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.

Introduction: Computer navigation and customized 3D-printed jigs improve accuracy during bone tumor resection, but such technologies can be bulky, costly, and require intraoperative radiation, or long lead time to be ready in OR. Methods: We developed a method utilizing a compact, inexpensive, non-X-ray based 3D surface light scanner to provide a visual aid that helps surgeons accurately draw osteotomy lines on the surface of exposed bone to reproduce a well-defined preoperative bone resection plan. We tested the accuracy of the method on 18 sawbones using a distal femur hemimetaphyseal resection model and compared it with a traditional, freehand method. Results: The method significantly reduces the positional error from 2.53 (± 1.13) mm to 1.04 (± 0.43) mm (p< 0.001), and angular error of the front angle from 2.10° (± 0.83°) to 0.80° (± 0.66°) (p=0.001). The method also reduces the mean maximum deviation of the bone resection, with respect to the preoperative path, from 3.75mm to 2.69mm (p=0.003). However, no increased accuracy was observed at the back side of the bone surface where this method would not be expected to provide information. Discussion: In summary, we developed a novel 3D-LAD navigation technology. From the experimental study, we demonstrated that the method can improve the ability of surgeons to accurately draw the preoperative osteotomy lines and perform resection of a primary bone sarcoma, with comparison to traditional methods, using 18 sawbones.