{"title":"通过半监督学习对形态染色进行不确定性辅助虚拟免疫组化检测","authors":"","doi":"10.1016/j.optlaseng.2024.108657","DOIUrl":null,"url":null,"abstract":"<div><div>Tumor suppressor gene TP53 plays a crucial role in cancer diagnosis and prognosis. The gene encodes the tumor suppressor protein p53, which can be identified through immunohistochemical (IHC) staining in various cancers, including gastric carcinoma. However, IHC staining is more costly and therefore not as prevalent as routine hematoxylin-eosin (H&E) staining. In this study, we present a semi-supervised learning-based approach for immunological detection (SSID) of TP53 mutation directly on H&E-stained gastric tissue sections, intending to improve gastric cancer diagnosis. SSID is trained on a small set of annotated image pairs and a larger unannotated dataset of H&E-stained images. It can detect the regions showing strong p53 expression, indicating TP53 mutation, and we validate the accuracy of our approach through both qualitative assessment (pathologists' average score of 2.22/3) and quantitative evaluation (e.g., averaged mean Intersection-over-Union of 0.73). Moreover, we introduce Bayesian uncertainty to assess the credibility of the detected masks, aiming to prevent misdiagnosis and inappropriate treatment. Our results demonstrate that SSID can circumvent the expensive and laborious IHC staining procedures and enable the diagnosis and prognosis of gastric cancer through immunological detection of TP53 mutation.</div></div>","PeriodicalId":49719,"journal":{"name":"Optics and Lasers in Engineering","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Uncertainty-assisted virtual immunohistochemical detection on morphological staining via semi-supervised learning\",\"authors\":\"\",\"doi\":\"10.1016/j.optlaseng.2024.108657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Tumor suppressor gene TP53 plays a crucial role in cancer diagnosis and prognosis. The gene encodes the tumor suppressor protein p53, which can be identified through immunohistochemical (IHC) staining in various cancers, including gastric carcinoma. However, IHC staining is more costly and therefore not as prevalent as routine hematoxylin-eosin (H&E) staining. In this study, we present a semi-supervised learning-based approach for immunological detection (SSID) of TP53 mutation directly on H&E-stained gastric tissue sections, intending to improve gastric cancer diagnosis. SSID is trained on a small set of annotated image pairs and a larger unannotated dataset of H&E-stained images. It can detect the regions showing strong p53 expression, indicating TP53 mutation, and we validate the accuracy of our approach through both qualitative assessment (pathologists' average score of 2.22/3) and quantitative evaluation (e.g., averaged mean Intersection-over-Union of 0.73). Moreover, we introduce Bayesian uncertainty to assess the credibility of the detected masks, aiming to prevent misdiagnosis and inappropriate treatment. Our results demonstrate that SSID can circumvent the expensive and laborious IHC staining procedures and enable the diagnosis and prognosis of gastric cancer through immunological detection of TP53 mutation.</div></div>\",\"PeriodicalId\":49719,\"journal\":{\"name\":\"Optics and Lasers in Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics and Lasers in Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0143816624006353\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics and Lasers in Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0143816624006353","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
Uncertainty-assisted virtual immunohistochemical detection on morphological staining via semi-supervised learning
Tumor suppressor gene TP53 plays a crucial role in cancer diagnosis and prognosis. The gene encodes the tumor suppressor protein p53, which can be identified through immunohistochemical (IHC) staining in various cancers, including gastric carcinoma. However, IHC staining is more costly and therefore not as prevalent as routine hematoxylin-eosin (H&E) staining. In this study, we present a semi-supervised learning-based approach for immunological detection (SSID) of TP53 mutation directly on H&E-stained gastric tissue sections, intending to improve gastric cancer diagnosis. SSID is trained on a small set of annotated image pairs and a larger unannotated dataset of H&E-stained images. It can detect the regions showing strong p53 expression, indicating TP53 mutation, and we validate the accuracy of our approach through both qualitative assessment (pathologists' average score of 2.22/3) and quantitative evaluation (e.g., averaged mean Intersection-over-Union of 0.73). Moreover, we introduce Bayesian uncertainty to assess the credibility of the detected masks, aiming to prevent misdiagnosis and inappropriate treatment. Our results demonstrate that SSID can circumvent the expensive and laborious IHC staining procedures and enable the diagnosis and prognosis of gastric cancer through immunological detection of TP53 mutation.
期刊介绍:
Optics and Lasers in Engineering aims at providing an international forum for the interchange of information on the development of optical techniques and laser technology in engineering. Emphasis is placed on contributions targeted at the practical use of methods and devices, the development and enhancement of solutions and new theoretical concepts for experimental methods.
Optics and Lasers in Engineering reflects the main areas in which optical methods are being used and developed for an engineering environment. Manuscripts should offer clear evidence of novelty and significance. Papers focusing on parameter optimization or computational issues are not suitable. Similarly, papers focussed on an application rather than the optical method fall outside the journal''s scope. The scope of the journal is defined to include the following:
-Optical Metrology-
Optical Methods for 3D visualization and virtual engineering-
Optical Techniques for Microsystems-
Imaging, Microscopy and Adaptive Optics-
Computational Imaging-
Laser methods in manufacturing-
Integrated optical and photonic sensors-
Optics and Photonics in Life Science-
Hyperspectral and spectroscopic methods-
Infrared and Terahertz techniques