Clinical breast cancer · 2025
Development and Validation of Preoperative Ultrasound-Based Risk Scoring Systems for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Multicenter, Retrospective Study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Ping Jieyi, Zha Hailing, Chen Baoding, Gu Jun, Du Liwen, Cai Mengjun, Lin Minjia, Liu Xiaoan, Wang Hui, Li Cuiying
Аннотация
PURPOSE: This study aimed to develop and validate 2 preoperative ultrasound (US)-based risk-scoring systems to predict axillary lymph node (ALN) metastasis and the number of metastatic ALNs (≤ 2 vs. > 2) in patients with breast cancer.
METHOD: A multicenter retrospective study included 1194 women with breast cancer from 3 institutions. Institutions 1 and 2 were used for training (n = 643) and testing (n = 275), while 276 patients from Institution 3 served as the validation set. Multivariate logistic regression was used to construct 2 risk-scoring systems based on US features. Predictive performance was assessed using area under the curve (AUC), calibration plots, and decision curve analysis.
RESULTS: The risk-scoring system for ALN metastasis (presence vs. absence), based on the longest diameter and margin of the mass, mass pathology, long-to-short axis ratio of the ALN, cortical morphological features, and blood flow type of the ALN, achieved AUCs of 0.86, 0.81, and 0.84 in the training, testing, and validation sets, respectively. The risk-scoring system for predicting the number of metastatic ALNs (≤ 2 vs. >2), based on US (the longest diameter and the number of suspicious ALNs on US), achieved AUCs of 0.78, 0.75, and 0.78 in the training, testing, and validation sets, respectively. Calibration plots showed good model calibration, and decision curve analysis confirmed the clinical utility of both models.
CONCLUSION: The 2 preoperative US-based risk-scoring systems effectively predicted ALN metastasis and the number of metastatic ALNs, aiding clinicians in assessing the risk of ALN involvement in breast cancer patients.
METHOD: A multicenter retrospective study included 1194 women with breast cancer from 3 institutions. Institutions 1 and 2 were used for training (n = 643) and testing (n = 275), while 276 patients from Institution 3 served as the validation set. Multivariate logistic regression was used to construct 2 risk-scoring systems based on US features. Predictive performance was assessed using area under the curve (AUC), calibration plots, and decision curve analysis.
RESULTS: The risk-scoring system for ALN metastasis (presence vs. absence), based on the longest diameter and margin of the mass, mass pathology, long-to-short axis ratio of the ALN, cortical morphological features, and blood flow type of the ALN, achieved AUCs of 0.86, 0.81, and 0.84 in the training, testing, and validation sets, respectively. The risk-scoring system for predicting the number of metastatic ALNs (≤ 2 vs. >2), based on US (the longest diameter and the number of suspicious ALNs on US), achieved AUCs of 0.78, 0.75, and 0.78 in the training, testing, and validation sets, respectively. Calibration plots showed good model calibration, and decision curve analysis confirmed the clinical utility of both models.
CONCLUSION: The 2 preoperative US-based risk-scoring systems effectively predicted ALN metastasis and the number of metastatic ALNs, aiding clinicians in assessing the risk of ALN involvement in breast cancer patients.