Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026
Ultrasound-Based IHC4-Associated Radiomic Model for Predicting Late Recurrence in ER-Positive Breast Cancer: A Multicenter Retrospective Study.
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Quan MengYao, Wang ChangYan, Yuan Hong, Huang YunXia, Zhang YaFang, Zhou Jian-Hua, Peng YuLan, Chang Cai, Zhang Qi, Zhou ShiChong
Аннотация
OBJECTIVES: This study aimed to develop and validate an ultrasound-based IHC4-associated radiomic model for predicting late recurrence in ER-positive breast cancer and to evaluate its potential to support risk stratification and guide decisions on extended endocrine therapy.
METHODS: In this retrospective multicenter study, patients were divided into a training cohort, an internal validation cohort, and two external validation cohorts. Radiomic features associated with the immunohistochemical four-marker (IHC4) score were selected to construct a support vector machine (SVM)-based IHC4-associated radiomic model for generating an IHC4-associated radiomic score. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Late distant recurrence (DR) was defined as the primary endpoint, and progression-free survival (PFS) was defined as the secondary endpoint. The prognostic value of the score was further evaluated using Cox regression analysis.
RESULTS: A total of 523 patients were included in this study. Seven IHC4-associated radiomic features were selected to construct the IHC4-associated radiomic model. The model demonstrated consistent performance across cohorts, with AUCs of 0.81, 0.84, 0.79, and 0.81 in the training, internal validation, and two external validation cohorts, respectively. The IHC4-associated radiomic score stratified late DR risk across the training, internal validation, and external validation cohorts. In the secondary exploratory PFS analysis, the score remained associated with PFS in multivariable Cox analysis (HR = 4.246, 95% CI: 1.749-10.307, P = .001).
CONCLUSIONS: The IHC4-associated radiomic model may serve as a noninvasive biomarker for predicting prognosis in ER-positive breast cancer.
METHODS: In this retrospective multicenter study, patients were divided into a training cohort, an internal validation cohort, and two external validation cohorts. Radiomic features associated with the immunohistochemical four-marker (IHC4) score were selected to construct a support vector machine (SVM)-based IHC4-associated radiomic model for generating an IHC4-associated radiomic score. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Late distant recurrence (DR) was defined as the primary endpoint, and progression-free survival (PFS) was defined as the secondary endpoint. The prognostic value of the score was further evaluated using Cox regression analysis.
RESULTS: A total of 523 patients were included in this study. Seven IHC4-associated radiomic features were selected to construct the IHC4-associated radiomic model. The model demonstrated consistent performance across cohorts, with AUCs of 0.81, 0.84, 0.79, and 0.81 in the training, internal validation, and two external validation cohorts, respectively. The IHC4-associated radiomic score stratified late DR risk across the training, internal validation, and external validation cohorts. In the secondary exploratory PFS analysis, the score remained associated with PFS in multivariable Cox analysis (HR = 4.246, 95% CI: 1.749-10.307, P = .001).
CONCLUSIONS: The IHC4-associated radiomic model may serve as a noninvasive biomarker for predicting prognosis in ER-positive breast cancer.