Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer
Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer
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DOI：
10.1109/jbhi.2016.2565515
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发表时间：
2017-07-01
影响因子：
7.7
通讯作者：
Gurcan, Metin
中科院分区：
文献类型：
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作者：
Niazi, M. Khalid Khan;Yao, Keluo;Gurcan, Metin
几十年来，组织病理学特征，特别是格里森分级系统，对前列腺癌的诊断、治疗和预后有重要贡献。然而，前列腺癌在生物学行为上表现出巨大的异质性，因此建立更好的预后和预测标志物对于临床定位和新诊断恶性肿瘤的男性的个性化治疗尤为重要。许多自动分级系统已开发格里森分级，但在医学界的接受程度一直缺乏，由于较差的可解释性。为了克服这个问题，我们开发了一套视觉上有意义的特征来区分低级别和高级别前列腺癌。视觉上有意义的特性集由luminal和architectural特性组成。对于光腔特征，我们计算：1)原子核到它们最近的光腔空间的最短路径；2)上皮细胞核与细胞核总数的比值。如果一个细胞核与腔隙之间的最短路径不包含任何其他细胞核，则认为它是上皮细胞核；3)所有原子核到最近腔空间的平均最短距离。对于结构特征，我们使用方向滤波器组计算基质和核的方向变化。这些特性被用来创建两个子空间；一个前列腺影像组织病理学评估为低级别，另一个为高级别。与子空间相关联的等级被认为是对测试图像的预测，其结果是最小的重建误差。为了进行训练，我们使用了43个感兴趣区域（ROI）图像，这些图像是从The Cancer Genome Atlas （TCGA）数据库的25张前列腺全切片图像中提取的。为了进行测试，我们使用了一个独立的数据集，从30张前列腺全幻灯片图像中提取了88个roi。该方法对所考虑的病例谱的训练和测试准确率分别为93.0%和97.6%。应用视觉上有意义的特征为前列腺癌分级提供了有希望的准确性和一致性。
Histopathologic features, particularly Gleason grading system, have contributed significantly to the diagnosis, treatment, and prognosis of prostate cancer for decades. However, prostate cancer demonstrates enormous heterogeneity in biological behavior, thus establishing improved prognostic and predictive markers is particularly important to personalize therapy of men with clinically localized and newly diagnosed malignancy. Many automated grading systems have been developed for Gleason grading but acceptance in the medical community has been lacking due to poor interpretability. To overcome this problem, we developed a set of visually meaningful features to differentiate between low-and high-grade prostate cancer. The visually meaningful feature set consists of luminal and architectural features. For luminal features, we compute: 1) the shortest path from the nuclei to their closest luminal spaces; 2) ratio of the epithelial nuclei to the total number of nuclei. A nucleus is considered an epithelial nucleus if the shortest path between it and the luminal space does not contain any other nucleus; 3) average shortest distance of all nuclei to their closest luminal spaces. For architectural features, we compute directional changes in stroma and nuclei using directional filter banks. These features are utilized to create two subspaces; one for prostate images histopathologically assessed as low grade and the other for high grade. The grade associated with a subspace, which results in the minimum reconstruction error is considered as the prediction for the test image. For training, we utilized 43 regions of interest (ROI) images, which were extracted from 25 prostate whole slide images of The Cancer Genome Atlas (TCGA) database. For testing, we utilized an independent dataset of 88 ROIs extracted from 30 prostate whole slide images. The method resulted in 93.0% and 97.6% training and testing accuracies, respectively, for the spectrum of cases considered. The application of visually meaningful features provided promising levels of accuracy and consistency for grading prostate cancer.