Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.

IF 11.6 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Liver Cancer Pub Date : 2022-11-28 eCollection Date: 2023-08-01 DOI:10.1159/000528034
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
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引用次数: 0

Abstract

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.

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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
简介在晚期肝细胞癌(HCC)患者中,伦伐替尼联合抗PD-1抗体显示出良好的抗肿瘤效果,但临床获益仅限于部分患者。我们开发并验证了一种基于放射学的模型,用于预测晚期肝细胞癌患者对这种联合疗法的客观反应:我们从中国的9个中心回顾性地招募了接受来伐替尼和抗PD-1抗体一线联合治疗的患者(170人),其中124人和46人分别进入训练组和验证组。从治疗前对比增强核磁共振成像中提取放射学特征。经过特征选择后,利用神经网络建立了临床病理学模型、放射学模型和临床病理学-放射学模型。对模型的性能、放射学特征与临床病理学特征相比的增量预测价值以及放射学特征与存活率之间的关系进行了评估:结果:临床病理模型可适度预测客观反应,训练组和验证组的AUC分别为0.748(95% CI:0.656-0.840)和0.702(95% CI:0.547-0.884)。放射学模型预测反应的AUC分别为0.886(95% CI:0.815-0.957)和0.820(95% CI:0.648-0.984),具有良好的校准性和临床实用性。放射学特征对临床病理学特征的增量预测价值得到了证实,在训练组和验证组中,净再分类指数分别为 47.9% (p < 0.001) 和 41.5% (p = 0.025)。此外,在训练组和验证组中,放射学特征与总生存期和无进展生存期相关,但改良白蛋白-胆红素分级和中性粒细胞-淋巴细胞比值与总生存期和无进展生存期无关:结论:从治疗前磁共振成像中提取的放射学特征可以预测不可切除或晚期HCC患者对来伐替尼加抗PD-1抗体联合治疗的个体化客观反应,比临床病理特征具有更高的预测价值,并且与联合治疗后的总生存期和无进展生存期相关。
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来源期刊
Liver Cancer
Liver Cancer Medicine-Oncology
CiteScore
20.80
自引率
7.20%
发文量
53
审稿时长
16 weeks
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.
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