1
|
Chang S, Krzyzanowska H, Bowden AK. Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2024; 17:289-311. [PMID: 38424030 DOI: 10.1146/annurev-anchem-061622-014208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy.
Collapse
Affiliation(s)
- Shuang Chang
- 1Vanderbilt Biophotonics Center, Vanderbilt University, Nashville, Tennessee, USA;
- 2Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Halina Krzyzanowska
- 1Vanderbilt Biophotonics Center, Vanderbilt University, Nashville, Tennessee, USA;
- 2Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Audrey K Bowden
- 1Vanderbilt Biophotonics Center, Vanderbilt University, Nashville, Tennessee, USA;
- 2Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- 3Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| |
Collapse
|
2
|
Liu C, Wang J, Shen J, Chen X, Ji N, Yue S. Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy. PNAS NEXUS 2024; 3:pgae208. [PMID: 38860145 PMCID: PMC11164103 DOI: 10.1093/pnasnexus/pgae208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/17/2024] [Indexed: 06/12/2024]
Abstract
Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.
Collapse
Affiliation(s)
- Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Jiejun Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| |
Collapse
|
3
|
Chen X, Shen J, Liu C, Shi X, Feng W, Sun H, Zhang W, Zhang S, Jiao Y, Chen J, Hao K, Gao Q, Li Y, Hong W, Wang P, Feng L, Yue S. Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification. Anal Chem 2024; 96:6158-6169. [PMID: 38602477 DOI: 10.1021/acs.analchem.3c04930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.
Collapse
Affiliation(s)
- Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Beijing 100191, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiaoyu Shi
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Weichen Feng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Hongyi Sun
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Weifeng Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Shengpai Zhang
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Yuqing Jiao
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Jing Chen
- Su Zhou Surgi-Master High Tech Co., Ltd., Zhangjiagang, Suzhou 215626, China
| | - Kun Hao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Qi Gao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Yitong Li
- Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100021, China
| | - Weili Hong
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Pu Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Limin Feng
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| |
Collapse
|
4
|
Chen W, Chen Y, Wu C, Zhang X, Huang X. The accuracy of Fiber-Optic Raman Spectroscopy in the detection and diagnosis of head and neck neoplasm in vivo: a systematic review and meta-analysis. PeerJ 2023; 11:e16536. [PMID: 38099303 PMCID: PMC10720414 DOI: 10.7717/peerj.16536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/07/2023] [Indexed: 12/17/2023] Open
Abstract
Purpose The aim of this article was to review and collectively assess the published studies of fiber-optic Raman spectroscopy (RS) of the in vivo detection and diagnosis of head and neck carcinomas, and to derive a consensus average of the accuracy, sensitivity and specificity. Methods The authors searched four databases, including Ovid-Medline, Ovid-Embase, Cochrane Library, and the China National Knowledge Infrastructure (CNKI), up to February 2023 for all published studies that assessed the diagnostic accuracy of fiber-optic RS in the in vivo detection of head and neck carcinomas. Nonqualifying studies were screened out in accordance with the specified exclusion criteria, and relevant information about the diagnostic performance of fiber-optic RS was excluded. Publication bias was estimated by Deeks' funnel plot asymmetry test. A random effects model was adopted to calculate the pooled sensitivity, specificity and diagnostic odds ratio (DOR). Additionally, the authors conducted a summary receiver operating characteristic (SROC) curve analysis and threshold analysis, reporting the area under the curve (AUC) to evaluate the overall performance of fiber-optic RS in vivo. Results Ten studies (including 16 groups of data) were included in this article, and a total of 5365 in vivo Raman spectra (cancer = 1,746; normal = 3,619) were acquired from 877 patients. The pooled sensitivity and specificity of fiber-optic RS of head and neck carcinomas were 0.88 and 0.94, respectively. SROC curves were generated to estimate the overall diagnostic accuracy, and the AUC was 0.96 (95% CI [0.94-0.97]). No significant publication bias was found in this meta-analysis by Deeks' funnel plot asymmetry test. The heterogeneity of these studies was significant; the Q test values of the sensitivity and specificity were 106.23 (P = 0.00) and 64.21 (P = 0.00), respectively, and the I2 index of the sensitivity and specificity were 85.88 (95% CI [79.99-91.77]) and 76.64 (95% CI [65.45-87.83]), respectively. Conclusion Fiber-optic RS was demonstrated to be a reliable technique for the in vivo detection of head and neck carcinoma with high accuracy. However, considering the high heterogeneity of these studies, more clinical studies are needed to reduce the heterogeneity, and further confirm the utility of fiber-optic Raman spectroscopy in vivo.
