Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.
Technical Department, Atom Medical Corporation, Tokyo, Japan.
J Neurooncol. 2020 Jan;146(2):321-327. doi: 10.1007/s11060-019-03376-9. Epub 2019 Dec 21.
It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.
A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients.
The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly.
A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype.
在决定治疗策略时，了解低级别胶质瘤（LGG）的分子亚型很有用。本研究旨在术前对此进行诊断。
开发了一种深度学习模型，通过包括磁共振成像（MRI）、正电子发射断层扫描（PET）和计算机断层扫描（CT）在内的多模态数据来预测 3 组分子亚型。使用包含 217 名 LGG 患者信息的数据集，通过留一交叉验证评估性能。
当数据集包含 MRI、PET 和 CT 数据时，模型表现最佳。该模型对训练数据集的分子亚型预测准确率为 96.6%，对测试数据集的预测准确率为 68.7%。当数据集仅包含 MRI、MRI 和 PET 以及 MRI 和 CT 数据时，该模型的测试准确率分别为 58.5%、60.4%和 59.4%。用于依次预测异柠檬酸脱氢酶（IDH）基因突变和染色体 1p 和 19q 臂缺失（1p/19q）的传统方法的总体准确率为 65.9%。这比直接预测 3 组分子亚型的建议方法低 2.8 个百分点。
为了直接预测 3 组分类，开发了一种基于多模态数据的深度学习模型来术前诊断分子亚型。交叉验证表明，该模型对测试数据集的总体准确率为 68.7%。这是第一个在预测 LGG 分子亚型时，将 3 组分类问题的预期值提高一倍的模型。
