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Yang J, Xu P, Wu S, Chen Z, Fang S, Xiao H, Hu F, Jiang L, Wang L, Mo B, Ding F, Lin LL, Ye J. Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124461. [PMID: 38759393 DOI: 10.1016/j.saa.2024.124461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/02/2024] [Accepted: 05/11/2024] [Indexed: 05/19/2024]
Abstract
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.
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Affiliation(s)
- Junqing Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Pei Xu
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Siyi Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Zhou Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shiyan Fang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Haibo Xiao
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Fengqing Hu
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Lianyong Jiang
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Lei Wang
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Bin Mo
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Fangbao Ding
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China.
| | - Linley Li Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Jian Ye
- 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.
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Elsheikh S, Coles NP, Achadu OJ, Filippou PS, Khundakar AA. Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects. BIOSENSORS 2024; 14:33. [PMID: 38248410 PMCID: PMC10813143 DOI: 10.3390/bios14010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.
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Affiliation(s)
- Suzan Elsheikh
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
| | - Nathan P. Coles
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
| | - Ojodomo J. Achadu
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
| | - Panagiota S. Filippou
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
| | - Ahmad A. Khundakar
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Jiang Z, Wang X, Chu K, Smith ZJ. Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution. Analyst 2023; 148:4710-4720. [PMID: 37622207 DOI: 10.1039/d3an00997a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is "backwards-compatible" with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.
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Affiliation(s)
- Ziling Jiang
- University of Science and Technology of China, Department of Precision Machinery & Precision Instrumentation, Hefei, Anhui, China 230027.
| | - Xianli Wang
- University of Science and Technology of China, Department of Precision Machinery & Precision Instrumentation, Hefei, Anhui, China 230027.
| | - Kaiqin Chu
- University of Science and Technology of China, Suzhou Institute for Advanced Research, Suzhou, Jiangsu, China 215123
| | - Zachary J Smith
- University of Science and Technology of China, Department of Precision Machinery & Precision Instrumentation, Hefei, Anhui, China 230027.
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