Department of Interventional Therapy, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China.
Department of Infectious DiseaseThe Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, People's Republic of China.
J Cancer Res Clin Oncol. 2023 Jul;149(8):5181-5192. doi: 10.1007/s00432-022-04467-3. Epub 2022 Nov 12.
To construct and validate a combined nomogram model based on magnetic resonance imaging (MRI) radiomics and Albumin-Bilirubin (ALBI) score to predict therapeutic response in unresectable hepatocellular carcinoma (HCC) patients treated with hepatic arterial infusion chemotherapy (HAIC).
The retrospective study was conducted on 112 unresectable HCC patients who underwent pretherapeutic MRI examinations. Patients were randomly divided into training (n = 79) and validation cohorts (n = 33). A total of 396 radiomics features were extracted from the volume of interest of the primary lesion by the Artificial Kit software. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal radiomic features. After feature selection, three models, including the clinical, radiomics, and combined models, were developed to predict the non-response of unresectable HCC to HAIC treatment. The performance of these models was evaluated by the receiver operating characteristic curve. According to the most efficient model, a nomogram was established, and the performance of which was also assessed by calibration curve and decision curve analysis. Kaplan-Meier curve and log-rank test were performed to evaluate the Progression-free survival (PFS).
Using the LASSO regression, we ultimately selected three radiomics features from T2-weighted images to construct the radiomics score (Radscore). Only the ALBI score was an independent factor associated with non-response in the clinical model (P = 0.033). The combined model, which included the ALBI score and Radscore, achieved better performance in the prediction of non-response, with an AUC of 0.79 (95% CI 0.68-0.90) and 0.75 (95% CI 0.58-0.92) in the training and validation cohorts, respectively. The nomogram based on the combined model also had good discrimination and calibration (P = 0.519 for the training cohort and P = 0.389 for the validation cohort). The Kaplan-Meier analysis also demonstrate that the high-score patients had significantly shorter PFS than the low-score patients (P = 0.031) in the combined model, with median PFS 6.0 vs 9.0 months.
The nomogram based on the combined model consisting of MRI radiomics and ALBI score could be used as a biomarker to predict the therapeutic response of unresectable HCC after HAIC.
构建并验证基于磁共振成像（MRI）放射组学和白蛋白-胆红素（ALBI）评分的联合列线图模型，以预测接受肝动脉灌注化疗（HAIC）治疗的不可切除肝细胞癌（HCC）患者的治疗反应。
本回顾性研究纳入了 112 例接受术前 MRI 检查的不可切除 HCC 患者。患者被随机分为训练队列（n=79）和验证队列（n=33）。通过人工套件软件从原发性病变的感兴趣区域提取了 396 个放射组学特征。应用最小绝对值收缩和选择算子（LASSO）回归来识别最优放射组学特征。特征选择后，建立了包括临床、放射组学和联合模型在内的三种模型，以预测不可切除 HCC 对 HAIC 治疗的无反应性。通过受试者工作特征曲线评估这些模型的性能。根据最有效的模型，建立了一个列线图，并通过校准曲线和决策曲线分析评估其性能。Kaplan-Meier 曲线和对数秩检验用于评估无进展生存期（PFS）。
通过 LASSO 回归，我们最终从 T2 加权图像中选择了三个放射组学特征来构建放射组学评分（Radscore）。只有 ALBI 评分是临床模型中与无反应相关的独立因素（P=0.033）。联合模型包括 ALBI 评分和 Radscore，在预测无反应方面表现出更好的性能，在训练和验证队列中的 AUC 分别为 0.79（95%CI 0.68-0.90）和 0.75（95%CI 0.58-0.92）。基于联合模型的列线图也具有良好的区分度和校准度（训练队列中 P=0.519，验证队列中 P=0.389）。Kaplan-Meier 分析还表明，在联合模型中，高分患者的无进展生存期明显短于低分组患者（P=0.031），中位无进展生存期为 6.0 个月 vs 9.0 个月。
基于 MRI 放射组学和 ALBI 评分的联合模型列线图可作为预测不可切除 HCC 患者 HAIC 治疗反应的生物标志物。
