Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.

ABSTRACT:

Background

The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation.

Methods

Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer ac

SUBMITTER: McRae MP 

PROVIDER: S-EPMC7078980 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature