Department of Minimally Invasive Interventional Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Cancer for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong Province, People's Republic of China.
School of Basic Medicine, Fourth Military Medical University, No. 169 Changle West Rd, Xi'an, 710032, People's Republic of China.
BMC Gastroenterol. 2022 Mar 8;22(1):108. doi: 10.1186/s12876-022-02129-9.
Current study aims to determine the prognostic value of Multiparameter MRI after combined Lenvatinib and TACE therapy in patients with advanced unresectable hepatocellular carcinoma (HCC).
A total of 61 HCC patients with pre-treatment Multiparameter MRI in Sun Yat-sen University Cancer Center from January 2019 to March 2021 were recruited in the current study. All patients received combined Lenvatinib and TACE treatment. Potential clinical and imaging risk factors for disease progression were analyzed using Cox regression model. Each patient extracts signs from the following 7 sequences: T1WI, T1WI arterial phase, T1WI portal phase, T1WI delay phase, T2WI, DWI (b = 800), ADC.1782 quantitative 3D radiomic features were extracted for each sequence, A random forest algorithm is used to select the first 20 features by feature importance. 7 logit regression-based prediction model was built for seven sequences based on the selected features and fivefold cross validation was used to evaluate the performance of each model.
CR, PR, SD were reported in 14 (23.0%), 35 (57.4%) and 7 (11.5%) patients, respectively. In multivariate analysis, tumor number (hazard ratio, HR = 4.64, 95% CI 1.03-20.88), and arterial phase intensity enhancement (HR = 0.24, 95% CI 0.09-0.64; P = 0.004) emerged as independent risk factors for disease progression. In addition to clinical factors, the radiomics signature enhanced the accuracy of the clinical model in predicting disease progression, with an AUC of 0.71, a sensitivity of 0.99%, and a specificity of 0.95.
Radiomic signatures derived from pretreatment MRIs could predict response to combined Lenvatinib and TACE therapy. Furthermore, it can increase the accuracy of a combined model for predicting disease progression. In order to improve clinical outcomes, clinicians may use this to select an optimal treatment strategy and develop a personalized monitoring protocol.
本研究旨在探讨多参数 MRI 对仑伐替尼联合 TACE 治疗后不能切除的晚期肝细胞癌（HCC）患者的预后价值。
本研究共纳入 2019 年 1 月至 2021 年 3 月中山大学肿瘤防治中心 61 例接受仑伐替尼联合 TACE 治疗的 HCC 患者。所有患者均接受仑伐替尼联合 TACE 治疗。采用 COX 回归模型分析疾病进展的潜在临床和影像学危险因素。从以下 7 个序列中提取每个患者的征象：T1WI、T1WI 动脉期、T1WI 门静脉期、T1WI 延迟期、T2WI、DWI（b=800）、ADC。对每个序列提取 1782 个定量 3D 放射组学特征，使用随机森林算法根据特征重要性选择前 20 个特征。基于选定特征，为 7 个序列建立 7 个基于 logit 回归的预测模型，并使用五重交叉验证评估每个模型的性能。
14 例（23.0%）、35 例（57.4%）和 7 例（11.5%）患者分别报告完全缓解（CR）、部分缓解（PR）和疾病稳定（SD）。多变量分析显示，肿瘤数目（危险比，HR=4.64，95%可信区间 1.03-20.88）和动脉期强化程度（HR=0.24，95%可信区间 0.09-0.64；P=0.004）是疾病进展的独立危险因素。除临床因素外，放射组学特征提高了临床模型预测疾病进展的准确性，AUC 为 0.71，敏感性为 0.99%，特异性为 0.95。
多参数 MRI 对仑伐替尼联合 TACE 治疗的疗效有预测作用。此外，它可以提高联合模型预测疾病进展的准确性。为了改善临床结局，临床医生可能会使用这种方法来选择最佳的治疗策略，并制定个性化的监测方案。
