PO.BCS02.04 · 生物信息与计算
用于预测不可切除肝细胞癌中基于lenvatinib的治疗方案应答和结局的无创人工智能系统
Noninvasive artificial intelligence system for prediction of lenvatinib based therapeutic regimens response and outcome in unresectable hepatocellular carcinoma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
目的：lenvatinib是不可切除肝细胞癌（uHCC）的一线治疗选择，临床实践中采用四种既定的治疗方案。然而，治疗应答率仍不理想，仅有一部分患者获得有意义的临床获益。我们假设反映肿瘤血管化和微环境异质性的影像组学特征可增强治疗应答预测。这项多中心回顾性-前瞻性混合研究旨在开发和验证影像组学深度学习模型（VALIANT），用于uHCC患者的个体化治疗应答分层。
方法：本研究纳入来自四家机构、经组织学或影像学确诊为不可切除疾病的435例uHCC患者（2018-2024年）。从治疗开始前采集的增强CT影像的动脉期和静脉期提取影像组学特征。采用迁移学习策略构建了八个VALIANT模型，通过五折交叉验证和外部验证队列以受试者工作特征曲线下面积（AUC）评估性能。此外，一个由16例患者组成的前瞻性验证队列根据预测评分最高的方案接受治疗推荐，并进行后续应答评估。
结果：159例患者（37%应答率）实现部分缓解（PR）。八个VALIANT模型表现稳健，平均AUC为0.92。基于动脉期的TPI模型显示出更优的判别能力（训练AUC 0.95；外部验证AUC 0.92），分类指标均衡（敏感性0.90，特异性0.84，准确率0.95），表明在各机构间具有出色的泛化性。所有VALIANT模型均显著将患者分层为应答/无应答不同组别，且总生存结局存在差异（log-rank P<0.01）。在前瞻性队列中，16例患者中有13例（81.3%）达到了预测的应答类别，AI预测的最优方案与实际临床应答之间的一致性为87.5%。
结论：VALIANT模型中深度学习影像组学与肿瘤表型特征的整合显著增强了对uHCC中基于lenvatinib治疗的应答预测。该临床适用的AI系统能够对四种基于lenvatinib的方案进行比较评估，促进个体化治疗选择，并有可能通过精准医学方法改善患者结局。
查看英文原文 English abstract
Purpose: Lenvatinib represents a first-line therapeutic option for unresectable hepatocellular carcinoma (uHCC), with four established treatment regimens utilized in clinical practice. However, treatment response rates remain suboptimal, with only a subset of patients achieving meaningful clinical benefit. We hypothesized that radiomics features reflecting tumor vascularization and microenvironmental heterogeneity could enhance treatment response prediction. This multi-center retrospective-prospective hybrid study aimed to develop and validate radiomic deep learning models (VALIANT) for personalized treatment response stratification in uHCC patients.
Methods: This study enrolled 435 uHCC patients (2018-2024) from four institutions with histologically or radiologically confirmed unresectable disease. Radiomic features were extracted from arterial and venous phases of contrast-enhanced CT imaging acquired prior to treatment initiation. Eight VALIANT models were constructed using transfer learning strategies, with performance evaluated by area under the receiver operating characteristic curve (AUC) through five-fold cross-validation and external validation cohorts. Additionally, a prospective validation cohort of 16 patients received treatment recommendations based on the highest-scoring predicted regimen, with subsequent response assessment.
Results: Partial response (PR) was achieved in 159 patients (37% response rate). The eight VALIANT models demonstrated robust performance with a mean AUC of 0.92. The arterial phase-based TPI model exhibited superior discriminative ability (training AUC 0.95; external validation AUC 0.92) with balanced classification metrics (sensitivity 0.90, specificity 0.84, accuracy 0.95), indicating excellent generalizability across institutions. All VALIANT models significantly stratified patients into distinct response/non-response groups with divergent overall survival outcomes (log-rank P < 0.01). In the prospective cohort, 13 of 16 patients (81.3%) achieved the predicted response category, with 87.5% concordance between AI-predicted optimal regimen and actual clinical response.
Conclusion: The integration of deep learning radiomics and tumor phenotypic features in VALIANT models significantly enhances treatment response prediction for lenvatinib-based therapy in uHCC. This clinically applicable AI system enables comparative assessment of four lenvatinib-based regimens, facilitating personalized therapeutic selection and potentially improving patient outcomes through precision medicine approaches.
利益披露 Disclosure
B. Chen, None..
Y. Gan, None..
E. Zou, None.