Journal Article

Deep learning‐based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia

Gomez, Ricardo Santiago, Williams, Michelle, Zhang, Xinyi, Gleber‐Netto, Frederico O., William, William N., Xiao, Guanghua, Vigneswaran, Nadarajah, Ferrarotto, Renata, Pickering, Curtis Rg, Palsgrove, Doreen, Papadimitrakopoulou, Vassiliki, Bishop, Justin, Wang, Shidan, Bell, Diana, Sarkar, Arunangshu, Heymach, John V., Lippman, Scott M., Myers, Jeffrey N., Gillenwater, Ann M., Martins‐Chaves, Roberta Rayra

2023

Overview

Background Oral leukoplakia (OL) is associated with an increased risk for oral cancer (OC) development. Prediction of OL cancer progression may contribute to decreased OC morbidity and mortality by favoring early intervention. Current OL progression risk assessment approaches face large interobserver variability and is weakly prognostic. We hypothesized that convolutional neural networks (CNN)‐based histology image analyses could accelerate the discovery of better OC progression risk models. Methods Our CNN‐based oral mucosa risk stratification model (OMRS) was trained to classify a set of nondysplastic oral mucosa (OM) and a set of OC H&E slides. As a result, the OMRS model could identify abnormal morphological features of the oral epithelium. By applying this model to OL slides, we hypothesized that the extent of OC‐like features identified in the OL epithelium would correlate with its progression risk. The OMRS model scored and categorized the OL cohort (n = 62) into high‐ and low‐risk groups. Results OL patients classified as high‐risk (n = 31) were 3.98 (95% CI 1.36–11.7) times more likely to develop OC than low‐risk ones (n = 31). Time‐to‐progression significantly differed between high‐ and low‐risk groups (p = 0.003). The 5‐year OC development probability was 21.3% for low‐risk and 52.5% for high‐risk patients. The predictive power of the OMRS model was sustained even after adjustment for age, OL site, and OL dysplasia grading (HR = 4.52, 1.5–13.7). Conclusion The ORMS model successfully identified OL patients with a high risk of OC development and can potentially benefit OC early diagnosis and prevention policies. We developed a deep learning image analysis model using a convolutional neural network to predict the risk of oral cancer development from whole slide images in a patient with oral leukoplakia. The model successfully discriminated patients into two groups with distinct risk for oral cancer development, which represents a potential ancillary tool for the management of oral leukoplakia and may contribute to the early detection and treatment of oral cancer.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Algorithms / Biopsy / carcinogenesis / Connective tissue / convolutional neural network / Datasets / Deep Learning / Disease / Disease progression / Dysplasia / Epithelium / Humans / Image processing / Leukokeratosis / Leukoplakia, Oral - diagnostic imaging / Leukoplakia, Oral - etiology / Leukoplakia, Oral - pathology / Morbidity / Morphology / Mouth Mucosa - pathology / Mouth Neoplasms - diagnostic imaging / Mouth Neoplasms - pathology / Mucosa / Neural networks / Oral cancer / oral leukoplakia / Pathology / patient prognosis / Patients / precancer / Probability / Prognosis / Risk assessment / Risk groups / whole slide imaging