Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma
| العنوان: | Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma |
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| المؤلفون: | Jun Ma, Zhiyuan Bo, Zhengxiao Zhao, Jinhuan Yang, Yan Yang, Haoqi Li, Yi Yang, Jingxian Wang, Qing Su, Juejin Wang, Kaiyu Chen, Zhengping Yu, Yi Wang, Gang Chen |
| المصدر: | Cancers Volume 15 Issue 3 Pages: 625 |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Cancer Research, machine learning, Oncology, treatment response, lenvatinib, transarterial chemoembolization, hepatocellular carcinoma, Shapley Additive exPlanation |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| تدمد: | 2072-6694 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e090385f0534e35a85e3d1b5f2a647cTest https://doi.org/10.3390/cancers15030625Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....9e090385f0534e35a85e3d1b5f2a647c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20726694 |
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