Purpose: We aimed to construct machine learning (ML) radiomics models to predict response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC). Experimental Design: Patients with HCC receiving lenvatinib monotherapy at three institutions were retrospectively identified and assigned to training and external validation cohorts. Tumor response after initiation of lenvatinib was evaluated. Radiomics features were extracted from contrast-enhanced CT images. The K-means clustering algorithm was used to distinguish radiomics-based subtypes. Ten ML radiomics models were constructed and internally validated by 10-fold cross-validation. These models were subsequently verified in an external validation cohort. Results: A total of 109 patients were identified for analysis, namely, 74 in the training cohort and 35 in the external validation cohort. Thirty-two patients showed partial response, 33 showed stable disease, and 44 showed progressive disease. The overall response rate (ORR) was 29.4%, and the disease control rate was 59.6%. A total of 224 radiomics features were extracted, and 25 significant features were identified for further analysis. Two distant radiomics-based subtypes were identified by K-means clustering, and subtype 1 was associated with a higher ORR and longer progression-free survival (PFS). Among the 10 ML algorithms, AutoGluon displayed the highest predictive performance (AUC = 0.97), which was relatively stable in the validation cohort (AUC = 0.93). Kaplan–Meier analysis showed that responders had a better overall survival [HR = 0.21; 95% confidence interval (CI): 0.12–0.36; P < 0.001] and PFS (HR = 0.14; 95% CI: 0.09–0.22; P < 0.001) than nonresponders. Conclusions: Valuable ML radiomics models were constructed, with favorable performance in predicting the response to lenvatinib monotherapy for unresectable HCC.

Background: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. Methods: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4–12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model. Results: A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61–0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level. Conclusions: A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

The demand-oriented road improvement may cause inconsistency between roads of same type in terms of traffic channelization and control, and thus may reduce self-explainability of the roads. In this study, situation awareness is employed as theoretical foundation to evaluate the self-explainability of demand-oriented road improvement. Ten drivers were recruited to conduct a naturalistic study and performed a verbal commentary while driving on two roads of same type in Beijing. The one, which kept the initial design, acted as a control condition. Another one, which was channelized and controlled according to traffic demand, acted as the experimental condition. Then the drivers’ situation awareness was modeled using semantic networks. The content and structure analysis of these networks were analyzed. The results show that the former road is richer in situation awareness and has a higher self-explainability. The findings suggest that the demand-oriented road improvement is not beneficial to road safety due to poor self-explainability.

Essential scripts for each model and the specific parameters of the models.

Baseline characteristics between two different radiomics-based subtypes for unresectable HCC treated with lenvatinib monotherapy.