Abstract
Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma surgery. Frozen section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin status. However, it takes time to complete histologic examination of these samples, and the technique does not provide real-time diagnosis in the operating room (OR), which delays completion of the operation. This paper presents a study and development of sensing technology using Raman spectroscopy that could be used for detection and classification of the tumor after resection with negative sarcoma margins in real time. We acquired Raman spectra from samples of sarcoma and surrounding benign muscle, fat, and dermis during surgery and developed (i) a quantitative method (QM) and (ii) a machine learning method (MLM) to assess the spectral patterns and determine if they could accurately identify these tissue types when compared to findings in adjacent H&E-stained frozen sections. High classification accuracy (>85%) was achieved with both methods, indicating that these four types of tissue can be identified using the analytical methodology. A hand-held Raman probe could be employed to further develop the methodology to obtain spectra in the OR to provide real-time in vivo capability for the assessment of sarcoma resection margin status.