Collapse
Affiliation(s)
- Wen Chen
- Department of Stomatology and Immunology Research Center for Oral and Systemic Health, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yafei Chen
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chenzhou Wu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xidong Zhang
- Department of Pharmacy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaofeng Huang
- Department of Stomatology and Immunology Research Center for Oral and Systemic Health, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
5
|
Vaddi A, Tadinada A, Lurie A, Deymier A. Evaluation of near-infrared Raman spectroscopy in the differentiation of cortical bone, trabecular bone, and Bio-Oss bone graft: an ex-vivo study. Oral Surg Oral Med Oral Pathol Oral Radiol 2023; 136:632-639. [PMID: 37394288 DOI: 10.1016/j.oooo.2023.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 07/04/2023]
Abstract
OBJECTIVE We evaluated the ability of near-infrared Raman spectroscopy (near-IR RS) to differentiate among cortical bone, trabecular bone, and Bio-Oss, a bovinebone-based graft material. STUDY DESIGN We obtained a thinly sliced section of the mandible to collect cortical and trabecular bone samples and placed compacted Bio-Oss bone graft into a partially edentulous mandible in a dry human skull to obtain a comparable Bio-Oss sample. We performed near-IR RS of the 3 samples and evaluated the resultant Raman spectra to evaluate their differences. RESULTS We identified 3 sets of spectroscopic markers that differentiated Bio-Oss from human bone. The first consisted of significant shifts in the location of the 960 cm-1 phosphate (PO43-) peak and a reduction in its width, suggesting that Bio-Oss is more crystalline than bone. The second was the reduced carbonate content of Bio-Oss compared to bone, as determined from the 1070 cm-1/960 cm-1 peak area ratio. The final marker was the lack of collagen-associated peaks in Bio-Oss compared to cortical and trabecular bone. CONCLUSIONS Near-IR RS can reliably differentiate human cortical and trabecular bone from Bio-Oss via 3 sets of spectral markers associated with mineral crystallinity, carbonate content, and collagen content that differ significantly between them. Integrating this modality into dental practice may assist in implant treatment planning.
Collapse
Affiliation(s)
- Anusha Vaddi
- Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA.
| | - Aditya Tadinada
- Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA
| | - Alan Lurie
- Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA
| | - Alix Deymier
- Department of Biomedical Engineering, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA
| |
Collapse
|
6
|
Lin J, Lin D, Qiu S, Huang Z, Liu F, Huang W, Xu Y, Zhang X, Feng S. Shifted-excitation Raman difference spectroscopy for improving in vivo detection of nasopharyngeal carcinoma. Talanta 2023; 257:124330. [PMID: 36773510 DOI: 10.1016/j.talanta.2023.124330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
A strong fluorescence background is one of the common interference factors of Raman spectroscopic analysis in biological tissue. This study developed an endoscopic shifted-excitation Raman difference spectroscopy (SERDS) system for real-time in vivo detection of nasopharyngeal carcinoma (NPC) for the first time. Owing to the use of the SERDS method, the high-quality Raman signals of nasopharyngeal tissue could be well extracted and characterized from the complex raw spectra by removing the fluorescence interference signals. Significant spectral differences relating to proteins, phospholipids, glucose, and DNA were found between 42 NPC and 42 normal tissue sites. Using linear discriminant analysis, the diagnostic accuracy of SERDS for NPC detection was 100%, which was much higher than that of raw Raman spectroscopy (75.0%), showing the great potential of SERDS for improving the accurate in vivo detection of NPC.
