我们旨在使用一种新的cyclin D1表达模式评分系统建立图像识别和生存预测模型，用于人乳头瘤病毒阴性口腔或咽鳞状细胞癌患者。对610例人乳头瘤病毒阴性口腔/咽鳞状细胞癌患者的临床病理数据进行了回顾性分析。利用Cox单变量和多变量风险回归分析，比较了cyclin D1表达模式评分与传统评分方法- cyclin D1表达水平评分- 在患者总体生存和无进展生存方面的关系。使用YOLOv5算法建立了一种基于cyclin D1表达模式评分系统的图像识别模型。从该模型中，使用DeepHit和DeepSurv算法建立了两个独立的生存预测模型。乳头状导管癌癌巢中cyclin D1存在三种表达模式。与cyclin D1表达水平评分相比，cyclin D1表达模式评分与口腔鳞状细胞癌患者（p < 0.0001）和咽鳞状细胞癌患者（p < 0.05）的预后显著相关。此外，它在口腔鳞状细胞癌（p < 0.0001）和咽鳞状细胞癌（p < 0.05）中是一个独立的预后危险因素。基于cyclin D1表达模式的图像识别模型显示平均测试组准确率为78.48% ± 4.31%。在总体生存预测模型中，DeepSurv和DeepHit建立的测试组平均一致性指数分别为0.71 ± 0.02和0.70 ± 0.01。将cyclin D1表达模式的图像识别模型与生存预测模型结合，对人乳头瘤病毒阴性口腔或咽鳞状细胞癌患者的总体生存预后具有相对良好的预测效果。©2023年John Wiley＆Sons A / S发表，John Wiley＆Sons Ltd发行。

We aimed to establish image recognition and survival prediction models using a novel scoring system of cyclin D1 expression pattern in patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma.The clinicopathological data of 610 patients with human papillomavirus-negative oral/oropharyngeal squamous cell carcinoma were analyzed retrospectively. Cox univariate and multivariate risk regression analyses were performed to compare cyclin D1 expression pattern scoring with the traditional scoring method-cyclin D1 expression level scoring-in relation to patients' overall and progression-free survival. An image recognition model employing the cyclin D1 expression pattern scoring system was established by YOLOv5 algorithms. From this model, two independent survival prediction models were established using the DeepHit and DeepSurv algorithms.Cyclin D1 had three expression patterns in oral and oropharyngeal squamous cell carcinoma cancer nests. Superior to cyclin D1 expression level scoring, cyclin D1 expression pattern scoring was significantly correlated with the prognosis of patients with oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). Moreover, it was an independent prognostic risk factor in both oral squamous cell carcinoma (p < 0.0001) and oropharyngeal squamous cell carcinoma (p < 0.05). The cyclin D1 expression pattern-derived image recognition model showed an average test set accuracy of 78.48% ± 4.31%. In the overall survival prediction models, the average concordance indices of the test sets established by DeepSurv and DeepHit were 0.71 ± 0.02 and 0.70 ± 0.01, respectively.Combined with the image recognition model of the cyclin D1 expression pattern, the survival prediction model had a relatively good prediction effect on the overall survival prognosis of patients with human papillomavirus-negative oral or oropharyngeal squamous cell carcinoma.© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.