Detection of deep lesion in resected stomach by near-infrared hyperspectral imaging

Near-infrared hyperspectral imaging (NIR-HSI) is well known that it enables chemical composition analysis with high bio-transparency and high spatial resolution. Thus, hyperspectral imaging is potential in noninvasive and label-free diagnosis of deep lesion by machine learning. In this study, detection of deep lesions such as Gastrointestinal Stromal Tumor (GIST) and Gastric Cancer (GC) including unexposed areas was investigated using NIR-HSI. As the result, although GIST specimens had a normal mucosal layer covering the lesion, NIR-HSI analysis by machine learning showed an average prediction accuracy of 86.1%. In case of GC specimens, average prediction accuracy of GC regions in all area, exposed area and unexposed area were 79.9%, 80.9% and 77.8%, respectively.