Su Ke, Liu Xin, Zeng Yue-Can, Xu Junnv, Li Han, Wang Heran, Du Shanshan, Wang Huadong, Yue Jinbo, Yin Yong, Li Zhenjiang
Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China.
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, People's Republic of China.
J Hepatocell Carcinoma. 2025 May 9;12:933-947. doi: 10.2147/JHC.S521378. eCollection 2025.
This study was conducted to assess the efficacy and safety of magnetic resonance (MR)-guided hypofractionated radiotherapy in patients with unresectable hepatocellular carcinoma (HCC). Machine learning-based radiomics was utilized to predict responses in these patients.
This retrospective study included 118 hCC patients who received MR-guided hypofractionated radiotherapy. The primary study endpoint was the objective response rate (ORR). Radiomics features were based on the gross tumor volume (GTV). K-means clustering was performed to differentiate cancer subtypes based on radiomics. Nine radiomics-utilizing machine learning models were built and validated internally through 5-fold cross-validation.
The ORR, median progression-free survival (mPFS), and median overall survival (mOS) were 54.4%, 21.7 months, and 40.7 months, respectively. No patient experienced Grade 3/4 adverse events. 1130 radiomics features were extracted from the GTV, of which 7 were included for further analysis. K-means clustering identified 2 subtypes based on the selected features. Subtype 1 had significantly higher response, longer mPFS, and longer mOS than Subtype 2. In both internal and external validations, the multi-layer perceptron (MLP) model demonstrated superior predictive performance for response, achieving a receiver operating characteristic-area under the curve (ROC-AUC) of 0.804 and 0.842, respectively.
MR-guided radiotherapy was proven to be effective and safe for HCC. The machine learning radiomics model developed in this study could accurately predict the response of radiotherapy-treated inoperable HCC.
本研究旨在评估磁共振（MR）引导下的大分割放疗对不可切除肝细胞癌（HCC）患者的疗效和安全性。利用基于机器学习的放射组学预测这些患者的反应。
这项回顾性研究纳入了118例接受MR引导下大分割放疗的HCC患者。主要研究终点是客观缓解率（ORR）。放射组学特征基于大体肿瘤体积（GTV）。进行K均值聚类以根据放射组学区分癌症亚型。构建了9个利用放射组学的机器学习模型，并通过5折交叉验证进行内部验证。
ORR、中位无进展生存期（mPFS）和中位总生存期（mOS）分别为54.4%、21.7个月和40.7个月。没有患者发生3/4级不良事件。从GTV中提取了1130个放射组学特征，其中7个被纳入进一步分析。K均值聚类根据选定特征确定了2个亚型。亚型1的反应明显更高，mPFS和mOS比亚型2更长。在内部和外部验证中，多层感知器（MLP）模型在反应预测性能方面均表现出色，曲线下面积（ROC-AUC）分别为0.804和0.842。
MR引导下的放疗被证明对HCC有效且安全。本研究中开发的机器学习放射组学模型可以准确预测放疗治疗的不可切除HCC的反应。
