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

文献类型：期刊论文

作者Hou, Xingzhong1,2; Guan, Zhen1; Zhang, Xianwei3,4; Hu, Xiao5; Zou, Shuangmei6; Liang, Chunzi7; Shi, Lulin1,2; Zhang, Kaitai8; You, Haihang1,9
刊名COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
出版日期2024-12-01
卷号257页码:18
关键词Virtual Stain Stain-to-stain transformation Multi-model framework Explainable AI Tumor budding
ISSN号0169-2607
DOI10.1016/j.cmpb.2024.108352
英文摘要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.
WOS研究方向Computer Science ; Engineering ; Medical Informatics
语种英语
WOS记录号WOS:001309749900001
出版者ELSEVIER IRELAND LTD
源URL[http://119.78.100.204/handle/2XEOYT63/39591]  
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.

入库方式： OAI收割

来源：计算技术研究所

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