Advances toward fully automated in vivo assessment of oral epithelial dysplasia by nuclear endomicroscopy-A pilot study

Background: Uncertainties in detection of oral epithelial dysplasia (OED) frequently result from sampling error especially in inflammatory oral lesions. Endomicroscopy allows non-invasive, "en face" imaging of upper oral epithelium, but parameters of OED are unknown.

Methods: Mucosal nuclei were imaged in 34 toluidine blue-stained oral lesions with a commercial endomicroscopy. Histopathological diagnosis showed four biopsies in "dys-/neoplastic," 23 in "inflammatory," and seven in "others" disease groups. Strength of different assessment strategies of nuclear scoring, nuclear count, and automated nuclear analysis were measured by area under ROC curve (AUC) to identify histopathological "dys-/neoplastic" group. Nuclear objects from automated image analysis were visually corrected.

Results: Best-performing parameters of nuclear-to-image ratios were the count of large nuclei (AUC=0.986) and 6-nearest neighborhood relation (AUC=0.896), and best parameters of nuclear polymorphism were the count of atypical nuclei (AUC=0.996) and compactness of nuclei (AUC=0.922). Excluding low-grade OED, nuclear scoring and count reached 100% sensitivity and 98% specificity for detection of dys-/neoplastic lesions. In automated analysis, combination of parameters enhanced diagnostic strength. Sensitivity of 100% and specificity of 87% were seen for distances of 6-nearest neighbors and aspect ratios even in uncorrected objects. Correction improved measures of nuclear polymorphism only. The hue of background color was stronger than nuclear density (AUC=0.779 vs 0.687) to detect dys-/neoplastic group indicating that macroscopic aspect is biased.

Conclusions: Nuclear-to-image ratios are applicable for automated optical in vivo diagnostics for oral potentially malignant disorders. Nuclear endomicroscopy may promote non-invasive, early detection of dys-/neoplastic lesions by reducing sampling error.