Predictive Value of Opto-magnetic Imaging Spectroscopy in Discriminating Oral Squamous Cell Carcinoma from Non-tumor Tissue in Surgical Margins
| Autor: | Djuro Koruga, Djurica Grga, Drago Jelovac, Zvezdana Tepavčević, Bogdan Lisul, Milan Petrovic |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
0206 medical engineering Predictive value Biomedical Engineering 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine In vivo Magnetic imaging medicine Device Basal cell I10 business.industry Oral cancer General Medicine 020601 biomedical engineering Tumor tissue Imaging spectroscopy Histopathology Radiology business Ex vivo Surgical Margins Status |
| Zdroj: | Journal of Medical & Biological Engineering |
| ISSN: | 2199-4757 1609-0985 |
| DOI: | 10.1007/s40846-019-00473-y |
| Popis: | PurposeNegative surgical margins status is of extreme importance for better prognosis and lower recurrence rate in patients undergoing surgical treatment for oral squamous cell carcinoma (OSCC). Standard histopathology is time consuming and current optical procedures come with various limitations. We examined the potential of a new imaging technique-opto-magnetic imaging spectroscopy (OMIS) in discriminating tumor (OSCC) from adjacent non-tumor oral cavity tissue.MethodsA total of 46 samples from 21 patients were included in this study. Samples were taken from a fresh surgical specimen of primary OSCC. Digital imaging of samples was performed within 60 min of resection using OMIS device. Images were processed through a specifically designed convolution algorithm based on light-matter interaction. As a result a convolution spectrum was generated. Predictive value of spectral data was assessed using correlation tests and Naive Bayes classification model.ResultsThere was no significant correlation in distribution of characteristic peaks between tumor and non-tumor tissue (P>0.05). Tumor tissue showed more magnetic activity compared to non-tumor tissue. Naive Bayes classifier with kernel density estimation discriminated tumor from non-tumor tissue with the accuracy of 82.61%, 86.96% sensitivity, 78.26% specificity and the AUC value of 0.917.ConclusionOMIS seems to be a promising optical method for ex vivo characterization of OSCC and non-tumor tissue. Further investigation is necessary to determine how tissue type and level of pathological transformation impact OMIS results. Ultimately, this could aid surgeons in using this method as an in vivo indicator for surgical resection with safe margins. |
| Databáze: | OpenAIRE |
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