Classification of Soft Tissue Sarcoma Specimens with Raman Spectroscopy as Smart Sensing Technology

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.

Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery

“…Li et al also classified STS specimens with Raman spectroscopy using both a quantitative method and a machine learning method, which resulted in a classification accuracy of 85%. 24 Larson et al used mid-infrared spectroscopy to show differences in absorption between sarcoma tissue and healthy tissue for laser ablation of sarcoma, but no classification was performed. 25 Finally, Gong et al implemented intraoperative margin assessment based on ICG in patients with STSs.…”

“…Li et al. also classified STS specimens with Raman spectroscopy using both a quantitative method and a machine learning method, which resulted in a classification accuracy of 85% 24 . Larson et al.…”

“…Optical techniques have emerged as a potentially valuable tool for tissue sensing purposes in the medical field. For STS, a couple of optical techniques have been studied in recent years, including near-infrared autofluorescence, 22 Raman spectroscopy, 23 , 24 mid-infrared spectroscopy, 25 and indocyanine green fluorescence imaging (ICG). 26 While some of these methods have demonstrated promising outcomes, not every approach is readily available, has a sufficient measurement volume, is offered as an automatic tumor detection model, or achieved an adequate level of accuracy for clinical application.…”

Gravitational Surface Vortex Formation and Suppression Control: A Review from Hydrodynamic Characteristics

“…Limited local characteristics can only be obtained under ideal assumptions or in combination with experimental studies [15][16][17][18][19][20][21][22]. The interfacial morphology and flow pattern are nonlinear due to the surface vortex's characteristics, such as its three-dimensional instability, turbulence, and spatio-temporal multi-scale coupling [23][24][25][26][27][28][29][30][31][32][33][34]. It makes it difficult to accurately characterize physical variables like the vortex scales, vorticity features and streamlining evolving [35][36][37][38][39][40][41][42][43][44][45].…”