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

Journal: Discover oncology
Published Date:

Abstract

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.

Authors

  • Tingru Shao
    Department of Stomatology, ZhuJiang Hospital of Southern Medical University, 253 Gongye Avenue Middle, Haizhu District, Guangzhou, 510280, China.
  • Peirong Ni
    Department of Stomatology, ZhuJiang Hospital of Southern Medical University, 253 Gongye Avenue Middle, Haizhu District, Guangzhou, 510280, China.
  • Chun Wang
    Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, China.
  • Jiahui Li
    College of Communication Engineering, Jilin University, Changchun, Jilin China.
  • Xiaozhi Lv
    Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.

Keywords

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