Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks

doi:10.1007/s00259-021-05326-y

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

	Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks
	Shen, Biluo 5,6; Zhang, Zhe 3,4; Shi, Xiaojing 5,6; Cao, Caiguang 5,6; Zhang, Zeyu 6; Hu, Zhenhua 5,6; Ji, Nan 1,3,4; Tian, Jie 1,2,5,6
刊名	EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
	2021-04-27
页码	11
关键词	Fluorescence imaging Deep learning Convolutional neural networks Intraoperative pathology Gliomas
ISSN号	1619-7070
DOI	10.1007/s00259-021-05326-y
通讯作者	Hu, Zhenhua(zhenhua.hu@ia.ac.cn) ; Ji, Nan(jinan@bjtth.org) ; Tian, Jie(tian@ieee.org)
英文摘要	Purpose Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon's visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery. Methods Glioma patients (N = 23) were enrolled and injected with indocyanine green for fluorescence image-guided surgery. Tissue samples (N = 1874) were harvested from surgery of these patients, and the second near-infrared window (NIR-II, 1000-1700 nm) fluorescence images were obtained. Deep convolutional neural networks (CNNs) combined with NIR-II fluorescence imaging (named as FL-CNN) were explored to automatically provide pathological diagnosis of glioma in situ in real-time during patient surgery. The pathological examination results were used as the gold standard. Results The developed FL-CNN achieved the area under the curve (AUC) of 0.945. Comparing to neurosurgeons' judgment, with the same level of specificity >80%, FL-CNN achieved a much higher sensitivity (93.8% versus 82.0%, P < 0.001) with zero time overhead. Further experiments demonstrated that FL-CNN corrected >70% of the errors made by neurosurgeons. FL-CNN was also able to rapidly predict grade and Ki-67 level (AUC 0.810 and 0.625) of tumor specimens intraoperatively. Conclusion Our study demonstrates that deep CNNs are better at capturing important information from fluorescence images than surgeons' evaluation during patient surgery. FL-CNN is highly promising to provide pathological diagnosis intraoperatively and assist neurosurgeons to obtain maximum resection safely.
资助项目	National Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China (NSFC)[62027901] ; National Natural Science Foundation of China (NSFC)[92059207] ; National Natural Science Foundation of China (NSFC)[81930048] ; National Natural Science Foundation of China (NSFC)[81930053] ; National Natural Science Foundation of China (NSFC)[81227901] ; National Natural Science Foundation of China (NSFC)[81801864] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Natural Science Foundation[JQ19027] ; Capital Characteristic Clinical Application Project[Z181100001718196] ; Zhuhai High-level Health Personnel Team Project (Zhuhai)[HLHPTP201703] ; innovative research team of high-level local universities in Shanghai
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000644731700001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Beijing Natural Science Foundation ; Capital Characteristic Clinical Application Project ; Zhuhai High-level Health Personnel Team Project (Zhuhai) ; innovative research team of high-level local universities in Shanghai
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44236]
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	Hu, Zhenhua; Ji, Nan; Tian, Jie
作者单位	1.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China 2.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China 3.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China 4.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, 119 South Fourth Ring West Rd, Beijing 100070, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Beijing Key Lab Mol Imagi, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shen, Biluo,Zhang, Zhe,Shi, Xiaojing,et al. Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks[J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,2021:11.
APA	Shen, Biluo.,Zhang, Zhe.,Shi, Xiaojing.,Cao, Caiguang.,Zhang, Zeyu.,...&Tian, Jie.(2021).Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,11.
MLA	Shen, Biluo,et al."Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks".EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021):11.