Kun Huang, Haiyan Ma, Haikuan Liu, Jia Yuan, Xiaolan He, Yuancheng Liu, Zhouli Zheng, Rongpin Wang
{"title":"基于mri的放射组学特征预测lenvatinib靶向治疗肝癌的疗效：一项回顾性队列研究。","authors":"Kun Huang, Haiyan Ma, Haikuan Liu, Jia Yuan, Xiaolan He, Yuancheng Liu, Zhouli Zheng, Rongpin Wang","doi":"10.1007/s00432-025-06306-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Targeted therapy with lenvatinib is a preferred option for advanced hepatocellular carcinoma, however, predicting its efficacy remains challenging. This study aimed to build a nomogram integrating clinicoradiological indicators and radiomics features to predict the response to lenvatinib in patients with hepatocellular carcinoma.</p><p><strong>Methods: </strong>This study included 211 patients with hepatocellular carcinoma from two centers, who were allocated into the training (107 patients), internal test (46 patients) and external test set(58 patients). Radiomics features were extracted using a Pyradiomics-based system. Biopsy specimens were used for immunohistochemical staining of epidermal growth factor receptor. Risk factors for predicting treatment response were screened out to construct models by logistic regression analysis. The performance of models was evaluated with receiver operating characteristic and decision curve analyses.</p><p><strong>Results: </strong>A total of 9370 radiomics features were extracted from five sequences. Subsequently, seven radiomics features were identified for modeling. Intratumoral vessel and expression level of epidermal growth factor receptor were independent risk factors for predicting treatment response to lenvatinib and were used to build a clinical model. No difference was found in predicting performance between clinical model and radiomics model. The combined model, integrating intratumoral vessel, epidermal growth factor receptor and radiomics features, had better predicting performance with areas under the receiver operating characteristic curve of 0.908, 0.877, and 0.870 for the training, internal test and external test sets, respectively.</p><p><strong>Conclusion: </strong>This study underscored the significant potential of radiomics features combined with clinicoradiological indicators in the prediction of treatment response to lenvatinib in patients with hepatocellular carcinoma.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":"151 9","pages":"251"},"PeriodicalIF":2.8000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422998/pdf/","citationCount":"0","resultStr":"{\"title\":\"MRI-based radiomics signatures for predicting the efficacy of targeted therapy with lenvatinib in hepatocellular carcinoma: a retrospective cohort study.\",\"authors\":\"Kun Huang, Haiyan Ma, Haikuan Liu, Jia Yuan, Xiaolan He, Yuancheng Liu, Zhouli Zheng, Rongpin Wang\",\"doi\":\"10.1007/s00432-025-06306-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Targeted therapy with lenvatinib is a preferred option for advanced hepatocellular carcinoma, however, predicting its efficacy remains challenging. This study aimed to build a nomogram integrating clinicoradiological indicators and radiomics features to predict the response to lenvatinib in patients with hepatocellular carcinoma.</p><p><strong>Methods: </strong>This study included 211 patients with hepatocellular carcinoma from two centers, who were allocated into the training (107 patients), internal test (46 patients) and external test set(58 patients). Radiomics features were extracted using a Pyradiomics-based system. Biopsy specimens were used for immunohistochemical staining of epidermal growth factor receptor. Risk factors for predicting treatment response were screened out to construct models by logistic regression analysis. The performance of models was evaluated with receiver operating characteristic and decision curve analyses.</p><p><strong>Results: </strong>A total of 9370 radiomics features were extracted from five sequences. Subsequently, seven radiomics features were identified for modeling. Intratumoral vessel and expression level of epidermal growth factor receptor were independent risk factors for predicting treatment response to lenvatinib and were used to build a clinical model. No difference was found in predicting performance between clinical model and radiomics model. The combined model, integrating intratumoral vessel, epidermal growth factor receptor and radiomics features, had better predicting performance with areas under the receiver operating characteristic curve of 0.908, 0.877, and 0.870 for the training, internal test and external test sets, respectively.</p><p><strong>Conclusion: </strong>This study underscored the significant potential of radiomics features combined with clinicoradiological indicators in the prediction of treatment response to lenvatinib in patients with hepatocellular carcinoma.</p>\",\"PeriodicalId\":15118,\"journal\":{\"name\":\"Journal of Cancer Research and Clinical Oncology\",\"volume\":\"151 9\",\"pages\":\"251\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422998/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cancer Research and Clinical Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00432-025-06306-7\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cancer Research and Clinical Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00432-025-06306-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
MRI-based radiomics signatures for predicting the efficacy of targeted therapy with lenvatinib in hepatocellular carcinoma: a retrospective cohort study.
Purpose: Targeted therapy with lenvatinib is a preferred option for advanced hepatocellular carcinoma, however, predicting its efficacy remains challenging. This study aimed to build a nomogram integrating clinicoradiological indicators and radiomics features to predict the response to lenvatinib in patients with hepatocellular carcinoma.
Methods: This study included 211 patients with hepatocellular carcinoma from two centers, who were allocated into the training (107 patients), internal test (46 patients) and external test set(58 patients). Radiomics features were extracted using a Pyradiomics-based system. Biopsy specimens were used for immunohistochemical staining of epidermal growth factor receptor. Risk factors for predicting treatment response were screened out to construct models by logistic regression analysis. The performance of models was evaluated with receiver operating characteristic and decision curve analyses.
Results: A total of 9370 radiomics features were extracted from five sequences. Subsequently, seven radiomics features were identified for modeling. Intratumoral vessel and expression level of epidermal growth factor receptor were independent risk factors for predicting treatment response to lenvatinib and were used to build a clinical model. No difference was found in predicting performance between clinical model and radiomics model. The combined model, integrating intratumoral vessel, epidermal growth factor receptor and radiomics features, had better predicting performance with areas under the receiver operating characteristic curve of 0.908, 0.877, and 0.870 for the training, internal test and external test sets, respectively.
Conclusion: This study underscored the significant potential of radiomics features combined with clinicoradiological indicators in the prediction of treatment response to lenvatinib in patients with hepatocellular carcinoma.
期刊介绍：
The "Journal of Cancer Research and Clinical Oncology" publishes significant and up-to-date articles within the fields of experimental and clinical oncology. The journal, which is chiefly devoted to Original papers, also includes Reviews as well as Editorials and Guest editorials on current, controversial topics. The section Letters to the editors provides a forum for a rapid exchange of comments and information concerning previously published papers and topics of current interest. Meeting reports provide current information on the latest results presented at important congresses.
The following fields are covered: carcinogenesis - etiology, mechanisms; molecular biology; recent developments in tumor therapy; general diagnosis; laboratory diagnosis; diagnostic and experimental pathology; oncologic surgery; and epidemiology.
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