Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms.

Abstract

A major challenge for the early diagnosis of oral cancer is the ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive squamous cell carcinoma (SCC) from those at low risk. Our group has previously used high-resolution image analysis algorithms to quantify the nuclear phenotypic changes occurring in OPLs. This approach, however, requires a manual selection of nuclei images. Here, we investigated a new, semi-automated algorithm to identify OPLs at high risk of progressing into invasive SCC from those at low risk using Random Forests, a tree-based ensemble classifier. We trained a sequence of classifiers using morphometric data calculated on nuclei from 29 normal, 5 carcinoma in situ (CIS) and 28 SCC specimens. After automated discrimination of nuclei from other objects (i.e., debris, clusters, etc.), a nuclei classifier was trained to discriminate abnormal nuclei (8,841) from normal nuclei (5,762). We extracted voting scores from this trained classifier and created an automated nuclear phenotypic score (aNPS) to identify OPLs at high risk of progression. The new algorithm showed a correct classification rate of 80% (80.6% sensitivity, 79.3% specificity) at the cellular level for the test set, and a correct classification rate of 75% (77.8% sensitivity, 71.4% specificity) at the tissue level with a negative predictive value of 76% and a positive predictive value of 74% for predicting progression among 71 OPLs, performed on par with the manual method in our previous study. We conclude that the newly developed aNPS algorithm serves as a crucial asset in the implementation of high-resolution image analysis in routine clinical pathology practice to identify lesions that require molecular evaluation or more frequent follow-up.