Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis

Background There is a low reproducibility of the Gleason scores that determine the grade group of prostate cancer given the intra‐ and interobserver variability among pathologists. This study aimed to develop an automated approach for estimating prostate cancer grade groups based on features obtained from histological image analysis. Methods Fifty‐nine patients who underwent radical prostatectomy were selected under the approval of the institutional review board of our university hospital. For estimation, we followed the grade group criteria provided by the International Society of Urological Pathology in 2014. One hundred eight specimen slides obtained from the patients were digitized to extract 110 regions of interest (ROI) from hematoxylin and eosin‐stained histological images using a digital whole slide scanner at ×20 magnification with a pixel size of 0.4 μm. Each color pixel value in the ROI was decomposed into six intensities corresponding to the RGB (red, green, and blue) and HSV (hue, saturation, and value) color models. Image features were extracted by histological image analysis, obtaining 54 features from the ROI based on histogram and texture analyses in the six types of decomposed histological images. Then, 40 representative features were selected from the 324 histological image features based on statistically significant differences (P