Liang Liang, Pi Qiangfeng, Jiang Shuo, Zhou Jie, Singer Lauren, Cao Li
Department of Neurosurgery, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China.
Department of Neurology, Ezhou Central Hospital, Ezhou, China.
Transl Cancer Res. 2025 Aug 31;14(8):5002-5011. doi: 10.21037/tcr-2025-800. Epub 2025 Aug 13.
Glioma, which has a high degree of malignancy and mortality, is mainly treated by radiotherapy. Acute radiation-induced brain injury is one of the common complications of radiotherapy and can lead to brain herniation. Identifying risk of acute radiation-induced brain injury can facilitate the improvement of diagnostic and treatment strategies to ultimately improve patient outcomes. The purpose this study was to construct and validate a prediction model for acute radiation-induced brain injury in patients with glioma.
The data from 420 patients with glioma admitted to the Nanxishan Hospital of Guangxi Zhuang Autonomous Region from January 2020 to December 2024 were retrospectively collected as the training set, while the data from 180 patients with glioma treated at the 940th Hospital of Joint Logistics Support Force of PLA during the same period were collected as the validation set. The differences in the clinical characteristics of patients with acute brain injury (n=112) and non-brain injury (n=308) in the training set were analyzed, as were the risk factors of acute radiation-induced brain injury. According to the relevant risk factors, a prediction model for acute radiation-induced brain injury was constructed and validated in the validation set.
Age, diabetes, size of gross tumor volume, radiation dose of gross tumor volume, and concurrent chemotherapy were independent risk factors for acute radiation-induced brain injury in patients with glioma, with the relative risks being 1.060 [95% confidence interval (CI): 1.030-1.091], 3.080 (95% CI: 1.384-6.852), 1.075 (95% CI: 1.049-1.100), 1.241 (95% CI: 1.176-1.310), and 3.951 (95% CI: 1.877-8.317), respectively. The area under the receiver operating characteristic (ROC) curve of the training set was 0.907 (95% CI: 0.875-0.939), and the area under curve of the validation set was 0.913 (95% CI: 0.861-0.965). The Hosmer-Lemeshow goodness-of-fit test was conducted on the model in the validation set, with a Chi-squared value of 5.135 and a P value of 0.743.
Patients with glioma have a high incidence of acute radiation-induced brain injury during radiotherapy, which can lead to a poor prognosis. The model we developed demonstrated good efficacy and reliability for identifying risk of acute radiation-induced brain injury.
胶质瘤具有高度恶性和死亡率，主要通过放射治疗。急性放射性脑损伤是放射治疗的常见并发症之一，可导致脑疝。识别急性放射性脑损伤的风险有助于改进诊断和治疗策略，最终改善患者预后。本研究的目的是构建并验证胶质瘤患者急性放射性脑损伤的预测模型。
回顾性收集2020年1月至2024年12月在广西壮族自治区南溪山医院收治的420例胶质瘤患者的数据作为训练集，同时收集同期在中国人民解放军联勤保障部队第940医院治疗的180例胶质瘤患者的数据作为验证集。分析训练集中急性脑损伤患者（n = 112）和非脑损伤患者（n = 308）的临床特征差异，以及急性放射性脑损伤的危险因素。根据相关危险因素，构建急性放射性脑损伤的预测模型，并在验证集中进行验证。
年龄、糖尿病、肿瘤总体积大小、肿瘤总体积的放射剂量和同步化疗是胶质瘤患者急性放射性脑损伤的独立危险因素，相对风险分别为1.060 [95%置信区间（CI）：1.030 - 1.091]、3.080（95% CI：1.384 - 6.852）、1.075（95% CI：1.049 - 1.100）、1.241（95% CI：1.176 - 1.310）和3.951（95% CI：1.877 - 8.317）。训练集的受试者操作特征（ROC）曲线下面积为0.907（95% CI：0.875 - 0.939），验证集的曲线下面积为0.913（95% CI：0.861 - 0.965）。对验证集中的模型进行Hosmer-Lemeshow拟合优度检验，卡方值为5.135，P值为0.743。
胶质瘤患者在放射治疗期间急性放射性脑损伤的发生率较高，这可能导致预后不良。我们开发的模型在识别急性放射性脑损伤风险方面显示出良好的有效性和可靠性。
