International journal of general medicine · 2026
MRI-Based Intratumoral and Multi-Range Peritumoral Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer.
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Chu Bingchen, Zhan Tongyun, Liu Xing, Huang Haiying
Аннотация
BACKGROUND: To determine the best-performing peritumoral boundary among tested distances and evaluate the predictive value of preoperative multiparametric MRI radiomics for axillary lymph node metastasis (ALNM) in breast cancer.
METHODS: Clinical and imaging data of 273 patients from two centers were retrospectively analyzed. ROIs were manually delineated on Fs-T2WI and DCE-MRI images, and peritumoral regions were generated by expanding the tumor ROI by 3 mm, 5 mm, and 7 mm. Patients from Center 1 (n = 201) were randomly divided into training and internal validation set (7:3), while Center 2 (n = 72) served as the external validation set. LASSO regression and an SVM classifier were used to construct predictive models. A nomogram integrating the best-performing radiomic model with clinical parameters was developed. Model performance was assessed using AUC and DCA.
RESULTS: Maximum tumor diameter was identified as an independent risk factor for ALNM (OR = 4.042, 95% CI: 2.488-6.532; P < 0.001). Among the tested peritumoral margins, the 5 mm model demonstrated the best predictive efficacy, significantly outperforming both 3 mm (AUC 0.694) and 7 mm (AUC 0.717) models (DeLong test, all P < 0.05). The fusion model combining DCE-MRI intra- and peritumoral (5 mm) features with Fs-T2WI intratumoral features achieved AUCs of 0.909, 0.818, and 0.768 in the training, internal validation, and external validation sets, respectively. The nomogram integrating the fusion model with maximum tumor diameter further improved performance, with AUCs of 0.943, 0.884, and 0.853, respectively.
CONCLUSION: Among the tested distances, a 5 mm peritumoral margin provided the best predictive performance for ALNM. The multiparametric MRI radiomic model effectively predicts ALNM and offers a valuable non-invasive tool for preoperative assessment.
METHODS: Clinical and imaging data of 273 patients from two centers were retrospectively analyzed. ROIs were manually delineated on Fs-T2WI and DCE-MRI images, and peritumoral regions were generated by expanding the tumor ROI by 3 mm, 5 mm, and 7 mm. Patients from Center 1 (n = 201) were randomly divided into training and internal validation set (7:3), while Center 2 (n = 72) served as the external validation set. LASSO regression and an SVM classifier were used to construct predictive models. A nomogram integrating the best-performing radiomic model with clinical parameters was developed. Model performance was assessed using AUC and DCA.
RESULTS: Maximum tumor diameter was identified as an independent risk factor for ALNM (OR = 4.042, 95% CI: 2.488-6.532; P < 0.001). Among the tested peritumoral margins, the 5 mm model demonstrated the best predictive efficacy, significantly outperforming both 3 mm (AUC 0.694) and 7 mm (AUC 0.717) models (DeLong test, all P < 0.05). The fusion model combining DCE-MRI intra- and peritumoral (5 mm) features with Fs-T2WI intratumoral features achieved AUCs of 0.909, 0.818, and 0.768 in the training, internal validation, and external validation sets, respectively. The nomogram integrating the fusion model with maximum tumor diameter further improved performance, with AUCs of 0.943, 0.884, and 0.853, respectively.
CONCLUSION: Among the tested distances, a 5 mm peritumoral margin provided the best predictive performance for ALNM. The multiparametric MRI radiomic model effectively predicts ALNM and offers a valuable non-invasive tool for preoperative assessment.