Histopathology of Hematoxylin and Eosin (H&E)-stained tissue obtained from biopsy is commonly used in prostate cancer (PCa) diagnosis. Automatic PCa classification of digitized H&E slides has been developed before, but no attempts have been made to classify PCa using additional tissue stains registered to H&E. In this paper, we demonstrate that using H&E, Ki67 and p63-stained (3-stain) tissue improves PCa classification relative to H&E alone. We also show that we can infer PCa-relevant Ki67 and p63 information from the H&E slides alone, and use it to achieve H&E-based PCa classification that is comparable to the 3-stain classification. Reported improvements apply to classifying benign vs. malignant tissue, and low grade (Gleason group 2) vs. high grade (Gleason groups 3,4,5) cancer. Specifically, we conducted four classification tasks using 333 tissue samples extracted from 231 radical prostatectomy patients: regression tree-based classification using either (i) 3-stain features, with a benign vs malignant area under the curve (AUC = 92.9%), or (ii) real H&E features and H&E features learned from Ki67 and p63 stains (AUC = 92.4%), as well as deep learning classification using either (iii) real 3-stain tissue patches (AUC = 94.3%) and (iv) real H&E patches and generated Ki67 and p63 patches (AUC = 93.0%) using a deep convolutional generative adversarial network. Classification performance was assessed with Monte Carlo cross validation and quantified in terms of the Area Under the Curve, Brier score, sensitivity, and specificity. Our results are interpretable and indicate that the standard H&E classification could be improved by mimicking other stain types.

从活检获得的苏木精和曙红（H＆E）染色组织的组织病理学通常用于前列腺癌（PCa）诊断。以前已经开发了对数字化H＆E玻片进行自动PCa分类的方法，但是尚未尝试使用注册到H＆E的其他组织染色剂对PCa进行分类。在本文中，我们证明与单独使用H＆E相比，使用H＆E，Ki67和p63染色（3色）组织可改善PCa分类。我们还表明，我们可以仅从H＆E幻灯片中推断出与PCa相关的Ki67和p63信息，并使用它来实现与3色分类相当的基于H＆E的PCa分类。已报道的改进适用于将良性与恶性组织分类，以及将低度（格里森组2）与高等级（格里森组3、4、5）进行分类。特别，我们使用了从231例前列腺癌根治术患者中提取的333个组织样本进行了四项分类任务：基于回归树的分类，使用（i）3种染色特征，曲线下的良性与恶性面积（AUC = 92.9％），或（ii ）真实的H＆E特征和从Ki67和p63染色获得的H＆E特征（AUC = 92.4％），以及使用（iii）真正的3色组织斑块（AUC = 94.3％）和（iv）真正的H＆E斑块进行的深度学习分类并使用深度卷积生成对抗网络生成Ki67和p63补丁（AUC = 93.0％）。分类性能通过蒙特卡洛交叉验证进行评估，并根据“曲线下面积”，“布莱尔得分”，敏感性和特异性进行量化。我们的结果是可以解释的，并表明标准H＆