Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline

doi:10.1007/s11060-023-04306-6

	Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline
	Ma, Yixin 1,2; Shi, Feng 3; Sun, Tianyang 3; Chen, Hong 2,4; Cheng, Haixia 2,4; Liu, Xiaojia 2,4; Wu, Shuai 1,2; Lu, Junfeng 1,2; Zou, Yaping 5; Zhang, Jun 5; Jin, Lei 1,2; Shen, Dinggang3,6,7 ; Wu, Jinsong 1,2
	2023-05-01
发表期刊	JOURNAL OF NEURO-ONCOLOGY (IF:3.2[JCR-2023],3.7[5-Year])
ISSN	0167-594X
EISSN	1573-7373
发表状态	已发表
DOI	10.1007/s11060-023-04306-6
摘要	PurposeClassification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI.MethodsA one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available.ResultsThe pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 s per slide.ConclusionHAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.
关键词	Neuropathology Histology Deep learning Multiply instance learning Clinical practice
URL	查看原文
收录类别	SCI
语种	英语
资助项目	Shanghai Municipal Science and Technology Commision "Science and Technology Innovation Action Plan" Project[22S31905400] ; Shanghai Shenkang Hospital Development Center[SHDC12018114]
WOS研究方向	Oncology ; Neurosciences & Neurology
WOS类目	Oncology ; Clinical Neurology
WOS记录号	WOS:000986316500001
出版者	SPRINGER
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/305030
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组
通讯作者	Jin, Lei; Shen, Dinggang; Wu, Jinsong
作者单位	1.Fudan Univ, Neurol Surg Dept, Glioma Surg Div, Huashan Hosp, Shanghai, Peoples R China 2.Fudan Univ, Natl Ctr Neurol Disorders, Huashan Hosp, Shanghai, Peoples R China 3.Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai, Peoples R China 4.Fudan Univ, Dept Pathol, Huashan Hosp, Shanghai, Peoples R China 5.Wuhan Zhongji Biotechnol Co Ltd, Wuhan, Peoples R China 6.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China 7.Shanghai Clin Res & Trial Ctr, Shanghai, Peoples R China
通讯作者单位	生物医学工程学院
推荐引用方式 GB/T 7714	Ma, Yixin,Shi, Feng,Sun, Tianyang,et al. Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline[J]. JOURNAL OF NEURO-ONCOLOGY,2023.
APA	Ma, Yixin.,Shi, Feng.,Sun, Tianyang.,Chen, Hong.,Cheng, Haixia.,...&Wu, Jinsong.(2023).Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.JOURNAL OF NEURO-ONCOLOGY.
MLA	Ma, Yixin,et al."Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline".JOURNAL OF NEURO-ONCOLOGY (2023).