Radiation oncology (London, England) · 2026
Personalizing adjuvant radiotherapy decisions in breast cancer: an externally test MRI radiomics approach for recurrence risk stratification.
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Long Man, Xie Yu, Ruan Jinqiu, Liao Rui, Zheng Xia, Gao Songliang, Mao Keyu, Dong Guili, Yang Guangjun, Li Zhuolin, Li Zhenhui
Аннотация
PURPOSE: To develop a radiomics signature based on preoperative MRI to predict recurrence-free survival (RFS) and which patients will benefit from adjuvant radiotherapy in breast cancer patients.
EXPERIMENTAL DESIGN: 771 eligible patients from two independent centers were collected. The training set (n = 504) obtained was from the Yunnan Cancer Hospital, and the external test set (n = 267) was obtained from the Duke Breast Cancer MRI Study. Pearson correlation analysis and clustering were used to construct the radiomics signature Rad-score. Based on the best cut-off value, the patients were divided into a high-risk group and a low-risk group, and the prognostic value was evaluated. A combined model nomogram was developed based on the radiomics features incorporating independent clinicopathological risk factors. The combined model's performance was assessed regarding its discrimination, calibration, and clinical usefulness. Kaplan-Meier analysis was used to identify patients who benefited from adjuvant radiotherapy.
RESULTS: The high-risk group was significantly associated with worse RFS in the training and test sets (p = 0.001 and 0.048). The combination model can accurately predict BC recurrence risk (AUC, 0.752; 95% confidence interval [CI], 0.698-0.807). Additionally, adjuvant radiotherapy was not associated with the outcome of high-risk patients (training and test sets: p = 0.665 and 0.903, respectively). In the low-risk group, adjuvant radiotherapy was significantly associated with RFS (training and test sets: p = 0.004 and P＜0.001).
CONCLUSIONS: The Rad-score can effectively predict the prognosis of patients with breast cancer, and identify high-risk patients who do not benefit from adjuvant radiotherapy.
CLINICAL TRIAL NUMBER: Not applicable.
EXPERIMENTAL DESIGN: 771 eligible patients from two independent centers were collected. The training set (n = 504) obtained was from the Yunnan Cancer Hospital, and the external test set (n = 267) was obtained from the Duke Breast Cancer MRI Study. Pearson correlation analysis and clustering were used to construct the radiomics signature Rad-score. Based on the best cut-off value, the patients were divided into a high-risk group and a low-risk group, and the prognostic value was evaluated. A combined model nomogram was developed based on the radiomics features incorporating independent clinicopathological risk factors. The combined model's performance was assessed regarding its discrimination, calibration, and clinical usefulness. Kaplan-Meier analysis was used to identify patients who benefited from adjuvant radiotherapy.
RESULTS: The high-risk group was significantly associated with worse RFS in the training and test sets (p = 0.001 and 0.048). The combination model can accurately predict BC recurrence risk (AUC, 0.752; 95% confidence interval [CI], 0.698-0.807). Additionally, adjuvant radiotherapy was not associated with the outcome of high-risk patients (training and test sets: p = 0.665 and 0.903, respectively). In the low-risk group, adjuvant radiotherapy was significantly associated with RFS (training and test sets: p = 0.004 and P＜0.001).
CONCLUSIONS: The Rad-score can effectively predict the prognosis of patients with breast cancer, and identify high-risk patients who do not benefit from adjuvant radiotherapy.
CLINICAL TRIAL NUMBER: Not applicable.