Cancer medicine · 2026
Visualized Clinical-Radiomics Model Based on Non-Contrast Computed Tomography for Predicting Efficacy of Surufatinib in Hepatic Metastases of Neuroendocrine Neoplasms.
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Feng Miaomiao, Zhao Man, Duan Xiaoling, Hou Jiaojiao, Wang Qi, Yin Fei
Аннотация
BACKGROUND: Hepatic metastatic neuroendocrine neoplasms (HM-NENs) have few treatment biomarkers and low survival rates. We created a clinical-radiomics fusion model based on non-contrast computed tomography (NCCT) to predict Surufatinib efficacy in HM-NENs. We presented it as a nomogram, meeting unmet requirements in precision medicine.
METHODS: This retrospective study included 76 HM-NEN patients (131 hepatic metastases) treated with Surufatinib. Regions of interest (ROI) were manually segmented, and the best response to Surufatinib was decided based on Modified Response Evaluation Criteria in Solid Tumor (mRECIST). Radiomics features were extracted from the pretreatment NCCT. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select radiomics features and calculate a Radiomics score (Radscore). Multivariable logistic regression analysis was utilized to create the clinical-radiomics fusion model, which included clinical characteristics and Radscore and was displayed as a nomogram. The area under the receiver operating characteristic curve (ROC) was used to assess model performance, and internal validation was done using the bootstrap resampling approach.
RESULTS: After multivariate logistic regression analysis, the Radscore, the diameter of hepatic metastasis, the number of hepatic metastases, and extrahepatic metastasis were included as predictors in the final model. The area under the curve (AUC) of the clinical-radiomics fusion model to predict the response of Surufatinib of HM-NENs was 0.926 (95% confidence interval [CI]: 0.881-0.971). The AUC verified by bootstrap was 0.926 (95% CI: 0.880-0.966), indicating a good performance of the fusion model.
CONCLUSION: The clinical-radiomics fusion model can effectively identify patients with HM-NENs sensitive to Surufatinib therapy. The nomogram provides clinicians with a convenient and dependable tool for decision-making.
METHODS: This retrospective study included 76 HM-NEN patients (131 hepatic metastases) treated with Surufatinib. Regions of interest (ROI) were manually segmented, and the best response to Surufatinib was decided based on Modified Response Evaluation Criteria in Solid Tumor (mRECIST). Radiomics features were extracted from the pretreatment NCCT. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select radiomics features and calculate a Radiomics score (Radscore). Multivariable logistic regression analysis was utilized to create the clinical-radiomics fusion model, which included clinical characteristics and Radscore and was displayed as a nomogram. The area under the receiver operating characteristic curve (ROC) was used to assess model performance, and internal validation was done using the bootstrap resampling approach.
RESULTS: After multivariate logistic regression analysis, the Radscore, the diameter of hepatic metastasis, the number of hepatic metastases, and extrahepatic metastasis were included as predictors in the final model. The area under the curve (AUC) of the clinical-radiomics fusion model to predict the response of Surufatinib of HM-NENs was 0.926 (95% confidence interval [CI]: 0.881-0.971). The AUC verified by bootstrap was 0.926 (95% CI: 0.880-0.966), indicating a good performance of the fusion model.
CONCLUSION: The clinical-radiomics fusion model can effectively identify patients with HM-NENs sensitive to Surufatinib therapy. The nomogram provides clinicians with a convenient and dependable tool for decision-making.