Background and Objective

Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Furthermore, recent reports indicate that the presence of patterns of the Gleason scale such as the cribriform pattern may also correlate with a worse prognosis compared to other patterns belonging to the Gleason grade 4. Current clinical guidelines have indicated the convenience of highlight its presence during the analysis of biopsies. All these requirements suppose a great workload for the pathologist during the analysis of each sample, which is based on the pathologist’s visual analysis of the morphology and organisation of the glands in the tissue, a time-consuming and subjective task.

In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors’ knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies. This analysis must include the Gleason grading of local structures, the detection of cribriform patterns, and the Gleason scoring of the whole biopsy.

Methods

The methodological core of this work is a patch-wise predictive model based on convolutional neural networks able to determine the presence of cancerous patterns based on the Gleason grading system. In particular, we train from scratch a simple self-design architecture with three filters and a top model with global-max pooling. The cribriform pattern is detected by retraining the set of filters of the last convolutional layer in the network. Subsequently, a biopsy-level prediction map is reconstructed by bi-linear interpolation of the patch-level prediction of the Gleason grades. In addition, from the reconstructed prediction map, we compute the percentage of each Gleason grade in the tissue to feed a multi-layer perceptron which provides a biopsy-level score.

Results

In our SICAPv2 database, composed of 182 annotated whole slide images, we obtained a Cohen’s quadratic kappa of 0.77 in the test set for the patch-level Gleason grading with the proposed architecture trained from scratch. Our results outperform previous ones reported in the literature. Furthermore, this model reaches the level of fine-tuned state-of-the-art architectures in a patient-based four groups cross validation. In the cribriform pattern detection task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason scoring, we achieved a quadratic Cohen’s Kappa of 0.81 in the test subset.

Shallow CNN architectures trained from scratch outperform current state-of-the-art methods for Gleason grades classification. Our proposed model is capable of characterising the different Gleason grades in prostate tissue by extracting low-level features through three basic blocks (i.e. convolutional layer + max pooling). The use of global-max pooling to reduce each activation map has shown to be a key factor for reducing complexity in the model and avoiding overfitting. Regarding the Gleason scoring of biopsies, a multi-layer perceptron has shown to better model the decision-making of pathologists than previous simpler models used in the literature.

中文翻译：

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深入了解格里森评分标准：一种用于组织学前列腺分级和筛状图案检测的自动端对端系统。

背景与目的

前列腺癌是影响全世界男性的最常见疾病之一。格里森评分系统是前列腺癌的主要诊断和预后工具。此外，最近的报道表明，与属于格里森4级的其他模式相比，格里森量表的模式（如网状模式）的存在也可能与预后更差相关。活检分析。所有这些要求为病理学家在每个样品的分析过程中承担了巨大的工作量，这是基于病理学家对组织中腺体的形态和组织的视觉分析，这是一项耗时且主观的任务。

近年来，随着数字化设备的发展，计算机视觉技术在活检分析中的应用已经增加。然而，就作者所知，文献中尚未研究出自动检测属于格里森4级的单个筛状图案的算法的开发。本文提出的工作目的是开发一种基于深度学习的系统，该系统能够支持病理学家进行前列腺活检的日常分析。该分析必须包括局部结构的Gleason分级，筛状图案的检测以及整个活检的Gleason评分。

方法

这项工作的方法论核心是基于卷积神经网络的基于补丁的预测模型，该模型能够基于格里森分级系统确定癌症模式的存在。特别是，我们从头开始训练一个简单的自我设计架构，该架构具有三个过滤器和一个具有global-max池的顶级模型。通过重新训练网络中最后一个卷积层的过滤器集合来检测筛状图案。随后，通过对格里森等级的斑块级预测进行双线性插值来重建活检级预测图。此外，从重建的预测图上，我们计算组织中每个Gleason等级的百分比，以喂食提供活检水平评分的多层感知器。

结果

在我们的由182张带注释的整个幻灯片图像组成的SICAPv2数据库中，我们在补丁级格里森分级的测试集中获得了科恩的二次kappa值为0.77，而拟议的体系结构是从头开始训练的。我们的结果优于文献报道的结果。此外，该模型在基于患者的四组交叉验证中达到了微调的最新架构水平。在筛状图案检测任务中，ROC曲线下的面积为0.82。关于活检格里森评分，我们在测试子集中获得了0.81的二次Cohenκ值。

从头开始训练的浅层CNN架构胜过当前用于Gleason等级分类的最新方法。我们提出的模型能够通过三个基本块（即卷积层+最大池）提取低级特征来表征前列腺组织中不同的Gleason等级。使用全局最大池来减少每个激活图已显示是减少模型复杂性并避免过度拟合的关键因素。关于活检的格里森评分，多层感知器已显示出比以前文献中使用的更简单的模型更好地建模病理学家的决策。