Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework

doi:10.1016/j.cmpb.2024.108352

	Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework
	Hou, Xingzhong 1,2; Guan, Zhen 1; Zhang, Xianwei 3,4; Hu, Xiao 5; Zou, Shuangmei 6; Liang, Chunzi 7; Shi, Lulin 1,2; Zhang, Kaitai 8; You, Haihang 1,9
	2024-12-01
发表期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
卷号	257 页码:18
摘要	As the global incidence of cancer continues to rise rapidly, the need for swift and precise diagnoses has become increasingly pressing. Pathologists commonly rely on H&E-panCK stain pairs for various aspects of cancer diagnosis, including the detection of occult tumor cells and the evaluation of tumor budding. Nevertheless, conventional chemical staining methods suffer from notable drawbacks, such as time-intensive processes and irreversible staining outcomes. The virtual stain technique, leveraging generative adversarial network (GAN), has emerged as a promising alternative to chemical stains. This approach aims to transform biopsy scans (often H&E) into other stain types. Despite achieving notable progress in recent years, current state-of-the-art virtual staining models confront challenges that hinder their efficacy, particularly in achieving accurate staining outcomes under specific conditions. These limitations have impeded the practical integration of virtual staining into diagnostic practices. To address the goal of producing virtual panCK stains capable of replacing chemical panCK, we propose an innovative multi-model framework. Our approach involves employing a combination of Mask-RCNN (for cell segmentation) and GAN models to extract cytokeratin distribution from chemical H&E images. Additionally, we introduce a tailored dynamic GAN model to convert H&E images into virtual panCK stains, integrating the derived cytokeratin distribution. Our framework is motivated by the fact that the unique pattern of the panCK is derived from cytokeratin distribution. As a proof of concept, we employ our virtual panCK stains to evaluate tumor budding in 45 H&E whole-slide images taken from breast cancer-invaded lymph nodes . Through thorough validation by both pathologists and the QuPath software, our virtual panCK stains demonstrate a remarkable level of accuracy. In stark contrast, the accuracy of state-of-the-art single cycleGAN virtual panCK stains is negligible. To our best knowledge, this is the first instance of a multi model virtual panCK framework and the utilization of virtual panCK for tumor budding assessment. Our framework excels in generating dependable virtual panCK stains with significantly improved efficiency, thereby considerably reducing turnaround times in diagnosis. Furthermore, its outcomes are easily comprehensible even to pathologists who may not be well-versed in computer technology. We firmly believe that our framework has the potential to advance the field of virtual stain, thereby making significant strides towards improved cancer diagnosis.
关键词	Virtual Stain Stain-to-stain transformation Multi-model framework Explainable AI Tumor budding
DOI	10.1016/j.cmpb.2024.108352
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS记录号	WOS:001309749900001
出版者	ELSEVIER IRELAND LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39592
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Kaitai
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China 3.Henan Prov Peoples Hosp, Dept Pathol, Zhengzhou 450003, Henan, Peoples R China 4.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China 5.Peking Univ Canc Hosp & Inst, Dept Pathol, Key Lab Carcinogenesis & Translat Res, Minist Educ, Beijing, Peoples R China 6.Chinese Acad Med Sci & Peking Union Med Coll, Natl Canc Ctr, Natl Clin Res Ctr Canc, Dept Pathol,Canc Hosp, Beijing 100021, Peoples R China 7.Hubei Univ Chinese Med, Sch Lab Med, 16 Huangjia Lake West Rd, Wuhan 430065, Hubei, Peoples R China 8.Chinese Acad Med Sci & Peking Union Med Coll, Natl Canc Ctr, Natl Clin Res Ctr Canc, State Key Lab Mol Oncol,Canc Hosp,Dept Etiol & Car, Beijing 100021, Peoples R China 9.Zhongguancun Lab, Beijing 102206, Peoples R China
推荐引用方式 GB/T 7714	Hou, Xingzhong,Guan, Zhen,Zhang, Xianwei,et al. Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2024,257:18.
APA	Hou, Xingzhong.,Guan, Zhen.,Zhang, Xianwei.,Hu, Xiao.,Zou, Shuangmei.,...&You, Haihang.(2024).Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,257,18.
MLA	Hou, Xingzhong,et al."Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 257(2024):18.