420. IDENTIFICATION AND CLASSIFICATION OF TUMOR CELLS IN PATIENTS WITH BARRETT'S CARCINOMA BY HYPERSPECTRAL IMAGING (HSI). (24th September 2022)

• Record Type: Journal Article
• Authors: Maktabi, Marianne; Wichmann, Yannis; Köhler, Hannes; Chalopin, Claire; Jansen-Winkeln, Boris; Gockel, Ines; Thieme, René
• Is Part Of: Diseases of the esophagus. Volume 35(2022)Supplement 2
• Publication Date: 2022-09-24
• Subjects: Esophagus -- Diseases -- Periodicals; 616.32
• DOI: 10.1093/dote/doac051.420
• Languages: English
• ISSNs: 1120-8694

Abstract

Abstract: Hyperspectral imaging (HSI), as recently applied in medicine, is a novel technology combining imaging with spectroscopy. It might be used to identify, classify and discriminate malignant and non-malignant cells of histopathologic specimens. HSI allows the determination of a spectrum between the visual and near-infrared light (500-1000 nm). After surgical resection, specimens (n = 96) of Barrett's cancer were fixed in 4% formaldehyde and slices were conducted (3 μm), which were stained with hematoxylin and eosin (HE). Differences in the absorbance of squamous epithelium and esophageal adenocarcinoma (EAC) cells were determined at eosin's and hematoxylin's maximal absorption of 530 nm and 590 nm. A classification algorithm, which combines a multilayer perceptron (MLP) with a Gaussian filter and data reduction based on principal component analysis (PCA) was used for discrimination. For the first time in literature, we were able to analyze esophageal adenocarcinoma, tumor stroma and squamous epithelium cells by HSI. For both, the squamous epithelium and the esophageal adenocarcinoma cells, the intragroup variances were quite low. A set of 20 specimens was used in a testing phase, which showed MLP by using reflectance data to provide the best results. Leave-one-patient-out-cross validation for all 96 specimens showed an accuracy of 77% with a sensitivity of 87% and a specificity of 72% for EAC determination and visualization. Squamous epithelium and EAC cells show specific spectral alterations due to their HE-staining, when measured by HSI. However, the training algorithms need further validation to foster a semi-automatic decision-making process in histopathological tumor cell identification.