The color of cancer: Margin guidance for oral cancer resection using elastic scattering spectroscopy

Abstract

Objectives/Hypothesis To evaluate the usefulness of Elastic Scattering Spectroscopy (ESS) as a diagnostic adjunct to frozen section analysis in patients with diagnosed squamous cell carcinoma of the oral cavity. Study Type /Design Prospective, analytic study Methods Subjects for this single institution, IRB-approved study were recruited from among patients undergoing surgical resection for squamous cell cancer of the oral cavity. A portable, Elastic Scattering Spectroscopy (ESS) device with a contact fiberoptic probe was used to obtain spectral signals. Four to ten spectral readings were obtained on each subject from various sites including gross tumor and normal appearing mucosa in the surgical margin. Each reading was correlated with the histopathologic findings of biopsies taken from the exact location of the spectral readings. A diagnostic algorithm based on multidimensional pattern-recognition/machine-learning was developed. Sensitivity and specificity, error rate, and area under the curve (AUC) were used as performance metrics for tests involving classification between disease and non-disease classes. Results Thirty-four (34) subjects were enrolled in the study. One hundred seventy six (176) spectral data-point/biopsy-specimen pairs were available for analysis. ESS distinguished normal from abnormal tissue with a sensitivity ranging from 84% to 100% and specificity ranging from 71% to 89%., depending on how the cutoff between normal and abnormal tissue was defined (i.e., mild, moderate or severe dysplasia). There were statistically significant differences in malignancy scores between histologically normal tissue and invasive cancer and between non-inflamed tissue and inflamed tissue. Conclusions This is the first study to evaluate the effectiveness of ESS in guiding mucosal resection margins in oral cavity cancer. ESS provides fast, real-time assessment of tissue without the need for pathology expertise. ESS appears to be effective in distinguishing between normal mucosa and invasive cancer and between “normal” tissue (histologically normal and mild dysplasia) and “abnormal” tissue (severe dysplasia and carcinoma-in-situ) that might require further margin resection. Further studies, however, are needed with a larger sample size to validate these findings and to determine the effectiveness of ESS in distinguishing visibly and histologically normal tissue from visibly normal but histologically abnormal tissue.

Surgical margins in oral cavity squamous cell carcinoma: Current practices and future directions

“…Unlike other spectroscopic techniques, ESS did not seem to be influenced by tissue inflammation. A noted limitation was the small point measurement field (2 mm), which limits assessment of the entire mucosal margin simultaneously …”

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

“…There are several competing technologies in the field of head and neck imaging diagnostics. The most studied modalities include fluorescence and fluorescence lifetime imaging, [28] high-resolution microendoscopy, [26] elastic scattering spectroscopy, [29] Raman spectroscopy [30] and OCT. [4] There are many advantages and disadvantages to each of these technologies, some of which are due to the limitations in light tissue interactions. For instance, Raman spectroscopy, which relies on the inelastic scattering of monochromatic light that probes molecular bond vibrations, creates a biochemical "fingerprint" of the target tissue.…”

“…There are several competing technologies in the field of head and neck imaging diagnostics. The most studied modalities include fluorescence and fluorescence lifetime imaging, high‐resolution microendoscopy, elastic scattering spectroscopy, Raman spectroscopy and OCT . There are many advantages and disadvantages to each of these technologies, some of which are due to the limitations in light tissue interactions.…”

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

“…The tumor size and site are connected with prognosis ( Chakrabarti et al, 2010 ; Namin et al, 2020 ), for example, for patient survival rate and surgical decisions for tumor resection ( Upile et al, 2007 ). Much research has focused on the automated structure segmentation of oral cancer-related images ( Brouwer de Koning et al, 2018 ; Fei et al, 2017 ; Grillone et al, 2017 ; Marsden et al, 2020 ; van Rooij et al, 2019 ). In a study by Fei et al ( Fei et al, 2017 ), hyperspectral images of surgical cancerous tissue samples have been acquired to train and test the ML model.…”