دورية أكاديمية
Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer.
| العنوان: | Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer. |
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| المؤلفون: | Yang J; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China., Xu P; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China., Wu S; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China., Chen Z; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China., Fang S; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China., Xiao H; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China., Hu F; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China., Jiang L; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China., Wang L; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China., Mo B; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China., Ding F; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China. Electronic address: dingfangbao@xinhuamed.com.cn., Lin LL; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China. Electronic address: linli92@sjtu.edu.cn., Ye J; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China. Electronic address: yejian78@sjtu.edu.cn. |
| المصدر: | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2024 Sep 05; Vol. 317, pp. 124461. Date of Electronic Publication: 2024 May 12. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994- |
| مواضيع طبية MeSH: | Spectrum Analysis, Raman*/methods , Esophageal Neoplasms*/diagnosis , Esophageal Neoplasms*/pathology , Machine Learning* , Support Vector Machine*, Humans ; Discriminant Analysis ; Principal Component Analysis ; Algorithms |
| مستخلص: | Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Focal plane shift; Intraoperative; Raman diagnose; Raman visualization; Tumor boundary; Tumor delineation |
| تواريخ الأحداث: | Date Created: 20240517 Date Completed: 20240529 Latest Revision: 20240529 |
| رمز التحديث: | 20240530 |
| DOI: | 10.1016/j.saa.2024.124461 |
| PMID: | 38759393 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1873-3557 |
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| DOI: | 10.1016/j.saa.2024.124461 |