Risk assessment of oral leukoplakia by DNA content enhanced by machine learning models

The difficulty in risk assessing oral leukoplakia (OL) for malignant transformation has recently been reduced as novel strategies based on DNA content measurements have been providing consistent and reproducible predictive values, with a major advantage of using archived material. Regrettably, most such approaches are based on costly equipment and human resources that are not widely available, especially in populations with limited access to dedicated technology. The aim of this study was to investigate DNA content as a predictive marker of malignant transformation adapting novel image-based cytometry advances to a conventional flow cytometry context. Nuclei isolation was performed enzymatically on thick sections from paraffin embedded tissue from 97 cases, 18 that progressed to oral carcinoma and 79 that did not. Flow cytometry was used to establish DNA content based on propidium iodide fluorescent labeling of nuclear suspensions. Multiple logistic regression was used to establish DNA content thresholds for DNA content parameters (G1, S-phase, G2, 4cER) to facilitate risk classification criteria. The predictive values of each marker were calculated from Kaplan-Meier and the Log-rank tests (p