{"title":"多中心研究:治疗前核磁共振成像中的肿瘤放射学特征预测晚期肝细胞癌患者对伦伐替尼加抗PD-1抗体的反应","authors":"Bin Xu, San-Yuan Dong, Xue-Li Bai, Tian-Qiang Song, Bo-Heng Zhang, Le-Du Zhou, Yong-Jun Chen, Zhi-Ming Zeng, Kui Wang, Hai-Tao Zhao, Na Lu, Wei Zhang, Xu-Bin Li, Su-Su Zheng, Guo Long, Yu-Chen Yang, Hua-Sheng Huang, Lan-Qing Huang, Yun-Chao Wang, Fei Liang, Xiao-Dong Zhu, Cheng Huang, Ying-Hao Shen, Jian Zhou, Meng-Su Zeng, Jia Fan, Sheng-Xiang Rao, Hui-Chuan Sun","doi":"10.1159/000528034","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients.</p><p><strong>Methods: </strong>Patients (<i>N</i> = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed.</p><p><strong>Results: </strong>The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (<i>p</i> < 0.001) and 41.5% (<i>p</i> = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not.</p><p><strong>Conclusion: </strong>Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.</p>","PeriodicalId":18156,"journal":{"name":"Liver Cancer","volume":"12 3","pages":"262-276"},"PeriodicalIF":11.6000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f5/2c/lic-0012-0262.PMC10433098.pdf","citationCount":"0","resultStr":"{\"title\":\"Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.\",\"authors\":\"Bin Xu, San-Yuan Dong, Xue-Li Bai, Tian-Qiang Song, Bo-Heng Zhang, Le-Du Zhou, Yong-Jun Chen, Zhi-Ming Zeng, Kui Wang, Hai-Tao Zhao, Na Lu, Wei Zhang, Xu-Bin Li, Su-Su Zheng, Guo Long, Yu-Chen Yang, Hua-Sheng Huang, Lan-Qing Huang, Yun-Chao Wang, Fei Liang, Xiao-Dong Zhu, Cheng Huang, Ying-Hao Shen, Jian Zhou, Meng-Su Zeng, Jia Fan, Sheng-Xiang Rao, Hui-Chuan Sun\",\"doi\":\"10.1159/000528034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients.</p><p><strong>Methods: </strong>Patients (<i>N</i> = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed.</p><p><strong>Results: </strong>The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (<i>p</i> < 0.001) and 41.5% (<i>p</i> = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not.</p><p><strong>Conclusion: </strong>Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.</p>\",\"PeriodicalId\":18156,\"journal\":{\"name\":\"Liver Cancer\",\"volume\":\"12 3\",\"pages\":\"262-276\"},\"PeriodicalIF\":11.6000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f5/2c/lic-0012-0262.PMC10433098.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Liver Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1159/000528034\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Liver Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000528034","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.
Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients.
Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed.
Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not.
Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.
期刊介绍:
Liver Cancer is a journal that serves the international community of researchers and clinicians by providing a platform for research results related to the causes, mechanisms, and therapy of liver cancer. It focuses on molecular carcinogenesis, prevention, surveillance, diagnosis, and treatment, including molecular targeted therapy. The journal publishes clinical and translational research in the field of liver cancer in both humans and experimental models. It publishes original and review articles and has an Impact Factor of 13.8. The journal is indexed and abstracted in various platforms including PubMed, PubMed Central, Web of Science, Science Citation Index, Science Citation Index Expanded, Google Scholar, DOAJ, Chemical Abstracts Service, Scopus, Embase, Pathway Studio, and WorldCat.