Wesley Y. Kendall, Qinyi Tian, Shi Zhao, Seyedbabak Mirminachi, Erin O'Kane, Abel Joseph, Darin Dufault, David A. Miller, Chanjuan Shi, Jatin Roper, Adam Wax
{"title":"Deep learning classification of ex vivo human colon tissues using spectroscopic optical coherence tomography","authors":"Wesley Y. Kendall, Qinyi Tian, Shi Zhao, Seyedbabak Mirminachi, Erin O'Kane, Abel Joseph, Darin Dufault, David A. Miller, Chanjuan Shi, Jatin Roper, Adam Wax","doi":"10.1002/jbio.202400082","DOIUrl":null,"url":null,"abstract":"<p>Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.</p>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biophotonics","FirstCategoryId":"101","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jbio.202400082","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.
使用结肠镜筛查结肠直肠癌(CRC)改善了患者的预后;然而,它仍然是癌症相关死亡率的第三大原因,因此需要新的策略来改善筛查。在此,我们提出了一种基于光谱光学相干断层扫描(OCT)的光学活检技术。深度分辨 OCT 图像通过分析波长函数来测量光学组织特性,并将其作为机器学习算法的输入。此前,我们曾用这种方法分析了小鼠结肠息肉。在这里,我们将这种方法扩展到检查活检的人体结肠上皮组织样本。光学特性被用作新型深度学习架构的输入,在区分组织类型方面的准确率高达 97.9%。SOCT 参数用于创建假彩色正视 OCT 图像,深度学习分类用于按组织类型进行视觉分类。这项研究推动了 SOCT 在分析结肠上皮方面的临床应用。
期刊介绍:
The first international journal dedicated to publishing reviews and original articles from this exciting field, the Journal of Biophotonics covers the broad range of research on interactions between light and biological material. The journal offers a platform where the physicist communicates with the biologist and where the clinical practitioner learns about the latest tools for the diagnosis of diseases. As such, the journal is highly interdisciplinary, publishing cutting edge research in the fields of life sciences, medicine, physics, chemistry, and engineering. The coverage extends from fundamental research to specific developments, while also including the latest applications.