Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis
Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis
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DOI：
10.1002/pros.23943
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发表时间：
2019-12-23
期刊：
影响因子：
2.8
通讯作者：
Oda, Yoshinao
中科院分区：
文献类型：
--
作者：
Hossain, Alamgir;Arimura, Hidetaka;Oda, Yoshinao
背景考虑到病理学家之间观察者内和观察者间的差异，确定前列腺癌分级组的格里森评分的重现性较低。本研究旨在开发一种基于组织学图像分析获得的特征来估计前列腺癌分级组的自动化方法。方法选择经本院机构审查委员会批准的59例乳腺癌根治术患者。为了进行估计，我们遵循了国际泌尿病理学会2014年提供的分级组标准。将从患者获得的108个标本载玻片数字化，以使用像素大小为0.4 μ m的数字全载玻片扫描仪以20倍放大率从苏木精和曙红染色的组织学图像中提取110个感兴趣区域（ROI）。ROI中的每个颜色像素值被分解为对应于RGB（红色、绿色和蓝色）和HSV（色调、饱和度和明度）颜色模型的六个强度。通过组织学图像分析提取图像特征，对6种类型的组织学图像进行直方图和纹理分析，从感兴趣区域提取54个特征。然后，基于高（>= 3，Gleason评分>= 4 + 3）和低（>= 3，Gleason评分>= 4 + 3）的平均图像特征值之间的统计学显著差异（P <0.05），从324个组织学图像特征中选择40个代表性特征。
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 x20 magnification with a pixel size of 0.4 mu m. 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 < .05) between the mean image feature values for high (>= 3, Gleason score >= 4 + 3) and low (