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Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging

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收录情况： ◇ SCIE

作者：
Li Junjie[1] ; Zhang Peng[2] ; Qu Liying[1] ; Sun Ting[1] ; Duan Yunyun[1] ; Wu Minghao[1] ; Weng Jinyuan[3] ; Li Zhaohui[4] ; Gong Xiaodong[3] ; Liu Xing[5] ; Wang Yongzhi[2] ; Jia Wenqing[2] ; Su Xiaorui[6] ; Yue Qiang[6] ; Li Jianrui[7] ; Zhang Zhiqiang[7] ; Barkhof Frederik[8,9] ; Huang Raymond Y[10] ; Chang Ken[11] ; Sair Haris[12] ; Ye Chuyang[13] ; Zhang Liwei[2] ; Zhuo Zhizheng[1] ; Liu Yaou[1,*1] ;
机构： [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China. [2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China. [3]Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, People's Republic of China. [4]BioMind Inc., Beijing, People's Republic of China. [5]Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China. [6]Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China. [7]Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China. [8]UCL Institutes of Neurology and Healthcare Engineering, London, UK. [9]Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands. [10]Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. [11]Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. [12]Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. [13]School of Information and Electronics, Beijing Institute of Technology, Beijing, People's Republic of China.
出处：
DOI： 10.1002/jmri.28606
ISSN：

摘要：
Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG.This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images.Retrospective and prospective.For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males).5T and 3T, T2-weighted turbo spin echo imaging.Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve.Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant.In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%.In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making.2 Technical Efficacy: Stage 2.© 2023 International Society for Magnetic Resonance in Medicine.

基金：
National Science Foundation of China (Nos. 81870958 and 81571631), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (No. JQ20035), Beijing Youth Scholar and the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (No. XTYB201831).
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外文
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PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类 | 2 区 医学
小类 | 2 区 核医学
最新[2023]版：
大类 | 2 区 医学
小类 | 2 区 核医学
JCR分区：
出版当年[2023]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版]

第一作者：
第一作者机构： [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
共同第一作者：
通讯作者：
通讯机构： [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China. [*1]No. 119, the West Southern 4th Ring Road, Fengtai District, Beijing 100070, China.
推荐引用方式(GB/T 7714)：
Li Junjie,Zhang Peng,Qu Liying,et al.Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging[J].JOURNAL OF MAGNETIC RESONANCE IMAGING.2023,58(3):850-861.doi:10.1002/jmri.28606.
APA：
Li Junjie,Zhang Peng,Qu Liying,Sun Ting,Duan Yunyun...&Liu Yaou.(2023).Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.JOURNAL OF MAGNETIC RESONANCE IMAGING,58,(3)
MLA：
Li Junjie,et al."Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging".JOURNAL OF MAGNETIC RESONANCE IMAGING 58..3(2023):850-861

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