A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer Number

Authors

Xinyi Zhang
Frederico O Gleber-Netto
Shidan Wang
Kevin W Jin
Donghan M Yang
Ann M Gillenwater
Jeffrey N Myers
Renata Ferrarotto
Curtis R Pickering
Guanghua Xiao

Publication Date

7-31-2023

Journal

Cancers

Abstract

Head and neck squamous cell carcinoma (HNSCC), specifically in the oral cavity (oral squamous cell carcinoma, OSCC), is a common, complex cancer that significantly affects patients' quality of life. Early diagnosis typically improves prognoses yet relies on pathologist examination of histology images that exhibit high inter- and intra-observer variation. The advent of deep learning has automated this analysis, notably with object segmentation. However, techniques for automated oral dysplasia diagnosis have been limited to shape or cell stain information, without addressing the diagnostic potential in counting the number of cell layers in the oral epithelium. Our study attempts to address this gap by combining the existing U-Net and HD-Staining architectures for segmenting the oral epithelium and introducing a novel algorithm that we call Onion Peeling for counting the epithelium layer number. Experimental results show a close correlation between our algorithmic and expert manual layer counts, demonstrating the feasibility of automated layer counting. We also show the clinical relevance of oral epithelial layer number to grading oral dysplasia severity through survival analysis. Overall, our study shows that automated counting of oral epithelium layers can represent a potential addition to the digital pathology toolbox. Model generalizability and accuracy could be improved further with a larger training dataset.

Keywords

oral cavity and pharyngeal cancer, oral epithelium, histology, medical image analysis, deep learning

Comments

Supplementary Materials

Data Availability Statement

PMID: 37568707