Medical physics · 2026
Support vector machine-based MRI radiomics predict response to neoadjuvant therapy and progression-free-survival of Colorectal Liver Metastases patients.
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Li Zhuo-Fu, Guo Jia, Sun Chao, Tian Song, Ma Zi-Long, Ye Zhao-Xiang
Аннотация
BACKGROUND: The efficacy of neoadjuvant therapy (NAT) and surgical prognosis stratification in patients with colorectal cancer liver metastasis (CRLM) remain unclear.
PURPOSE: This study aims to develop and validate two radiomics models using baseline and delta radiomics features to predict the response to NAT and prognosis in CRLM.
METHODS: Baseline and Delta MRI radiomics features were extracted from 116 CRLM patients who underwent NAT combined with hepatectomy in the training set. Radiomics and clinical features were selected using Boruta algorithm to construct predictive models. In the external test set, the predictive performance of both models was evaluated using the area under the curve (AUC), decision curve analysis (DCA), calibration curves, and Brier score. Additionally, the delta model was compared with the clinical risk score (CRS).
RESULTS: The two models contain 6 and 4 variables, respectively, for predicting treatment response and progression-free survival (PFS). Both the response model and delta model demonstrated comparable predictive performance in the external test set (AUC = 0.806 for both). Furthermore, the delta model demonstrated superior performance to CRS in predicting PFS (AUC: 0.806 vs. 0.623).
CONCLUSIONS: Radiomics models based on baseline and delta MRI effectively predicted treatment response and postoperative PFS in CRLM patients, with the delta model showing superior accuracy for PFS prediction compared to CRS.
PURPOSE: This study aims to develop and validate two radiomics models using baseline and delta radiomics features to predict the response to NAT and prognosis in CRLM.
METHODS: Baseline and Delta MRI radiomics features were extracted from 116 CRLM patients who underwent NAT combined with hepatectomy in the training set. Radiomics and clinical features were selected using Boruta algorithm to construct predictive models. In the external test set, the predictive performance of both models was evaluated using the area under the curve (AUC), decision curve analysis (DCA), calibration curves, and Brier score. Additionally, the delta model was compared with the clinical risk score (CRS).
RESULTS: The two models contain 6 and 4 variables, respectively, for predicting treatment response and progression-free survival (PFS). Both the response model and delta model demonstrated comparable predictive performance in the external test set (AUC = 0.806 for both). Furthermore, the delta model demonstrated superior performance to CRS in predicting PFS (AUC: 0.806 vs. 0.623).
CONCLUSIONS: Radiomics models based on baseline and delta MRI effectively predicted treatment response and postoperative PFS in CRLM patients, with the delta model showing superior accuracy for PFS prediction compared to CRS.