Novel computational image analysis to predict regional nodal disease for early-stage oral cancer

Abstract

Objective: Nodal disease (N+) for early-stage oral squamous cell carcinoma (OSCC) is the most significant prognostic factor for survival. There is a lack of effective predictor to justify prophylactic neck dissection. Quantitative tissue pathology (QTP) has shown its promise in providing an objective means for diagnosis and prognosis of many cancer types. We conducted a pilot study on the utilization of QTP to evaluate risk of nodal disease. Study design: Retrospective case-control studySubjects and methods: Histological sections from 15 primary tumors of clinically node-negative (N0) patients were stained with Feulgen-Thionin followed by acquisition of digital images and image processing to measure the mean and variance of nuclear phenotype and tissue architecture features from 45,253 nuclei of 45 tumor nests. Association between features and nodal disease outcome (N0 or N+) was investigated using nested analysis of variance adjusted by patient. Ability to discriminate between N0 and N+ was analyzed using multivariate logistic regression and receiver operating characteristics (ROC) curve analysis. P-value<0.05 (2-sided) was considered significant. Results:The N+ group presented higher mean values of chromatin condensation levels and cell density compared to those of the N0 group. ROC curve showed a strong discriminative ability of chromatin condensation levels between the N+ and N0 groups with a sensitivity and specificity of 1.0 and 0.75, respectively. Conclusion:This study reports the first-ever data on QTP as a risk assessment tool for nodal disease in early-stage OSCCs. Such computational imaging analysis potentially provides a new objective approach to predict regional nodal disease.