Xu Bin, Dong San-Yuan, Bai Xue-Li, Song Tian-Qiang, Zhang Bo-Heng, Zhou Le-Du, Chen Yong-Jun, Zeng Zhi-Ming, Wang Kui, Zhao Hai-Tao, Lu Na, Zhang Wei, Li Xu-Bin, Zheng Su-Su, Long Guo, Yang Yu-Chen, Huang Hua-Sheng, Huang Lan-Qing, Wang Yun-Chao, Liang Fei, Zhu Xiao-Dong, Huang Cheng, Shen Ying-Hao, Zhou Jian, Zeng Meng-Su, Fan Jia, Rao Sheng-Xiang, Sun Hui-Chuan
Department of Liver Surgery and Transplantation, Liver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China.
Liver Cancer. 2022 Nov 28;12(3):262-276. doi: 10.1159/000528034. eCollection 2023 Aug.
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.
Patients ( = 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.
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% ( < 0.001) and 41.5% ( = 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.
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.
乐伐替尼联合抗PD-1抗体在晚期肝细胞癌（HCC）患者中显示出有前景的抗肿瘤效果，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组学的模型，以预测晚期HCC患者对这种联合治疗的客观反应。
从9个中国中心回顾性纳入接受乐伐替尼联合抗PD-1抗体一线治疗的患者（n = 170）；分别有124例和46例进入训练队列和验证队列。从治疗前的对比增强MRI中提取放射组学特征。在特征选择后，使用神经网络构建临床病理、放射组学和临床病理-放射组学模型。评估模型的性能、放射组学特征相对于临床病理特征的增量预测价值以及放射组学特征与生存率之间的关系。
临床病理模型在训练队列和验证队列中分别适度预测客观反应，AUC为0.748（95%CI：0.656 - 0.840）和0.702（95%CI：0.547 - 0.884）。放射组学模型预测反应的AUC分别为0.886（95%CI：0.815 - 0.957）和0.820（95%CI：0.648 - 0.984），具有良好的校准和临床实用性。放射组学特征相对于临床病理特征的增量预测价值在训练队列和验证队列中分别通过净重新分类指数47.9%（P < 0.001）和41.5%（P = 0.025）得到证实。此外，放射组学特征在训练队列和验证队列中均与总生存期和无进展生存期相关，但改良的白蛋白-胆红素分级和中性粒细胞与淋巴细胞比值则不然。
从治疗前MRI中提取的放射组学特征可以预测不可切除或晚期HCC患者对乐伐替尼联合抗PD-1抗体联合治疗的个体化客观反应，相对于临床病理特征提供增量预测价值，并与开始这种联合治疗方案后的总生存期和无进展生存期相关。
Zotero 插件
