Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. However, most of the algorithms rely on the fully annotated datasets which are expensive to generate. In this work, we proposed a novel weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: (1) extracting discriminative areas in a histopathology image by employing the Multiple Instance Learning (MIL) algorithm based on Transformers, (2) representing the image by constructing a graph using the discriminative patches, and (3) classifying the image into its Gleason grades by developing a Graph Convolutional Neural Network (GCN) based on the gated attention mechanism. We evaluated our algorithm using publicly available datasets, including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross validated the algorithm on an independent dataset. Results show that the proposed model achieved state-of-the-art performance in the Gleason grading task in terms of accuracy, F1 score, and cohen-kappa. The code is available at https://github.com/NabaviLab/Prostate-Cancer.
翻译:前列腺癌是全世界男性最常见的癌症,也是美国癌症死亡的第二大原因。前列腺癌的预言性特征之一是Gleason对骨髓病理学图像进行分级。Gleason等级是根据血马托克林和Eosin(H&E)的肿瘤结构指派的。这个过程耗时费时,并具有观察者之间的变异性。在过去几年里,深层次的学习算法被用来分析直系病理学图像,为分级前列腺癌带来有希望的结果。然而,大多数算法都依赖于具有充分注释的数据集的分级。在这项工作中,我们提出了一个新的弱度超强的算法来对前列腺癌等级进行分类。提议的算法包括三个步骤:(1) 利用基于变异体的多度学习模型(MIL)算法,(2) 通过使用分析补分的图来代表图像, 以及(3) 将图像分类的准确性能分类, 包括GLEAR-TC的分级数据。在可获取的GAAL-CAL-CAL-Calalal-alalal-deal-deal-deal-deal-deal-deal-deal 数据中, laviewal-deal-deal-deal-deal-deal-deal-al-al-deal-deal-al-de 数据, laxal-al-al-deal-al-al-dal-dal-al-al-dal-dal-dal-dal-dal-dal-dal-d-d-d-d-dal-daldal-d-d-d-dal-d-d-dal-dal-d-d-d-d-al-al-d-d-d-d-al-al-al-dal-al-al-al-al-al-al-dal-al-deal-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-