John Q Nguyen1, Zain S Gowani2, Maggie O'Connor1, Isaac J Pence1, The-Quyen Nguyen3, Ginger E Holt4, Herbert S Schwartz4, Jennifer L Halpern4, Anita Mahadevan-Jansen5. 1. Biophotonics Center, Vanderbilt University, 410 24th Ave. South (Keck FEL Center), Nashville, Tennessee 37232. 2. School of Medicine, Vanderbilt University, 2215 Garland Ave (Light Hall), Nashville, Tennessee 37232. 3. Department of Biomedical Engineering, Northwestern University, Silverman Hall, Evanston, Illinois 60208. 4. Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 691 Preston Building, Nashville, Tennessee 37232-6838. 5. Biophotonics Center, Vanderbilt University, 410 24th Ave. South (Keck FEL Center), Nashville, Tennessee 37232. anita.mahadevan-jansen@vanderbilt.edu.
Abstract
BACKGROUND AND OBJECTIVE: Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated through surgical resection. Current intraoperative margin assessment methods are limited and highlight the need for an improved approach with respect to time and specificity. Here we investigate the potential of near-infrared Raman spectroscopy for the intraoperative differentiation of STS from surrounding normal tissue. MATERIALS AND METHODS: In vivo Raman measurements at 785 nm excitation were intraoperatively acquired from subjects undergoing STS resection using a probe based spectroscopy system. A multivariate classification algorithm was developed in order to automatically identify spectral features that can be used to differentiate STS from the surrounding normal muscle and fat. The classification algorithm was subsequently tested using leave-one-subject-out cross-validation. RESULTS: With the exclusion of well-differentiated liposarcomas, the algorithm was able to classify STS from the surrounding normal muscle and fat with a sensitivity and specificity of 89.5% and 96.4%, respectively. CONCLUSION: These results suggest that single point near-infrared Raman spectroscopy could be utilized as a rapid and non-destructive surgical guidance tool for identifying abnormal tissue margins in need of further excision. Lasers Surg. Med. 48:774-781, 2016.
BACKGROUND AND OBJECTIVE: Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated through surgical resection. Current intraoperative margin assessment methods are limited and highlight the need for an improved approach with respect to time and specificity. Here we investigate the potential of near-infrared Raman spectroscopy for the intraoperative differentiation of STS from surrounding normal tissue. MATERIALS AND METHODS: In vivo Raman measurements at 785 nm excitation were intraoperatively acquired from subjects undergoing STS resection using a probe based spectroscopy system. A multivariate classification algorithm was developed in order to automatically identify spectral features that can be used to differentiate STS from the surrounding normal muscle and fat. The classification algorithm was subsequently tested using leave-one-subject-out cross-validation. RESULTS: With the exclusion of well-differentiated liposarcomas, the algorithm was able to classify STS from the surrounding normal muscle and fat with a sensitivity and specificity of 89.5% and 96.4%, respectively. CONCLUSION: These results suggest that single point near-infrared Raman spectroscopy could be utilized as a rapid and non-destructive surgical guidance tool for identifying abnormal tissue margins in need of further excision. Lasers Surg. Med. 48:774-781, 2016.
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