常规血红木和嗜酸性染色（H＆E）病理切片照片从人类乳头瘤病毒相关口咽鳞状细胞癌（HPV + OpSCC）中包含丰富的预后信息。在本研究中，我们开发了一个高内容图像分析（HCIA）工作流来量化HPV + OpSCC患者的H＆E图像特征，以识别预后特征并预测患者预后。首先，我们开发了一个开源的HCIA工具，用于单个细胞分割和H＆E图像分类。随后，我们使用我们的HCIA工具分析了一组889张诊断性H＆E切片图像，这些切片来自具有有利（FO，n = 60）或不利（UO，n = 30）预后的HPV + OpSCC患者的回顾性队列。我们已经确定并测量了31个预后特征，这些特征在每个样本中都被量化，并用于训练神经网络（NN）模型来预测患者预后。单变量和多变量统计分析显示，FO患者和UO患者之间在31个和17个变量中分别具有显着差异（P <0.05）。在单个图像水平上，NN模型在测试集和验证集中识别FO和UO患者的总体准确率分别为72.5％和71.2％。考虑每个患者的10张图像时，NN模型的准确率在测试集中增加到86.7％。我们的开源H＆E分析工作流和预测模型确认了先前报告的预后特征，并确定了新的因素，其具有令人满意的准确性来预测HPV + OpSCC的预后。我们的工作支持在数字病理学中使用机器学习，以在常规诊断病理学中利用临床相关的特征，而不需要额外的生物标志物。版权所有©2023作者。 由Elsevier Ltd.出版。保留所有权利。

Routine haematoxylin and eosin (H&E) photomicrographs from human papillomavirus-associated oropharyngeal squamous cell carcinomas (HPV + OpSCC) contain a wealth of prognostic information. In this study, we developed a high content image analysis (HCIA) workflow to quantify features of H&E images from HPV + OpSCC patients to identify prognostic features and predict patient outcomes.First, we have developed an open-source HCIA tool for single-cell segmentation and classification of H&E images. Subsequently, we have used our HCIA tool to analyse a set of 889 images from diagnostic H&E slides in a retrospective cohort of HPV + OpSCC patients with favourable (FO, n = 60) or unfavourable (UO, n = 30) outcomes. We have identified and measured 31 prognostic features which were quantified in each sample and used to train a neural network (NN) model to predict patient outcomes.Univariate and multivariate statistical analyses revealed significant differences between FO and UO patients in 31 and 17 variables, respectively (P < 0.05). At the single-image level, the NN model had an overall accuracy of 72.5% and 71.2% in recognising FO and UO patients when applied to test or validation sets, respectively. When considering 10 images per patient, the accuracy of the NN model increased to 86.7% in the test set.Our open-source H&E analysis workflow and predictive models confirm previously reported prognostic features and identifies novel factors which predict HPV + OpSCC outcomes with promising accuracy. Our work supports the use of machine learning in digital pathology to exploit clinically relevant features in routine diagnostic pathology without additional biomarkers.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.