Yang Jiahao, Cai Haiping, Zhang Liang, Alafate Wahafu, Xi Shaoyan, Du Jiahui, Ke Xueying, Zhang Yinian, Zhou Dong
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Department of Neurosurgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000002921.
This study aimed to develop a nomogram to predict the preoperative diagnostic probabilities of central lymphoma and glioma, as well as glioblastoma versus non-glioblastoma.
Retrospective analysis was performed on patients with central nervous system lymphoma or glioma who received treatment at our department, between 2016 and 2025. From 2016 to 2024, Eligible patients were randomly assigned to training and validation sets in a 7:3 ratio. Patients at our department from 2024-2025(n = 104) and two other medical centers (External Center1: n = 95, External Center2: n = 123) will be included as prospective external validation cohorts. Key variables for nomogram construction were identified through the integration of least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. To assess the performance of the nomogram, seven machine learning models were constructed, including logistic regression, decision tree, random forest, support vector machine (SVM), neural network, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (lightGBM), which were then evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis. The validation set was used for further model evaluation.
This retrospective study included 712 cases: 127 patients with newly diagnosed primary central nervous system lymphoma (PCNSL) and 586 patients with newly diagnosed glioma. In the diagnostic model for PCNSL versus glioma, the following five risk factors were included: age, Karnofsky Performance Status (KPS), neutrophil count (NEUT), neutrophil ratio (NEUT1), and monocyte count (MONO). The area under the curve (AUC) for the seven models ranged from 0.784 to 0.889, and the optimal AUC values obtained from the external validation sets at our center (2024-2025) and two other medical centers were 0.877, 0.716, and 0.743, respectively. In the diagnostic model for glioblastoma versus non-glioblastoma, three risk factors were included: age, neutrophil ratio (NEUT1), and monocyte count (MONO). The AUC for the seven models ranged from 0.778 to 0.857, while the optimal AUC values obtained from the external validation sets at our center (2024-2025) and two other medical centers were 0.861, 0.842, and 0.710.
This study developed and validated diagnostic probability models for central lymphoma versus glioma, and glioblastoma versus non-glioblastoma. These models may assist clinicians in determining the type of central malignant tumor affecting patients, thereby facilitating the development of more personalized and optimized treatment strategies.
本研究旨在开发一种列线图，以预测中枢淋巴瘤和胶质瘤，以及胶质母细胞瘤与非胶质母细胞瘤的术前诊断概率。
对2016年至2025年在我科接受治疗的中枢神经系统淋巴瘤或胶质瘤患者进行回顾性分析。2016年至2024年，符合条件的患者以7:3的比例随机分配到训练集和验证集。2024年至2025年在我科的患者（n = 104）以及另外两个医疗中心（外部中心1：n = 95，外部中心2：n = 123）将作为前瞻性外部验证队列纳入。通过整合最小绝对收缩和选择算子（LASSO）和多变量逻辑回归分析来确定列线图构建的关键变量。为评估列线图的性能，构建了七个机器学习模型，包括逻辑回归、决策树、随机森林、支持向量机（SVM）、神经网络、极端梯度提升（XGBoost）和轻量级梯度提升机（lightGBM），然后使用受试者操作特征曲线（AUC）下面积和决策曲线分析进行评估。验证集用于进一步的模型评估。
这项回顾性研究包括712例病例：127例新诊断的原发性中枢神经系统淋巴瘤（PCNSL）患者和586例新诊断的胶质瘤患者。在PCNSL与胶质瘤的诊断模型中，纳入了以下五个风险因素：年龄、卡诺夫斯基功能状态（KPS）、中性粒细胞计数（NEUT）、中性粒细胞比例（NEUT1）和单核细胞计数（MONO）。七个模型的曲线下面积（AUC）范围为0.784至0.889，在我们中心（2024 - 2025）和另外两个医疗中心的外部验证集中获得 的最佳AUC值分别为0.877、0.716和0.743。在胶质母细胞瘤与非胶质母细胞瘤的诊断模型中，纳入了三个风险因素：年龄、中性粒细胞比例（NEUT1）和单核细胞计数（MONO）。七个模型的AUC范围为0.778至0.857，而在我们中心（2024 - 2025）和另外两个医疗中心的外部验证集中获得的最佳AUC值分别为0.861、0.842和0.710。
本研究开发并验证了中枢淋巴瘤与胶质瘤以及胶质母细胞瘤与非胶质母细胞瘤的诊断概率模型。这些模型可能有助于临床医生确定影响患者的中枢恶性肿瘤类型，从而促进制定更个性化和优化的治疗策略。
