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

作     者：Liming Li Vamiq M.Mustahsan Guangyu He Felix B.Tavernier Gurtej Singh Brendan F.Boyce Fazel Khan Imin Kao Liming Li;Vamiq M.Mustahsan;Guangyu He;Felix B.Tavernier;Gurtej Singh;Brendan F.Boyce;Fazel Khan;Imin Kao

作者机构：Department of Mechanical EngineeringStony Brook UniversityNYUSA Department of Pathology and Laboratory MedicineStony Brook University HospitalStony BrookNYUSA Division of Plastic SurgeryDepartment of SurgeryStony Brook University HospitalStony BrookNYUSA Department of Orthopaedic SurgeryStony Brook University HospitalStony BrookNYUSA 

出 版 物：《Cyborg and Bionic Systems》 (类生命系统（英文）)

年 卷 期：2021年第2021卷第1期

页      面：90-101页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 100214[医学-肿瘤学] 10[医学] 

基　　金：NAno-Raman Molecular Imaging Laboratory NSF-MRI, (OCE-1336724) 

主　　题：surgery diagnosis operative 

摘      要：Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma *** section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin ***,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 *** 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 *** 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 *** 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.