Fourier transformation based texture analysis for differentiating between hyperplastic polyps and sessile serrated adenomas

Abstract

AbstractColorectal cancer (CRC) is the third most common type of cancer. One major pathway involved in the development of CRC is the serrated pathway. Colorectal polyps can be divided in benign, like small hyperplastic polyps and premalignant polyps, like the sessile serrated adenomas (SSA) that has a significant potential of malignant transformation. The morphological similarity between these types of polyp, not‐infrequently raises diagnostic difficulties. This study aimed to morphologically differentiate between hyperplastic polyps (HP) and SSAs by using automated computerized texture analysis of Fourier transformed histological images. Thirty images of HP and 58 images of SSA were analyzed by computerized texture analysis. A fast Fourier transformation was applied to the images. The Fourier frequency plots were further transformed into gray level co‐occurrence matrices and four textural variables were extracted: entropy, correlation, contrast, and homogeneity. Our study is the first to combine this type of analysis for automated classification of colonic neoplasia. The results were analyzed using statistical and neural network (NNET) classification models. The predictive values of these classifiers were compared. The statistical regression algorithm presented a sensitivity of 95% to detect the SSA and a specificity of 80% to detect the HP. The NNET analysis was superior to the statistical analysis displaying a classification accuracy of 100%. The results of this study have confirmed the hypothesis that Fourier based texture image analysis is helpful in differentiating between HP and SSA.Research HighlightsColorectal polyps can be divided in benign, like hyperplastic polyps (HP) and premalignant, like the sessile serrated adenomas (SSA). There is a high morphologic similarity between these two types of polyp that not‐infrequently raises diagnostic difficulties. The results of our morphometric analysis that were used to build a neural network based model of prediction of the polyp types, have a great clinical importance of identifying SSA polyps which have significant potential of malignant progression as compared to HP.