Collapse
Affiliation(s)
- Jinyong Lin
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China; Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Duo Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Sufang Qiu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Zufang Huang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
| | - Feng Liu
- Simple & Smart Instrument (Beijing) Co.,Ltd, China
| | - Wei Huang
- Department of Forensic Science, Fujian Police College, Fuzhou, 350007, PR China
| | - Yuanji Xu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Xianzeng Zhang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
| | - Shangyuan Feng
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
| |
Collapse
|
7
|
Shu C, Zheng W, Lin K, Lim CM, Huang Z. Real-time in vivo cancer staging of nasopharyngeal carcinoma patients with rapid fiberoptic Raman endoscopy. Talanta 2023; 259:124561. [PMID: 37080076 DOI: 10.1016/j.talanta.2023.124561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/08/2023] [Accepted: 04/12/2023] [Indexed: 04/22/2023]
Abstract
Cancer staging is important to guide treatment and for prognostication. This work aims to demonstrate the ability of rapid fiberoptic Raman endoscopy for real-time in vivo cancer staging of nasopharyngeal cancer (NPC) patients. We interrogate 278 tissue sites on the primary NPC with different cancer stages from 61 NPC patients and 50 healthy volunteers using rapid fiberoptic Raman endoscopy examination. Distinct Raman spectral differences of NPC at different cancer stages are observed through simultaneous fingerprint and high-wavenumber (FP/HW) Raman spectral measurements, reflecting the biomolecular differences of NPC tumor across various cancer stages. Raman staging model is established based on in vivo FP/HW tissue Raman spectra together with partial-least-squares linear-discriminant-analysis (PLS-LDA) and leave-one-tissue-site-out cross-validation (LOOCV). In vivo FP/HW Raman endoscopy provides an overall diagnostic accuracy of 92.81% for identifying different stages of NPC (i.e., NPC stage I&II and NPC stage III&IV) from normal nasopharynx. Specifically, the diagnostic sensitivity of 91.18% is obtained for identifying NPC stage I& II; and the sensitivity of 93.04% is achieved for classifying NPC stage III&IV from normal tissue. The key tissue biomolecular variations responsible for different NPC stages have been identified using biomolecular Raman modeling developed based on non-negative linear regression. The essential biomolecules (chondroitin sulfate, glucose, hemoglobin, oleic acid and triolein) are uncovered from the Raman spectra of NPC tissues through biomolecular modeling with significant variations (p < 0.05) between early-stage NPC (stage I and stage II) and late-stage NPC patients (stage III and stage IV). Our pivotal work demonstrates for the first time that fiberoptic Raman endoscopy is a robust analytical tool for real-time in vivo NPC staging in clinical settings.
Collapse
Affiliation(s)
- Chi Shu
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117576, Singapore
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117576, Singapore
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117576, Singapore
| | - Chwee Ming Lim
- Department of Otolaryngology, Singapore General Hospital, Duke-NUS Graduate Medical School, Singapore 169608
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117576, Singapore.
| |
Collapse
|
8
|
Zhang Y, Yu H, Li Y, Xu H, Yang L, Shan P, Du Y, Yan X, Chen X. Raman spectroscopy: A prospective intraoperative visualization technique for gliomas. Front Oncol 2023; 12:1086643. [PMID: 36686726 PMCID: PMC9849680 DOI: 10.3389/fonc.2022.1086643] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/05/2022] [Indexed: 01/06/2023] Open
Abstract
The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.
