Tao Ran, Lu Haohao, Dong Xiangjun, Ren Qian Qian, Fan Hongjie, Tang Zhaoming, Xia Xiangwen
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Front Oncol. 2025 Mar 13;15:1527108. doi: 10.3389/fonc.2025.1527108. eCollection 2025.
This study aimed to develop and evaluate the value of a nomogram based on quantitative MR signal intensity to predict response to combined systemic therapy of anti-angiogenesis and immune checkpoint inhibitor (ICI) in hepatocellular carcinoma (HCC) patients.
117 HCC patients who underwent the combined systemic treatment at a tertiary hospital between September 2020 and May 2024 were enrolled and divided into a development cohort (n = 82) and a validation cohort (n = 35). The predictive value of the relative signal intensity attenuation index (rSIAI) based on enhanced MR parameters and laboratory parameters on disease control was evaluated using receiver operating characteristic (ROC) curves, with the determination of optimal cut-off values (COVs) accomplished via Youden's index. Univariate and multivariable analyses were conducted to evaluate the association between COVs and disease control. The validity of the COVs was further confirmed through chi-square testing and calculation of Cramer's V coefficient (V). A nomogram was constructed based on the multivariable logistic regression model and evaluated for clinical applicability.
rSIAI from arterial to portal phase (rSI_ap) in combination with peripheral T-cell subset (CD4+) achieved the most accurate predictive performance for outcome compared to rSI_ap or CD4+ alone, with an area under the curve (AUC) of the ROC of 0.845 (95% CI, 0.748-0.915). A nomogram based on rSI_ap and CD4+ was constructed. Calibration and decision curve analyses confirmed the clinical relevance and value of the nomogram.
The nomogram based on rSI_ap has the potential to be a non-invasive tool for predicting disease control in advanced HCC patients who have received combined anti-angiogenesis and ICI therapies.
本研究旨在开发并评估基于定量磁共振信号强度的列线图在预测肝细胞癌（HCC）患者抗血管生成与免疫检查点抑制剂（ICI）联合全身治疗反应方面的价值。
纳入2020年9月至2024年5月在一家三级医院接受联合全身治疗的117例HCC患者，分为开发队列（n = 82）和验证队列（n = 35）。使用受试者工作特征（ROC）曲线评估基于增强磁共振参数和实验室参数的相对信号强度衰减指数（rSIAI）对疾病控制的预测价值，并通过约登指数确定最佳截断值（COV）。进行单因素和多因素分析以评估COV与疾病控制之间的关联。通过卡方检验和计算克莱默V系数（V）进一步确认COV的有效性。基于多因素逻辑回归模型构建列线图并评估其临床适用性。
与单独的rSI_ap或CD4⁺相比，动脉期至门静脉期的rSIAI（rSI_ap）与外周T细胞亚群（CD4⁺）相结合对结局具有最准确的预测性能，ROC曲线下面积（AUC）为0.845（95%CI，0.748 - 0.915）。构建了基于rSI_ap和CD4⁺的列线图。校准和决策曲线分析证实了列线图的临床相关性和价值。
基于rSI_ap的列线图有可能成为预测接受抗血管生成与ICI联合治疗的晚期HCC患者疾病控制情况的非侵入性工具。
