We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.
- Authors
Ma, Yixin; Shi, Feng; Sun, Tianyang; Chen, Hong; Cheng, Haixia; Liu, Xiaojia; Wu, Shuai; Lu, Junfeng; Zou, Yaping; Zhang, Jun; Jin, Lei; Shen, Dinggang; Wu, Jinsong
- Abstract
Purpose: Classification 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. Methods: A 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. Results: The 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. Conclusion: HAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.
- Subjects
BRAIN tumors; HISTOPATHOLOGY; ARTIFICIAL intelligence; MOLECULAR pathology; CENTRAL nervous system; NURSE practitioners; BIOINFORMATICS software
- Publication
Journal of Neuro-Oncology, 2023, Vol 163, Issue 1, p71
- ISSN
0167-594X
- Publication type
Article
- DOI
10.1007/s11060-023-04306-6