Towards Computer-Assisted Diagnosis of Precursor Colorectal Lesions

Abstract

Colorectal cancer (CRC) is the fourth most common cancer in men worldwide (International Agency of Research on Cancer, 2008). In many countries, regular colonoscopy screening is established as a crucial strategy for CRC prevention. During colonoscopy screening, detected precursor lesions such as adenomas and serrated polyps can be removed, thus reducing CRC incidence and mortality. After such a polypectomy, histological diagnosis is fundamental. With continuously rising numbers of participants in screening programs as well as removed polyps, an increased demand exists for an automated pre-screening and classification of colorectal lesions in digitized histological slides. Hence, in this study, initial experiments were conducted to evaluate which approaches are suitable for an automated pre-screening and classification of colorectal polyps into the known entities with different risk profiles. According to the latest WHO classification, key factors for distinguishing precursor lesions are serration, distribution of serration and cytological dysplasia. In this study, we investigate a learning scheme based on decision trees to identify image features, which precisely describe these key factors. It is shown that shape factors and histogram-based features extracted from digitized histological slides are suitable for computer-assisted prescreening and classification of precursor colorectal lesions.