Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China; Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China.
Hunan Key Laboratory of Oral Health Research, Xiangya Stomatological Hospital, Central South University, Changsha, China.
Lab Invest. 2023 Aug;103(8):100173. doi: 10.1016/j.labinv.2023.100173. Epub 2023 May 8.
Accurate prognostic stratification of oral leukoplakia (OLK) with risk of malignant transformation into oral squamous cell carcinoma is crucial. We developed an objective and powerful pathomics-based model for the prediction of malignant transformation in OLK using hematoxylin and eosin (H&E)-stained images. In total, 759 H&E-stained images from multicenter cohorts were included. A training set (n = 489), validation set (n = 196), and testing set (n = 74) were used for model development. Four deep learning methods were used to train and validate the model constructed using H&E-stained images. Pathomics features generated through deep learning combined with machine learning algorithms were used to develop a pathomics-based model. Immunohistochemical staining of Ki67, p53, and PD-L1 was used to interpret the black box of the model. Pathomics-based models predicted the malignant transformation of OLK (validation set area under curve [AUC], 0.899; testing set AUC, 0.813) and significantly identified high-risk and low-risk populations. The prediction performance of malignant transformation from dysplasia grading (validation set AUC, 0.743) was lower than that of the pathomics-based model. The expressions of Ki67, p53, and PD-L1 were correlated with various pathomics features. The pathomics-based model accurately predicted the malignant transformation of OLK and may be useful for the objective and rapid assessment of the prognosis of patients with OLK.
准确预测口腔白斑（OLK）恶性转化为口腔鳞状细胞癌的风险对于口腔医学非常重要。我们开发了一种基于组织病理学的客观而强大的模型，用于预测 OLK 中恶性转化的风险，该模型使用苏木精和伊红（H&E）染色图像。总共纳入了来自多中心队列的 759 张 H&E 染色图像。训练集（n=489）、验证集（n=196）和测试集（n=74）用于模型开发。使用四种深度学习方法来训练和验证使用 H&E 染色图像构建的模型。通过深度学习生成的组织病理学特征与机器学习算法相结合，用于开发组织病理学模型。Ki67、p53 和 PD-L1 的免疫组织化学染色用于解释模型的“黑盒”。组织病理学模型预测了 OLK 的恶性转化（验证集 AUC，0.899；测试集 AUC，0.813），并显著区分了高风险和低风险人群。从异型增生分级（验证集 AUC，0.743）预测恶性转化的预测性能低于基于组织病理学的模型。Ki67、p53 和 PD-L1 的表达与各种组织病理学特征相关。该组织病理学模型准确预测了 OLK 的恶性转化，可能有助于客观快速评估 OLK 患者的预后。
