Zhu Meilin, Hou Weishu, Gao Jiahao, Han Fang, Huang Shanshan, Li Xiaohu, Yin Longlin, Zhang Jiawen
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Quant Imaging Med Surg. 2025 Jun 6;15(6):5752-5768. doi: 10.21037/qims-24-2461. Epub 2025 May 29.
Determining the molecular status of gliomas is crucial for evaluating treatment efficacy and prognosis. However, this process currently requires the invasive and cumbersome method of histological analysis. We aimed to develop and validate a non-invasive three-classification machine learning (ML) model to predict the three molecular subtypes of adult-type diffuse gliomas according to the 2021 World Health Organization classification of tumors of the central nervous system 5 edition (WHO CNS 5).
This retrospective study included a total of 306 glioma patients, among whom 258 were from Center 1 (Huashan Hospital; 180 for the training and 78 for the internal validation set) and 48 were from Center 2 (The First Affiliated Hospital of Anhui Medical University; external validation set). Conventional magnetic resonance imaging (MRI) features of tumors were assessed, and the radiomics and Swin Transformer-based deep learning (RSTD) features were respectively extracted from tumor segmentation on axial three-dimensional contrast-enhanced T1-weighted (3D T1C) and T2-fluid-attenuated inversion recovery (T2-FLAIR) sequences. Three types of prediction models: conventional MRI (CM) model, RSTD model, and combined model were respectively trained using six ML classifiers [k-nearest neighbor (kNN), light gradient-boosting machine (LightGBM), random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and extreme gradient boosting (XGBoost)] to identify the three major molecular subtypes of adult-type diffuse gliomas. The performance of the models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, precision, and F1-score.
XGBoost classifier was chosen as our algorithm for model construction due to its superior performance in the training and internal validation cohorts. The combined model, which incorporates CM features, RSTD features, as well as demographic features, achieved best performance in the internal [micro-AUC (0.905) and macro-AUC (0.878)] and external validation sets [micro-AUC (0.911) and macro-AUC (0.891)]. The SHapley Additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) were used to explain the model.
Our study constructed a three-classification ML model that combined CM features, RSTD features, and demographic characteristics, achieved promising performance in predicting molecular subtypes of diffuse glioma. The combined model provided a non-invasive, timely, and accurate diagnostic approach prior to patient treatment to assist clinical decision-making.
确定胶质瘤的分子状态对于评估治疗效果和预后至关重要。然而，目前这一过程需要采用侵入性且繁琐的组织学分析方法。我们旨在开发并验证一种非侵入性的三分类机器学习（ML）模型，以根据2021年世界卫生组织中枢神经系统肿瘤分类第5版（WHO CNS 5）预测成人型弥漫性胶质瘤的三种分子亚型。
这项回顾性研究共纳入306例胶质瘤患者，其中258例来自中心1（华山医院；180例用于训练，78例用于内部验证集），48例来自中心2（安徽医科大学第一附属医院；外部验证集）。评估肿瘤的常规磁共振成像（MRI）特征，并分别从轴向三维对比增强T1加权（3D T1C）和T2液体衰减反转恢复（T2-FLAIR）序列的肿瘤分割中提取放射组学和基于Swin Transformer的深度学习（RSTD）特征。分别使用六种ML分类器[k近邻（kNN）、轻梯度提升机（LightGBM）、随机森林（RF）、支持向量机（SVM）、随机梯度下降（SGD）和极端梯度提升（XGBoost）]训练三种预测模型：常规MRI（CM）模型、RSTD模型和联合模型，以识别成人型弥漫性胶质瘤的三种主要分子亚型。使用受试者操作特征（ROC）曲线下面积（AUC）、敏感性、特异性、准确性、精确性和F1分数评估模型的性能。
由于XGBoost分类器在训练和内部验证队列中的优越性能，被选为我们构建模型的算法。结合CM特征、RSTD特征以及人口统计学特征的联合模型在内部[微观AUC（0.905）和宏观AUC（0.878）]和外部验证集[微观AUC（0.911）和宏观AUC（0.891）]中表现最佳。使用SHapley加法解释（SHAP）和梯度加权类激活映射（Grad-CAM）对模型进行解释。
我们的研究构建了一种结合CM特征、RSTD特征和人口统计学特征的三分类ML模型，在预测弥漫性胶质瘤的分子亚型方面取得了良好的性能。联合模型在患者治疗前提供了一种非侵入性、及时且准确的诊断方法，以协助临床决策。
