Dataset Information

Changes in Serum Growth Factors during Lenvatinib Predict the Post Progressive Survival in Patients with Unresectable Hepatocellular Carcinoma.

ABSTRACT: Serum growth factor changes and their effect on prognosis during lenvatinib for unresectable hepatocellular carcinoma (HCC) remain underexplored. The sequential changes in serum growth factors during lenvatinib for unresectable HCC were evaluated in 58 patients using complete clinical data, and preserved serum was used to investigate changes in FGF-19, ANG-2, HGF, VEGF, and EGF. Patients with a complete response (CR), partial response (PR), and stable disease (SD) were evaluated for growth factor changes between the best response and progressive disease (PD) points, classified based on these changes, and evaluated by post progression survival (PPS). A total of 8, 24, 18, and 8 patients showed CR, PR, SD, and PD, respectively. Multivariate analysis revealed that age, relative dose intensity, and baseline ANG-2 were significantly associated with treatment response. Growth factor changes between the best response and PD points revealed that patients could be classified into four groups based on the EGF, ANG-2, and HGF changes. Although patient characteristics at baseline and PD, their response to lenvatinib, and PFS were similar among those groups, patients with an increase in all growth factors had significantly shorter PPS (median PPS was 553, 323, and 316 versus 173 days in groups 1-4 p = 0.032). We revealed that the evaluation of the changes in growth factors during lenvatinib could predict PPS.

SUBMITTER: Yang Z

PROVIDER: S-EPMC8750627 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Changes in Serum Growth Factors during Lenvatinib Predict the Post Progressive Survival in Patients with Unresectable Hepatocellular Carcinoma.

Yang Zijian Z Suda Goki G Maehara Osamu O Ohara Masatsugu M Yoshida Sonoe S Hosoda Shunichi S Kimura Megumi M Kubo Akinori A Tokuchi Yoshimasa Y Fu Qingjie Q Yamada Ren R Kitagataya Takashi T Suzuki Kazuharu K Kawagishi Naoki N Nakai Masato M Sho Takuya T Natsuizaka Mitsuteru M Morikawa Kenichi K Ogawa Koji K Ohnishi Shunsuke S Sakamoto Naoya N

Cancers 20220104 1

Serum growth factor changes and their effect on prognosis during lenvatinib for unresectable hepatocellular carcinoma (HCC) remain underexplored. The sequential changes in serum growth factors during lenvatinib for unresectable HCC were evaluated in 58 patients using complete clinical data, and preserved serum was used to investigate changes in FGF-19, ANG-2, HGF, VEGF, and EGF. Patients with a complete response (CR), partial response (PR), and stable disease (SD) were evaluated for growth facto ...[more]

PMID: 35008398

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Project description:BackgroundLenvatinib 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.MethodsPatients 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.ResultsA 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.ConclusionsA 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.

Dataset Information

Changes in Serum Growth Factors during Lenvatinib Predict the Post Progressive Survival in Patients with Unresectable Hepatocellular Carcinoma.

Publications

Changes in Serum Growth Factors during Lenvatinib Predict the Post Progressive Survival in Patients with Unresectable Hepatocellular Carcinoma.

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