目前，口腔和喉咙上皮病变的诊断依赖于基于世界卫生组织（WHO）分类的组织学标准，这可能导致不同观察者之间存在差异。基于免疫组织化学（IHC）的综合诊断方法将有助于解释上皮病变模糊组织学结果。本研究应用IHC评估了104名患者的114例口腔和喉咙上皮病变中p53和Ki-67的表达。采用Logistic回归分析和决策树算法开发了一个评分系统和预测模型以区分上皮病变。采用Cohen's kappa系数评估观察者间的差异性，采用下一代测序（NGS）和IHC比较了TP53突变和p53表达模式。p53的两种表达模式，即弥漫型（HI型）和缺失型（LS型），以及Ki-67的HI型模式与高级别异型增生（HGD）或鳞状细胞癌（SqCC）显著相关。基于p53和Ki-67表达模式的评分系统将上皮病变分类为非异型增生（ND）或低级别异型增生（LGD），以及SqCC或HGD，准确度和接受者操作特征曲线下面积（AUC）分别为84.6%和0.85。采用p53和Ki-67表达模式构建的决策树模型将上皮病变分类为ND、LGD和第2组，包括HGD或SqCC，准确度和AUC分别为75%和0.87。综合诊断与非常一致的一致性更好（加权kappa 0.92，非加权kappa 0.88）。确认p53的HI型和LS型与错义突变和无义/移码突变相关。基于TP53突变和p53表达模式之间的相关性，我们开发了一个诊断预测模型。这些结果表明，基于p53和Ki-67表达模式的评分系统可以区分上皮病变，尤其是在形态学特征模糊的情况下。 版权所有 © 2023 Royal College of Pathologists of Australasia. Elsevier B.V.出版。保留所有权利。

Oral and laryngeal epithelial lesions are currently diagnosed using histological criteria based on the World Health Organization (WHO) classification, which can cause interobserver variability. An integrated diagnostic approach based on immunohistochemistry (IHC) would aid in the interpretation of ambiguous histological findings of epithelial lesions. In the present study, IHC was used to evaluate the expression of p53 and Ki-67 in 114 cases of oral and laryngeal epithelial lesions in 104 patients. Logistic regression analysis and decision tree algorithm were employed to develop a scoring system and predictive model for differentiating the epithelial lesions. Cohen's kappa coefficient was used to evaluate interobserver variability, and next-generation sequencing (NGS) and IHC were used to compare TP53 mutation and p53 expression patterns. Two expression patterns for p53, namely, diffuse expression type (pattern HI) and null type (pattern LS), and the pattern HI for Ki-67 were significantly associated with high-grade dysplasia (HGD) or squamous cell carcinoma (SqCC). With an accuracy and area under the receiver operating characteristic curve (AUC) of 84.6% and 0.85, respectively, the scoring system based on p53 and Ki-67 expression patterns classified epithelial lesions into two types: non-dysplasia (ND) or low-grade dysplasia (LGD) and SqCC or HGD. The decision tree model constructed using the p53 and Ki-67 expression patterns classified epithelial lesions into ND, LGD, and group 2, including HGD or SqCC, with an accuracy and AUC of 75% and 0.87, respectively. The integrated diagnosis had a better correlation with near perfect agreement (weighted kappa 0.92, unweighted kappa 0.88). The patterns HI and LS for p53 were confirmed to be correlated with missense mutations and nonsense/frameshift mutations, respectively. A predictive model for diagnosis was developed based on the correlation between TP53 mutation and p53 expression patterns. These results indicate that the scoring system based on p53 and Ki-67 expression patterns can differentiate epithelial lesions, especially in cases when the morphological features are ambiguous.Copyright © 2023 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.