Collapse
|
9
|
Lin Y, Qiu T, Lan Y, Li Z, Wang X, Zhou M, Li Q, Li Y, Liang J, Zhang J. Multi-Modal Optical Imaging and Combined Phototherapy of Nasopharyngeal Carcinoma Based on a Nanoplatform. Int J Nanomedicine 2022; 17:2435-2446. [PMID: 35656166 PMCID: PMC9151321 DOI: 10.2147/ijn.s357493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/11/2022] [Indexed: 11/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a common malignant tumor of the head and neck with a high incidence rate worldwide, especially in southern China. Phototheranostics in combination with nanoparticles is an integrated strategy for enabling simultaneous diagnosis, real-time monitoring, and administration of precision therapy for nasopharyngeal carcinoma (NPC). It has shown great potential in the field of cancer diagnosis and treatment owing to its unique noninvasive advantages. Many Chinese and international research teams have applied nano-targeted drugs to optical diagnosis and treatment technology to conduct multimodal imaging and collaborative treatment of NPC, which has become a hot research topic. In this review, we aimed to introduce the recent developments in phototheranostics of NPC based on a nanoplatform. This study aimed to elaborate on the applications of nanoplatform-based optical imaging strategies and treatment modalities, including fluorescence imaging, photoacoustic imaging, Raman spectroscopy imaging, photodynamic therapy, and photothermal therapy. This study is expected to provide a scientific basis for further research and development of NPC diagnosis and treatment.
Collapse
Affiliation(s)
- Yanping Lin
- Department of Radiology, DongGuan Tungwah Hospital, DongGuan, Guangdong, 523000, People's Republic of China
| | - Ting Qiu
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, Guangdong, 519000, People's Republic of China
| | - Yintao Lan
- Department of Biomedical Engineering, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 511436, People's Republic of China
| | - Zhaoyong Li
- Department of Radiology, DongGuan Tungwah Hospital, DongGuan, Guangdong, 523000, People's Republic of China
| | - Xin Wang
- Department of Oncology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, Guangdong, 511500, People's Republic of China
| | - Mengyu Zhou
- Department of Biomedical Engineering, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 511436, People's Republic of China
| | - Qiuyu Li
- Department of Radiology, DongGuan Tungwah Hospital, DongGuan, Guangdong, 523000, People's Republic of China
| | - Yao Li
- Department of Radiology, DongGuan Tungwah Hospital, DongGuan, Guangdong, 523000, People's Republic of China
| | - Junsheng Liang
- Department of Radiology, DongGuan Tungwah Hospital, DongGuan, Guangdong, 523000, People's Republic of China
| | - Jian Zhang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 511436, People's Republic of China.,Department of Oncology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, Guangdong, 511500, People's Republic of China
| |
Collapse
|
10
|
Liu K, Zhao Q, Li B, Zhao X. Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis. Front Bioeng Biotechnol 2022; 10:856591. [PMID: 35372295 PMCID: PMC8965449 DOI: 10.3389/fbioe.2022.856591] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/23/2022] [Indexed: 12/24/2022] Open
Abstract
Gastric cancer is usually diagnosed at late stage and has a high mortality rate, whereas early detection of gastric cancer could bring a better prognosis. Conventional gastric cancer diagnostic methods suffer from long diagnostic times, severe trauma, and a high rate of misdiagnosis and rely heavily on doctors’ subjective experience. Raman spectroscopy is a label-free molecular vibrational spectroscopy technique that identifies the molecular fingerprint of various samples based on the inelastic scattering of monochromatic light. Because of its advantages of non-destructive, rapid, and accurate detection, Raman spectroscopy has been widely studied for benign and malignant tumor differentiation, tumor subtype classification, and section pathology diagnosis. This paper reviews the applications of Raman spectroscopy for the in vivo and in vitro diagnosis of gastric cancer, methodology related to the spectroscopy data analysis, and presents the limitations of the technique.
