TSUZUKI Shunsuke
   Department   School of Medicine(Tokyo Women's Medical University Hospital), School of Medicine
   Position   Assistant Professor
Article types Original article
Language English
Peer review Peer reviewed
Title Prediction of lower-grade glioma molecular subtypes using deep learning.
Journal Formal name：Journal of neuro-oncology
Abbreviation：J Neurooncol
ISSN code：15737373/0167594X
Domestic / ForeginForegin
Volume, Issue, Page 146(2),pp.321-327
Author and coauthor MATSUI Yutaka†, MARUYAMA Takashi, NITTA Masayuki, SAITO Taiichi, TSUZUKI Shunsuke, TAMURA Manabu, KUSUDA Kaori, FUKUYA Yasukazu, ASANO Hidetsugu, MASAMUNE Ken, MURAGAKI Yoshihiro
Publication date 2019/12/21
Summary INTRODUCTION: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.METHODS: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.RESULTS: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.CONCLUSIONS: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.
DOI 10.1007/s11060-019-03376-9
PMID 31865510