CASIA OpenIR  > 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, Biluo1,2; Zhang, Zhe3,4; Shi, Xiaojing1,2; Cao, Caiguang1,2; Zhang, Zeyu1; Hu, Zhenhua1,2; Ji, Nan3,4,5; Tian, Jie1,2,5,6
Source PublicationEUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
ISSN1619-7070
2021-04-27
Volume48Issue:11Pages:3482-3492
Abstract

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.

KeywordFluorescence imaging Deep learning Convolutional neural networks Intraoperative pathology Gliomas
DOI10.1007/s00259-021-05326-y
URL查看原文
Indexed BySCI
Language英语
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000644731700001
PublisherSPRINGER
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44237
Collection中国科学院分子影像重点实验室
Corresponding AuthorHu, Zhenhua; Ji, Nan; Tian, Jie
Affiliation1.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
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, 119 South Fourth Ring West Rd, Beijing 100070, Peoples R China
4.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China
5.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
6.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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,2021,48(11):3482-3492.
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,48(11),3482-3492.
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 48.11(2021):3482-3492.
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