Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, China.
Department of Radiology, Guizhou Provincial People's Hospital, No. 83 East Zhongshan Road, Guiyang, 550002, Guizhou, China.
Radiol Med. 2024 Nov;129(11):1597-1610. doi: 10.1007/s11547-024-01890-z. Epub 2024 Oct 14.
Due to heterogeneity of molecular biology and microenvironment, therapeutic efficacy varies among hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) and tyrosine kinase inhibitors (TKIs). We examined combined models using clinicoradiological characteristics, mutational burden of signaling pathways, and radiomics features to predict survival prognosis.
Two cohorts comprising 111 patients with HCC were used to build prognostic models. The training and test cohorts included 78 and 33 individuals, respectively. Mutational burden was calculated based on 17 cancer-associated signaling pathways. Radiomic features were extracted and selected from computed tomography images using a pyradiomics system. Models based on clinicoradiological indicators, mutational burden, and radiomics score (rad-score) were built to predict overall survival (OS) and progression-free survival (PFS).
Eastern Cooperative Oncology Group performance status, Child-Pugh class, peritumoral enhancement, PI3K_AKT and hypoxia mutational burden, and rad-score were used to create a combined model predicting OS. C-indices were 0.805 (training cohort) and 0.768 (test cohort). The areas under the curve (AUCs) were 0.889, 0.900, and 0.917 for 1-year, 2-year, and 3-year OS, respectively. To predict PFS, alpha-fetoprotein level, tumor enhancement pattern, hypoxia and receptor tyrosine kinase mutational burden, and rad-score were used. C-indices were 0.782 (training cohort) and 0.766 (test cohort). AUCs were 0.885 and 0.925 for 6-month and 12-month PFS, respectively. Calibration and decision curve analyses supported the model's accuracy and clinical potential.
The nomogram models are hopeful to predict OS and PFS in patients with intermediate-advanced HCC treated with TACE plus TKIs, offering a promising tool for treatment decisions and monitoring patient progress.
由于分子生物学和微环境的异质性，接受经导管动脉化疗栓塞 (TACE) 和酪氨酸激酶抑制剂 (TKI) 治疗的肝细胞癌 (HCC) 患者的治疗效果存在差异。我们使用临床影像学特征、信号通路突变负担和放射组学特征来构建联合模型，以预测生存预后。
使用包含 111 名 HCC 患者的两个队列来构建预测模型。训练队列和测试队列分别包含 78 名和 33 名患者。根据 17 个癌症相关信号通路计算突变负担。使用 pyradiomics 系统从 CT 图像中提取并选择放射组学特征。基于临床影像学指标、突变负担和放射组学评分（rad-score）构建模型，以预测总生存（OS）和无进展生存（PFS）。
东部肿瘤协作组表现状态、Child-Pugh 分级、肿瘤周围增强、PI3K_AKT 和缺氧突变负担以及 rad-score 用于创建一个联合模型来预测 OS。C 指数分别为 0.805（训练队列）和 0.768（测试队列）。1 年、2 年和 3 年 OS 的曲线下面积（AUC）分别为 0.889、0.900 和 0.917。为了预测 PFS，使用甲胎蛋白水平、肿瘤增强模式、缺氧和受体酪氨酸激酶突变负担以及 rad-score。C 指数分别为 0.782（训练队列）和 0.766（测试队列）。6 个月和 12 个月 PFS 的 AUC 分别为 0.885 和 0.925。校准和决策曲线分析支持该模型的准确性和临床潜力。
该列线图模型有望预测接受 TACE 联合 TKI 治疗的中晚期 HCC 患者的 OS 和 PFS，为治疗决策和监测患者进展提供了一种有前途的工具。