Collapse
Affiliation(s)
- Kunxiang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- Cancer Microbiome Platform, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Bei Li
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Bei Li, ; Xia Zhao,
| | - Xia Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- Cancer Microbiome Platform, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Department of Microbiology, Army Medical University, Chongqing, China
- *Correspondence: Bei Li, ; Xia Zhao,
| |
Collapse
|
11
|
Qi Y, Yang L, Liu B, Liu L, Liu Y, Zheng Q, Liu D, Luo J. Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120400. [PMID: 34547683 DOI: 10.1016/j.saa.2021.120400] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.
Collapse
Affiliation(s)
- Yafeng Qi
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bangxu Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Li Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuhong Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| |
Collapse
|
12
|
Cialla-May D, Krafft C, Rösch P, Deckert-Gaudig T, Frosch T, Jahn IJ, Pahlow S, Stiebing C, Meyer-Zedler T, Bocklitz T, Schie I, Deckert V, Popp J. Raman Spectroscopy and Imaging in Bioanalytics. Anal Chem 2021; 94:86-119. [PMID: 34920669 DOI: 10.1021/acs.analchem.1c03235] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Dana Cialla-May
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Christoph Krafft
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Tanja Deckert-Gaudig
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Torsten Frosch
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Izabella J Jahn
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Susanne Pahlow
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Clara Stiebing
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Tobias Meyer-Zedler
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Iwan Schie
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Ernst-Abbe-Hochschule Jena, University of Applied Sciences, Department of Biomedical Engineering and Biotechnology, Carl-Zeiss-Promenade 2, 07745 Jena, Germany
| | - Volker Deckert
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| |
Collapse
|
13
|
Shu C, Zheng W, Wang Z, Yu C, Huang Z. Development and characterization of a disposable submillimeter fiber optic Raman needle probe for enhancing real-time in vivo deep tissue and biofluids Raman measurements. OPTICS LETTERS 2021; 46:5197-5200. [PMID: 34653150 DOI: 10.1364/ol.438713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
We report on the development and characterization of disposable submillimeter fiber optic Raman needle probe for enhancing real-time in vivo tissue and biofluids Raman measurements. The submillimeter Raman probe is designed and fabricated using an aluminum-coated multimode fiber tapered with a semispherical lens, resulting in the coaxial laser excitation/Raman collection configuration for maximizing tissue and biofluid Raman measurements. We demonstrate that, with the use of the Raman needle probe associated with the structured background subtraction algorithms developed, high quality tissue Raman spectra covering both the fingerprint (FP) (800-1800cm-1) and high-wavenumber (HW) (2800-3300cm-1) regions can be acquired within subseconds from different tissue types (e.g., skin, muscle, fat, cartilage, liver, and brain) and biofluids (e.g., blood, urine). By advancing the Raman needle probe into the murine brain tissue model, high quality depth-resolved deep tissue Raman spectra can also be acquired rapidly. This work shows that the submillimeter fiberoptic Raman needle probe is capable of achieving real-time collection of deep tissue and biofluids FP/HW Raman spectra with high signal to noise ratios. This opens a new avenue with dual functioning of Raman optical biopsy and fine needle aspiration biopsy for enhancing in vivo deep tissue and biofluids diagnosis and characterization in different organs.
Collapse
|
14
|
Accurate diagnosis of lung tissues for 2D Raman spectrogram by deep learning based on short-time Fourier transform. Anal Chim Acta 2021; 1179:338821. [PMID: 34535256 DOI: 10.1016/j.aca.2021.338821] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023]
Abstract
Multivariate statistical analysis methods have an important role in spectrochemical analyses to rapidly identify and diagnose cancer and the subtype. However, utilizing these methods to analyze lager amount spectral data is challenging, and poses a major bottleneck toward achieving high accuracy. Here, a new convolutional neural networks (CNN) method based on short-time Fourier transform (STFT) to diagnose lung tissues via Raman spectra readily is proposed. The models yield that the accuracies of the new method are higher than the conventional methods (principal components analysis -linear discriminant analysis and support vector machine) for validation group (95.2% vs 85.5%, 94.4%) and test group (96.5% vs 90.4%, 93.9%) after cross-validation. The results illustrate that the new method which converts one-dimensional Raman data into two-dimensional Raman spectrograms improve the discriminatory ability of lung tissues and can achieve automatically accurate diagnosis of lung tissues.
