could serve as a potential tool to preoperatively predict ALN status.