Using preprocedural clinical factors and CT radiomics to predict hepatocellular carcinoma response to yttrium-90 resin microspheres selective internal radiation therapy: A real-world study in China.

Abstract

e16179 Background: To evaluate the predictive potential of preprocedural clinical factors and CT radiomic features for treatment response in hepatocellular carcinoma (HCC) patients after selective internal radiation therapy (SIRT) with yttrium-90 resin microspheres. Methods: A retrospective analysis was conducted on 112 HCC patients treated with SIRT at Tsinghua Chang Gung Hospital between September 2022 and June 2024, with at least three months of follow-up. Patients were divided into a training set (78) and a validation set (34) based on treatment time. Treatment response was assessed using the modified Response Evaluation in Solid Tumors (mRECIST) criteria, classifying complete or partial remission as objective response (OR) and stable or progressive disease as no response (NR). Clinical, laboratory, and radiomic data were collected. Radiomic features were extracted from arterial and portal-phase contrast-enhanced CT scans within two months pre-SIRT, normalized using Z-scores, and redundant features were removed via intraclass correlation coefficients and correlation analysis. Key features were selected through univariate logistic regression, least absolute shrinkage and selection operator (LASSO), variance inflation factor analysis, and stepwise regression. Using these features, a nomogram model was constructed with traditional logistic regression, alongside machine learning models, including logistic regression (LR), naive bayes (NB), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Model performance was evaluated by the area under the curve (AUC), and ROC curves were compared using the DeLong test. Results: Seven clinical features were selected. AUCs for the Nomogram, LR, NB, SVM, RF, XGBoost, and DNN models were 0.919, 0.915, 0.900, 0.953, 0.985, 0.979, and 0.901 in the training set, and 0.760, 0.760, 0.792, 0.757, 0.740, 0.802, and 0.774 in the validation set. Six radiomics features were selected, yielding training set AUCs of 0.927, 0.929, 0.888, 0.946, 1.000, 1.000, and 0.986, and validation set AUCs of 0.681, 0.670, 0.568, 0.611, 0.576, 0.608, and 0.663. In the combined analysis (5 radiomic and 2 clinical features), the AUCs were 0.943, 0.943, 0.914, 0.931, 1.000, 1.000, and 0.959 in the training set, and 0.736, 0.726, 0.623, 0.660, 0.679, 0.670, and 0.646 in the validation set. Machine learning models, particularly RF and XGBoost, outperformed traditional statistical models in the training set (p < 0.05), though no significant differences were observed in the validation set, where traditional models remained robust. Conclusions: Models integrating clinical and radiomic features, developed using statistical and machine learning algorithms, show promise for predicting response to SIRT in HCC patients.