A comprehensive AI model development framework for consistent Gleason grading
Year: 2024
Type: article
Abstract: AI-based solutions for automated Gleason grading have been developed to assist pathologists to make rapid and quantitative assessments, but the generalization across various scanners and updating AI models continuously using new annotated data from end users remains a key bottleneck in the field. We proposed an comprehensive digital pathology workflow for AI-assisted Gleason grading, incorporating an image quality check software A!magQC, a cloud-based annotation platform A!HistoNotes and Pathologist-AI Interaction (PAI) strategy. To demonstrate and validate the pipeline, we employed it on prostate samples obtained from 5 scanners for Gleason grading. After training on 132 prostatectomy specimens scanned by an Akoya Biosciences scanner, validation on 55 prostatectomy specimens and 156 biopsy specimens yielded a sensitivity of 85%, specificity of 96% and F1 score of 78% on Gleason grading for prostatectomy specimens, and 96% sensitivity on tumor detection for biopsy specimens. For images scanned by other 4 scanners, the average F1 score increased from 67% to 75% on Gleason pattern detection after adopting our generalization solution. In clinical experiments conducted with 5 pathologists from Singapore and China, our pipeline accelerated Gleason scoring by 43%. Furthermore, it reduced annotation time by 60% via semi-automatic annotation, leading to improved model performance through incremental learning.
Source: Communications Medicine
Authors: Xinmi Huo, Kok Haur Ong, Kah Weng Lau, Laurent Gole, David M. Young, Char Loo Tan, Xiaohui Zhu, Chongchong Zhang, Yonghui Zhang, Longjie Li, Hao Han, Haoda Lu, Jing Zhang, Jun Hou, Huanfen Zhao, Hualei Gan, Lijuan Yin, Xingxing Wang, Xiaoyue Chen, Hong Lv, Haotian Cao, Xiaozhen Yu, Yabin Shi, Ziling Huang, Gabriel Marini, Jun Xu, Bingxian Liu, Bingxian Chen, Qiang Wang, Kun Gui, Wenzhao Shi, Yingying Sun, Wanyuan Chen, Dalong Cao, Stephan J. Sanders, Hwee Kuan Lee, Susan Swee-Shan Hue, Weimiao Yu, Soo Yong Tan
Institutions: Agency for Science, Technology and Research, Bioinformatics Institute, National University Hospital, National University Health System, Institute of Molecular and Cell Biology, University of California, San Francisco, Key Laboratory of Guangdong Province, Nanfang Hospital, Southern Medical University, Nanjing University of Information Science and Technology, Shanghai Changzheng Hospital, Fudan University, Zhongshan Hospital, Hebei General Hospital, Fudan University Shanghai Cancer Center, Changhai Hospital, Vishuo Biomedical (Singapore), Zhejiang Provincial People's Hospital, Hangzhou Medical College, Shanghai Medical College of Fudan University, Shanghai Cancer Institute, University of Oxford, National University of Singapore
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
OpenAlex ID: W4396781237