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Application of Targeted Optical Coherence Tomography in Oral Cancer: A Cross-Sectional Preliminary Study
Vera Panzarella1,2, Fortunato Buttacavoli1,3, Vito Rodolico4
1Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, Italy.
Background/Objectives:
The diagnosis of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) represent a significant challenge in oral medicine. Optical coherence tomography (OCT) shows promise for evaluating oral tissue microstructure but lacks standardized diagnostic protocols tailored to the structural variability and lesions of oral mucosa.
Methods:
This cross-sectional observational study aims to evaluate the diagnostic accuracy of targeted biopsy-based and site-coded OCT protocols for common OPMDs and OSCC. Adult patients clinically diagnosed with OPMDs, including oral leukoplakia (OL), oral lichen planus (OLP), and OSCC were enrolled. Clinical and OCT evaluation before and after punch scalpel-site registration preceding diagnostic biopsy on the target site was performed. Blinded observers analyzed the OCT scans for OCT-based diagnoses. Sensitivity, specificity, and diagnostic accuracy for OCT evaluations before and after punch scalpel-site registration were statistically compared with histological findings.
Results:
A dataset of 2520 OCT scans and 210 selected images from 21 patients was obtained. Sensitivity and specificity post-target site registration were high for OSCC (98.57%, 100.00%), OL (98.57%, 98.57%), and OLP (97.14%, 98.57%). The positive predictive values ranged from 97.14% to 100.00%, while negative predictive values ranged from 98.57% to 99.29%. Inter-observer agreements were strong for OSCC (0.84) and moderate for OL (0.54) and OLP (0.47-0.49). Targeted OCT scans significantly improved diagnostic accuracy for all conditions (p < 0.001).
Conclusions:
This preliminary study supports using site-targeted OCT scans followed by a site-targeted punch biopsy, enhancing precision in oral diagnostics. This approach is foundational for developing pioneering automated algorithms guiding oral cancer and pre-cancer diagnosis via OCT imaging.
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