Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

doi:10.1111/epi.16392

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Automated detection of hippocampal sclerosis using clinically empirical and radiomics features
	Mo, Jiajie 2,3,4; Liu, Zhenyu 5; Sun, Kai 1,5; Ma, Yanshan 6; Hu, Wenhan 2,3,4; Zhang, Chao 2,3,4; Wang, Yao 2,3,4; Wang, Xiu 2,3,4; Liu, Chang 2,3,4; Zhao, Baotian 2,3,4
刊名	EPILEPSIA
	2019-12-01
卷号	60 期号:12 页码:2519-2529
关键词	clinical features hippocampal sclerosis MRI negative radiomics
ISSN号	0013-9580
DOI	10.1111/epi.16392
通讯作者	Zhang, Kai(zhangkai62035@sina.com) ; Zhang, Jianguo(zjguo73@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Objective: Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS. Methods: Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS. Results: The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration. Significance: Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.
资助项目	National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81771399] ; National Natural Science Foundation of China[81701276] ; National Natural Science Foundation of China[81830033] ; Beijing Natural Science Foundation[7182109] ; National Key R&D Program of China[2017YFA0205200] ; Youth Innovation Promotion Association CAS[2019136] ; Beijing Municipal Science & Technology Commission[Z171100001017069] ; Beijing Municipal Administration of Hospitals' Ascent Plan[DFL20150503] ; Capital Health Research and Development of Special Fund[2018-2-1076]
WOS关键词	TEMPORAL-LOBE EPILEPSY ; BRAIN-TISSUE ; EX-VIVO ; MRI ; SEGMENTATION ; PATHOLOGY ; PATTERN ; ATROPHY ; SURGERY ; ATLAS
WOS研究方向	Neurosciences & Neurology
语种	英语
出版者	WILEY
WOS记录号	WOS:000545973100019
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Youth Innovation Promotion Association CAS ; Beijing Municipal Science & Technology Commission ; Beijing Municipal Administration of Hospitals' Ascent Plan ; Capital Health Research and Development of Special Fund
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40044]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Zhang, Kai; Zhang, Jianguo; Tian, Jie
作者单位	1.Xidian Univ, Engn Res Ctr Mol & Neuroimaging, Sch Life Sci & Technol, Minist Educ, Xian, Peoples R China 2.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, 119 South 4th Ring West Rd, Beijing 100070, Peoples R China 3.Capital Med Univ, Beijing Neurosurg Inst, Dept Neurosurg, Beijing, Peoples R China 4.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China 5.Chinese Acad Sci, Key Lab Mol Imaging, Inst Automat, Beijing, Peoples R China 6.Peking Univ, Epilepsy Ctr, Hosp 1, Fengtai Hosp, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Mo, Jiajie,Liu, Zhenyu,Sun, Kai,et al. Automated detection of hippocampal sclerosis using clinically empirical and radiomics features[J]. EPILEPSIA,2019,60(12):2519-2529.
APA	Mo, Jiajie.,Liu, Zhenyu.,Sun, Kai.,Ma, Yanshan.,Hu, Wenhan.,...&Tian, Jie.(2019).Automated detection of hippocampal sclerosis using clinically empirical and radiomics features.EPILEPSIA,60(12),2519-2529.
MLA	Mo, Jiajie,et al."Automated detection of hippocampal sclerosis using clinically empirical and radiomics features".EPILEPSIA 60.12(2019):2519-2529.