Erschienen in:
Open Access
05.03.2018 | Original Research
Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine
verfasst von:
Ryo Ogawa, Jun Nishikawa, Eizaburo Hideura, Atsushi Goto, Yurika Koto, Shunsuke Ito, Madoka Unno, Yuko Yamaoka, Ryo Kawasato, Shinichi Hashimoto, Takeshi Okamoto, Hiroyuki Ogihara, Yoshihiko Hamamoto, Isao Sakaida
Erschienen in:
Journal of Gastrointestinal Cancer
|
Ausgabe 3/2019
Abstract
Purpose
The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences.
Methods
Eighteen histopathologically confirmed early GC lesions were examined. We prepared images from white light endoscopy (WL), indigo carmine (Indigo), and acetic acid-indigo carmine chromoendoscopy (AIM). A border between cancerous and non-cancerous areas on endoscopic images was established from post-treatment pathological findings, and 2000 pixels with equivalent luminance values were randomly extracted from each image of cancerous and non-cancerous areas. Each pixel was represented as a three-dimensional vector with RGB values and defined as a sample. We evaluated the Mahalanobis distance using RGB values, indicative of color differences between cancerous and non-cancerous areas. We then conducted diagnosis test using a support vector machine (SVM) for each image. SVM was trained using the 100 training samples per class and determined which area each of 1900 test samples per class came from.
Results
The means of the Mahalanobis distances for WL, Indigo, and AIM were 1.52, 1.32, and 2.53, respectively and there were no significant differences in the three modalities. Diagnosability per endoscopy technique was assessed using the F1 measure. The means of F1 measures for WL, Indigo, and AIM were 0.636, 0.618, and 0.687, respectively. AIM images were better than WL and Indigo images for the diagnosis of GC.
Conclusion
Objective assessment by SVM found AIM to be suitable for diagnosis of early GC based on color differences.