Faculty, Staff and Student Publications
Language
English
Publication Date
6-17-2026
Journal
Journal of Clinical Medicine
DOI
10.3390/jcm15124692
PMID
42355860
PMCID
PMC13301221
PubMedCentral® Posted Date
6-17-2026
PubMedCentral® Full Text Version
Post-print
Abstract
Background/Objectives: Oral potentially malignant disorders (OPMDs) require accurate risk stratification to identify patients at the highest risk for severe oral epithelial dysplasia (OED) or oral squamous cell carcinoma (OSCC). We developed and internally validated the oral cancer numerical index (OCNI), a quantitative risk score derived from clinical features and deep learning-based brush cytology measurements.
Methods: This retrospective model development and internal validation study was conducted using data from the multicenter Grand Opportunity study. Prospectively recruited subjects with OPMD with complete data were divided at the subject level into a training set (n = 384) and a holdout test set (n = 164) using a 70:30 diagnosis-stratified split. The primary endpoint was severe OED or OSCC versus benign diagnoses, and mild and moderate OED. Predictors included age, sex, tobacco history, lesion color, lesion size, multiple lesions, ulcerative morphology, and the percentages of differentiated squamous epithelial and small round cells derived from deep learning-based cytology. Prespecified rule-out and rule-in thresholds were selected in the training set to target 90% sensitivity and 90% specificity, respectively, and then applied to the holdout test set.
Results: At the prespecified rule-out threshold (OCNI ≤ 37.6), sensitivity was 92% and negative predictive value was 97%. At the rule-in threshold (OCNI > 60.0), specificity was 89% and positive predictive value was 67%. Calibration was good in the holdout set (intercept, -0.07; slope, 1.13; Hosmer-Lemeshow p = 0.36), and OCNI increased significantly with worsening histopathologic severity.
Conclusions: OCNI provided an objective, clinically interpretable estimate of risk for severe OED or OSCC, with strong rule-out and rule-in performance and good calibration. These findings support further external validation of OCNI as an adjunctive tool for oral lesion risk stratification.
Keywords
oral potentially malignant disorders, oral epithelial dysplasia, oral squamous cell carcinoma, cytology, deep learning, artificial intelligence, intelligent cytology microfluidics
Published Open-Access
yes
Recommended Citation
, "Oral Cancer Numerical Index (OCNI): Development and Validation of a Cytology-Based Risk Assessment for Oral Lesions" (2026). Faculty, Staff and Student Publications. 210.
https://digitalcommons.library.tmc.edu/uthdb_docs/210