Automatic 1p/19q co-deletion identification of gliomas by MRI using deep learning U-net network. (January 2023)

• Record Type: Journal Article
• Authors: Zhao, Kai; Li, Boyuan; Zhang, Kai; Liu, Ruoyu; Gao, Long; Shu, Xujun; Liu, Minghang; Yang, Xuejun; Chen, Shengbo; Sun, Guochen
• Is Part Of: Computers & electrical engineering. Volume 105(2023)
• Publication Date: 2023-01
• Subjects: Deep learning -- U-net -- Gliomas -- 1p/19q co-deletion -- Molecular imaging; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108482
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new pipeline using U-net for genotype classification. Expanded data volume, modalities and tumor grade range while ensuring satisfying accuracy rate. Attempt to reveal the decision process of the neural network through a heat map. Abstract: The chromosome 1p/19q co-deletion which is a hallmark of oligodendroglioma plays more crucial role in glioma classification especially in "The 2021 WHO Classification of Tumors of the Central Nervous System". A more effective non-invasive method to distinguish 1p/19q co-deletion tumor from all gliomas can facilitate the strategy selection of pathologists, physicians, and surgeons. Preoperative MRI, including T1, T2, enhanced T1 and T2-FLAIR, from 61 glioma patients of our facility were reviewed. Data from 89 gliomas subjects from The Cancer Imaging Archive were recruited. Following the preprocessing, we improved the U-net and ResNet152 based on the MRI data of different modalities to determine the 1p/19q codeletion from overall gliomas. The different models were compared. The UMAP result implies that two different data share some similar traits. All the sensitivity, specificity and accuracy of U-net are higher than that of the ResNet152. The test accuracy with four modalities outperforms others significantly, reaching 92.156%. We introduce an efficient pipeline with U-net network for the identification of 1p/19q genotype status. The study implements one step judgement with multi-modal sequence MRI images. It takes a further step to suggest that machine learning can render more possibilities to conventional MRI.