Zhou Qing, Ke Xiaoai, Man Jiangwei, Jiang Jian, Ren Jialiang, Xue Caiqiang, Zhang Bin, Zhang Peng, Zhao Jun, Zhou Junlin
Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.
Strahlenther Onkol. 2025 Apr;201(4):398-410. doi: 10.1007/s00066-024-02283-x. Epub 2024 Sep 9.
To construct a comprehensive model for predicting the prognosis of patients with glioblastoma (GB) using a radiomics method and integrating clinical risk factors, tumor microenvironment (TME), and imaging characteristics.
In this retrospective study, we included 148 patients (85 males and 63 females; median age 53 years) with isocitrate dehydrogenase-wildtype GB between January 2016 and April 2022. Patients were randomly divided into the training (n = 104) and test (n = 44) sets. The best feature combination related to GB overall survival (OS) was selected using LASSO Cox regression analyses. Clinical, radiomics, clinical-radiomics, clinical-TME, and clinical-radiomics-TME models were established. The models' concordance index (C-index) was evaluated. The survival curve was drawn using the Kaplan-Meier method, and the prognostic stratification ability of the model was tested.
LASSO Cox analyses were used to screen the factors related to OS in patients with GB, including MGMT (hazard ratio [HR] = 0.642; 95% CI 0.414-0.997; P = 0.046), TERT (HR = 1.755; 95% CI 1.095-2.813; P = 0.019), peritumoral edema (HR = 1.013; 95% CI 0.999-1.027; P = 0.049), tumor purity (TP; HR = 0.982; 95% CI 0.964-1.000; P = 0.054), CD163 + tumor-associated macrophages (TAMs; HR = 1.049; 95% CI 1.021-1.078; P < 0.001), CD68 + TAMs (HR = 1.055; 95% CI 1.018-1.093; P = 0.004), and the six radiomics features. The clinical-radiomics-TME model had the best survival prediction ability, the C‑index was 0.768 (0.717-0.819). The AUC of 1‑, 2‑, and 3‑year OS prediction in the test set was 0.842, 0.844, and 0.795, respectively.
The clinical-radiomics-TME model is the most effective for predicting the survival of patients with GB. Radiomics features, TP, and TAMs play important roles in the prognostic model.
利用放射组学方法并整合临床危险因素、肿瘤微环境（TME）和影像特征，构建预测胶质母细胞瘤（GB）患者预后的综合模型。
在这项回顾性研究中，我们纳入了2016年1月至2022年4月期间148例异柠檬酸脱氢酶野生型GB患者（85例男性和63例女性；中位年龄53岁）。患者被随机分为训练集（n = 104）和测试集（n = 44）。使用LASSO Cox回归分析选择与GB总生存期（OS）相关的最佳特征组合。建立了临床、放射组学、临床 - 放射组学、临床 - TME和临床 - 放射组学 - TME模型。评估模型的一致性指数（C指数）。采用Kaplan - Meier法绘制生存曲线，并测试模型的预后分层能力。
LASSO Cox分析用于筛选GB患者中与OS相关的因素，包括甲基鸟嘌呤 - 甲基转移酶（MGMT；风险比[HR] = 0.642；95%置信区间0.414 - 0.997；P = 0.046）、端粒酶逆转录酶（TERT；HR = 1.755；95%置信区间1.095 - 2.813；P = 0.019）、瘤周水肿（HR = 1.013；95%置信区间0.999 - 1.027；P = 0.049）、肿瘤纯度（TP；HR = 0.982；95%置信区间0.964 - 1.000；P = 0.054）、CD163 +肿瘤相关巨噬细胞（TAM；HR = 1.049；95%置信区间1.021 - 1.078；P < 0.001）、CD68 + TAM（HR = 1.055；95%置信区间1.018 - 1.093；P = 0.004）以及六个放射组学特征。临床 - 放射组学 - TME模型具有最佳的生存预测能力，C指数为0.768（0.717 - 0.819）。测试集中1年、2年和3年OS预测的AUC分别为0.842、0.844和0.795。
临床 - 放射组学 - TME模型对预测GB患者的生存最为有效。放射组学特征、TP和TAM在预后模型中起重要作用。
