作为圣裘德儿童研究医院 (SJCRH) 脑肿瘤患者治疗决策进步的一部分，我们开发了三个强大的分类器：深度学习神经网络 (NN)、k 最近邻网络 (kNN) 和随机分类器Forest (RF)，根据参考系列 DNA 甲基化图谱进行训练，以对中枢神经系统 (CNS) 肿瘤类型进行分类。该模型的性能经过了来自两个独立队列的 2054 个样本的严格验证。除了模型性能的经典指标之外，我们还比较了三种模型的鲁棒性与降低的肿瘤纯度，这是此类分类器临床实用性的关键考虑因素。我们的研究结果表明，神经网络模型表现出最高的准确率，并保持了准确率和召回率之间的平衡。 NN 模型对与肿瘤纯度降低相关的性能下降的抵抗力最强，在纯度降至 50% 以下之前表现出良好的性能。通过严格的验证，我们的研究强调了基于 DNA 甲基化的深度学习方法在临床环境中改善脑肿瘤分类精准医学的潜力。© 2024。作者。

As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN model was the most resistant to drops in performance associated with a reduction in tumor purity, showing good performance until the purity fell below 50%. Through rigorous validation, our study emphasizes the potential of DNA-methylation-based deep learning methods to improve precision medicine for brain tumor classification in the clinical setting.© 2024. The Author(s).