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

Abstract

Background: There is low reproducibility of the grade group of prostate cancer due to 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 (RO I) from hematoxylin and eosin stained histological images using a digital whole slide scanner at 20× magnification with 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 < 0.05) between the mean image feature values for high (≥ 3, Gleason score ≥ 4 + 3) and low (≤ 2, Gleason score ≤ 3 + 4) grade groups. The relationship between grade groups and the most representative image feature (i.e., complexity) was approximated using regression to estimate real-number grade groups defined by continuous numerical grading. Finally, the grade groups were expressed as the conventional grade groups (i.e., integers from 1 to 5) using a piecewise step function. Results: The grade groups were correctly estimated by the proposed approach without errors on training (70 ROIs) and validation (40 ROIs) data Conclusions: Our results suggest that the proposed approach may support pathologists during evaluation of grade groups for prostate cancer, thus mitigating intra-and inter-observer variability.

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

“…Our ratiometric approach used a combination of red, green, and blue pixel intensity values. Automated histological studies using different wavelengths of light have primarily been applied in cancer applications 33 , 34 . One breast cancer study used multispectral (16-wavelengths) and RGB data to spectrally unmix breast cancer markers 33 .…”

Stratification of prostate cancer patients into low‐ and high‐grade groups using multiparametric magnetic resonance radiomics with dynamic contrast‐enhanced image joint histograms

“…All 44 patients with prostate cancer who underwent radical prostatectomy were retrospectively selected in this study with the approval of the institutional review board of Kyushu University Hospital, Japan. The radical prostatectomy specimens were processed as described previously 26 . Table 1 shows the patient characteristics and the significant differences between the training and test datasets in this study.…”

“…The radical prostatectomy specimens were processed as described previously. 26 Table 1 shows the patient characteristics and the significant differences between the training and test datasets in this study. The ISUP GGs and contours of all prostate cancer regions were determined as references based on the consensus between two pathologists (Fumio Kinoshita and Kenjiro Imada) on hematoxylin and eosin-stained histological images digitized using a digital whole slide scanner (Nanozoomer-SQ; Hamamatsu Photonics KK) at 20× magnification with a pixel size of 0.4 μm.…”