在晚期肝细胞癌（HCC）患者中，利妥昔单抗加抗PD-1抗体的联合应用显示出潜在的抗肿瘤效果，但其临床效益仅限于部分患者。我们开发并验证了一种基于放射组学的模型，用于预测晚期HCC患者对该联合治疗的客观疗效。我们回顾性地纳入了来自中国9个中心的170例接受一线联合治疗（利妥昔单抗加抗PD-1抗体）的患者，其中124例和46例分别进入训练组和验证组。我们从术前对比增强MRI中提取了放射组学特征，经过特征选择后使用神经网络构建了临床病理学模型、放射组学模型和临床病理学-放射组学模型。我们评估了这些模型的性能，比较了放射组学特征与临床病理学特征之间的增量预测价值，并分析了放射组学特征与生存率之间的关系。 临床病理学模型在训练组和验证组中分别以0.748（95% CI：0.656-0.840）和0.702（95% CI：0.547-0.884）的AUC值，对客观疗效有适度的预测能力。放射组学模型在训练组和验证组中分别以0.886（95% CI：0.815-0.957）和0.820（95% CI：0.648-0.984）的AUC值，具有良好的校准和临床实用性，可预测疗效。放射组学特征相较于临床病理学特征具有增量预测价值，训练组和验证组中的净再分类指数分别为47.9%（p < 0.001）和41.5%（p = 0.025）。此外，放射组学特征与治疗开始后的总生存期和无进展生存期在训练组和验证组中均有关联，而修订的白蛋白-胆红素分级和中性粒细胞-淋巴细胞比值则无关。 利用术前MRI提取的放射组学特征可以预测不可切除或晚期HCC患者对联合治疗（利妥昔单抗加抗PD-1抗体）的个体化客观疗效，相较于临床病理学特征，具有增量预测价值，并与该联合方案开始后的总生存期和无进展生存期相关。本研究版权归作者所有，由S. Karger AG, Basel出版。

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 (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.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.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.Copyright © 2022 by The Author(s). Published by S. Karger AG, Basel.