人类乳头瘤病毒（HPV）相关的扁桃体咽部鳞状细胞癌（OPSCC）是一个在西方国家发病率上升的OPSCC亚组，总体预后良好。多项证据表明，HPV相关肿瘤不是同质性肿瘤实体，强调了准确的预后生物标志物的必要性。在这项回顾性、多机构研究中，涉及了4个中心和一个数据库的906名患者，我们开发了一个名为OPSCCnet的深度学习算法，用于分析标准H&E染色的计算，以获得与预后相关的患者水平评分，并将其与联合HPV-DNA和p16状态进行比较。在将OPSCCnet与HPV状态进行比较时，该算法表现出良好的整体性能，测试组（n = 639）的平均接受者操作特征曲线下面积（AUROC）= 0.83（95% CI = 0.77-0.9），通过在HPV阳性类别的概率方差上应用固定阈值对案例进行筛选，AUROC可以提高到0.88，这是HPV异质性的潜在替代标志物。OPSCCnet可以用作筛选工具，在各种临床情况下优于黄金标准的HPV测试（OPSCCnet：五年生存率：96% [95% CI = 90-100%]；HPV测试：五年生存率：80% [95% CI = 71-90%]）。这可以通过三分级阈值的多变量分析得到证实（OPSCCnet：高HR = 0.15 [95% CI = 0.05-0.44]，中等HR = 0.58 [95% CI = 0.34-0.98]，p = 0.043，Cox比例风险模型，n = 211；HPV测试：HR = 0.29 [95% CI = 0.15-0.54]，p < 0.001，Cox比例风险模型，n = 211）。总之，我们的研究结果表明，通过分析标准的吉吉像素血红蛋白和嗜酸性染色（H&E）组织切片图像，OPSCCnet在各种临床情况下显示出优于p16/HPV-DNA检测的性能，特别是在准确分层这些患者方面。© 2023。Springer Nature有限公司。

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90-100%]; HPV testing: five-year survival rate: 80% [95% CI = 71-90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05-0.44], intermediate HR = 0.58 [95% CI = 0.34-0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15-0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients.© 2023. Springer Nature Limited.