Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging.

IF 4.9 3区 工程技术 Q1 CHEMISTRY, ANALYTICAL
Rujuan Wu, Jiayi Yang, Qi Chen, Changxing Yang, Qianqian Ge, Danni Rui, Huazhong Xiang, Dawei Zhang, Cheng Wang, Xiaoqing Zhao
{"title":"Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging.","authors":"Rujuan Wu, Jiayi Yang, Qi Chen, Changxing Yang, Qianqian Ge, Danni Rui, Huazhong Xiang, Dawei Zhang, Cheng Wang, Xiaoqing Zhao","doi":"10.3390/bios14100467","DOIUrl":null,"url":null,"abstract":"<p><p>Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging is crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. Therefore, how to improve the diagnosis accuracy and objectivity of pathologists has become an urgent problem to be solved. We used multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruijin Hospital. Scale-invariant feature transform was applied to multispectral images for image stitching, while the adaptive threshold segmentation method and random forest segmentation method were used for image segmentation, respectively. Synthetic pseudo-color images effectively highlight tissue differences. Quantitative analysis confirms significant variation in the nuclear area between normal and cSCC tissues (<i>p</i> < 0.001), supported by an AUC of 1 in ROC analysis. The AUC within cSCC tissues is 0.57. Further study shows higher nuclear atypia in poorly differentiated cSCC tissues compared to well-differentiated cSCC (<i>p</i> < 0.001), also with an AUC of 1. Lastly, well differentiated cSCC tissues show more and larger keratin pearls. These results have shown that combined MSI with imaging processing techniques will improve H&E stained human cSCC diagnosis accuracy, and it will be well utilized to distinguish histopathological staging features.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506349/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosensors-Basel","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/bios14100467","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging is crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. Therefore, how to improve the diagnosis accuracy and objectivity of pathologists has become an urgent problem to be solved. We used multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruijin Hospital. Scale-invariant feature transform was applied to multispectral images for image stitching, while the adaptive threshold segmentation method and random forest segmentation method were used for image segmentation, respectively. Synthetic pseudo-color images effectively highlight tissue differences. Quantitative analysis confirms significant variation in the nuclear area between normal and cSCC tissues (p < 0.001), supported by an AUC of 1 in ROC analysis. The AUC within cSCC tissues is 0.57. Further study shows higher nuclear atypia in poorly differentiated cSCC tissues compared to well-differentiated cSCC (p < 0.001), also with an AUC of 1. Lastly, well differentiated cSCC tissues show more and larger keratin pearls. These results have shown that combined MSI with imaging processing techniques will improve H&E stained human cSCC diagnosis accuracy, and it will be well utilized to distinguish histopathological staging features.

通过显微多光谱成像区分 H-E 染色人 cSCC 的组织病理学分期特征
皮肤鳞状细胞癌(cSCC)是第二大最常见的恶性皮肤肿瘤。早期精确的肿瘤分期诊断对长期预后至关重要。虽然病理诊断历来是金标准,但对分化程度的评估严重依赖于主观判断。因此,如何提高病理学家诊断的准确性和客观性已成为亟待解决的问题。我们利用多光谱成像(MSI)来提高肿瘤分类的准确性。苏木精和伊红(H&E)染色的 cSCC 切片来自上海瑞金医院。多光谱图像采用尺度不变特征变换进行图像拼接,同时分别采用自适应阈值分割法和随机森林分割法进行图像分割。合成伪彩色图像能有效突出组织差异。定量分析证实,正常组织和 cSCC 组织的核面积差异显著(p < 0.001),ROC 分析的 AUC 为 1。cSCC 组织内的 AUC 为 0.57。进一步研究显示,与分化良好的 cSCC 相比,分化不良的 cSCC 组织核不典型性更高(p < 0.001),AUC 也为 1。这些结果表明,将 MSI 与成像处理技术相结合将提高 H&E 染色人类 cSCC 诊断的准确性,并能很好地用于区分组织病理学分期特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biosensors-Basel
Biosensors-Basel Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
6.60
自引率
14.80%
发文量
983
审稿时长
11 weeks
期刊介绍: Biosensors (ISSN 2079-6374) provides an advanced forum for studies related to the science and technology of biosensors and biosensing. It publishes original research papers, comprehensive reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信