A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study

Yuxin Wu, Can F. Koyuncu, Paula Toro, German Corredor, Qianyu Feng, Christina Buzzy, Matthew Old, Theodoros Teknos, Stephen Thaddeus Connelly, Richard C. Jordan, Krystle A. Lang Kuhs, Cheng Lu, James S. Lewis, Anant Madabhushi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objective: Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmentation in OC-SCC have not been independently tested in a multi-center setting. In this study, we aimed to investigate the effectiveness and applicability of a convolutional neural network (CNN) model to perform epithelial segmentation using digitized H&E-stained diagnostic slides from OC-SCC patients in a multi-center setting. Methods: A CNN model was developed to segment the epithelial regions of digitized slides (n = 810), retrospectively collected from five different centers. Deep learning models were trained and validated using well-annotated tissue microarray (TMA) images (n = 212) at various magnifications. The best performing model was locked down and used for independent testing with a total of 478 whole-slide images (WSIs). Manually annotated epithelial regions were used as the reference standard for evaluation. We also compared the model generated results with IHC-stained epithelium (n = 120) as the reference. Results: The locked-down CNN model trained on the TMA image training cohorts with 10x magnification achieved the best segmentation performance. The locked-down model performed consistently and yielded Pixel Accuracy, Recall Rate, Precision Rate, and Dice Coefficient that ranged from 95.8% to 96.6%, 79.1% to 93.8%, 85.7% to 89.3%, and 82.3% to 89.0%, respectively for the three independent testing WSI cohorts. Conclusion: The automated model achieved a consistently accurate performance for automated epithelial region segmentation compared to manual annotations. This model could be integrated into a computer-aided diagnosis or prognosis system.

Original languageEnglish
Article number105942
JournalOral Oncology
Volume131
DOIs
StatePublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

ASJC Scopus subject areas

  • Oral Surgery
  • Oncology
  • Cancer Research

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