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<p>The aim of this study is to establish a deep learning model for predicting the pathological grade of oral squamous cell carcinoma(OSCC) based on whole slide images (WSIs). The histopathological images of 257 patients with OSCC were downloaded from TCGA database and randomly divided into training set, validation set and test set at a ratio of 7:2:1. The CPTAC-OSCC dataset, which contains 165 pathological slides of OSCC, was downloaded from the CPTAC database for external validation. The deep learning model constructed by CLAM algorithm achieved an AUC of 0.86 in the training set and an AUC of 0.71 in the external validation set. The results showed that the model had good prediction efficiency and good generalization ability. In addition, deep learning features were extracted from the model, and the prognostic risk model of OSCC was further constructed combined with transcriptome data to explore the differences in gene expression levels and immune cell infiltration levels between high and low risk groups. The deep learning model can accurately predict the pathological grade of patients with OSCC, which provides certain reference value for clinical diagnosis. The nomogram combining transcriptome data and patient clinical characteristics can be used as a prognostic classifier for clinical decision making and treatment.</p>

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Prediction of pathological grade of oral squamous cell carcinoma and construction of prognostic model based on deep learning algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月1日

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  • Tingru Shao,
  • Peirong Ni,
  • Chun Wang,
  • Jiahui Li,
  • Xiaozhi Lv

摘要

The aim of this study is to establish a deep learning model for predicting the pathological grade of oral squamous cell carcinoma(OSCC) based on whole slide images (WSIs). The histopathological images of 257 patients with OSCC were downloaded from TCGA database and randomly divided into training set, validation set and test set at a ratio of 7:2:1. The CPTAC-OSCC dataset, which contains 165 pathological slides of OSCC, was downloaded from the CPTAC database for external validation. The deep learning model constructed by CLAM algorithm achieved an AUC of 0.86 in the training set and an AUC of 0.71 in the external validation set. The results showed that the model had good prediction efficiency and good generalization ability. In addition, deep learning features were extracted from the model, and the prognostic risk model of OSCC was further constructed combined with transcriptome data to explore the differences in gene expression levels and immune cell infiltration levels between high and low risk groups. The deep learning model can accurately predict the pathological grade of patients with OSCC, which provides certain reference value for clinical diagnosis. The nomogram combining transcriptome data and patient clinical characteristics can be used as a prognostic classifier for clinical decision making and treatment.