Collapse
|
15
|
Shu C, Yan H, Zheng W, Lin K, James A, Selvarajan S, Lim CM, Huang Z. Deep Learning-Guided Fiberoptic Raman Spectroscopy Enables Real-Time In Vivo Diagnosis and Assessment of Nasopharyngeal Carcinoma and Post-treatment Efficacy during Endoscopy. Anal Chem 2021; 93:10898-10906. [PMID: 34319713 DOI: 10.1021/acs.analchem.1c01559] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this work, we develop a deep learning-guided fiberoptic Raman diagnostic platform to assess its ability of real-time in vivo nasopharyngeal carcinoma (NPC) diagnosis and post-treatment follow-up of NPC patients. The robust Raman diagnostic platform is established using innovative multi-layer Raman-specified convolutional neural networks (RS-CNN) together with simultaneous fingerprint and high-wavenumber spectra acquired within sub-seconds using a fiberoptic Raman endoscopy system. We have acquired a total of 15,354 FP/HW in vivo Raman spectra (control: 1761; NPC: 4147; and post-treatment (PT): 9446) from 888 tissue sites of 418 subjects (healthy control: 85; NPC: 82; and PT: 251) during endoscopic examination. The optimized RS-CNN model provides an overall diagnostic accuracy of 82.09% (sensitivity of 92.18% and specificity of 73.99%) for identifying NPC from control and post-treatment patients, which is superior to the best diagnosis performance (accuracy of 73.57%; sensitivity of 89.74%; and specificity of 58.10%) using partial-least-squares linear-discriminate-analysis, proving the robustness and high spectral information sensitiveness of the RS-CNN model developed. We further investigate the saliency map of the best RS-CNN models using the correctly predicted Raman spectra. The specific Raman signatures that are related to the cancer-associated biomolecular variations (e.g., collagens, lipids, and nucleic acids) are uncovered in the map, validating the diagnostic capability of RS-CNN models to correlate with biomolecular signatures. Deep learning-based Raman spectroscopy is a powerful diagnostic tool for rapid screening and surveillance of NPC patients and can also be deployed for longitudinal follow-up monitoring of post-treatment NPC patients to detect early cancer recurrences in the head and neck.
Collapse
Affiliation(s)
- Chi Shu
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Hanshu Yan
- Department of Electrical and Computer Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Anne James
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | | | - Chwee Ming Lim
- Department of Otolaryngology, Duke-NUS Graduate Medical School, Singapore General Hospital, Singapore 169608, Singapore
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576, Singapore
| |
Collapse
|
16
|
Xi X, Liang C. Perspective of Future SERS Clinical Application Based on Current Status of Raman Spectroscopy Clinical Trials. Front Chem 2021; 9:665841. [PMID: 34354978 PMCID: PMC8329355 DOI: 10.3389/fchem.2021.665841] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/18/2021] [Indexed: 12/18/2022] Open
Abstract
Raman spectroscopy has emerged as a promising tool in biomedical analysis and clinical diagnosis. The development of surface-enhanced Raman scattering spectroscopy (SERS) improved the detection limit with ultrahigh sensitivity and simplicity. More and more Raman spectroscopy clinical trials (R-PCT) have been conducted recently. However, there is a lack of an up-to-date review summarizing the current status of Raman clinical trials performed until now. Hence, the clinical trials for Raman were retrieved from the International Clinical Trials Registration Platform. We summarized the clinical characteristics of 55 registered Raman spectroscopy clinical trials (R-RSCTs) and 44 published Raman spectroscopy clinical trials (P-RSCTs). This review could assist researchers and clinicians to understand the current status of Raman spectroscopy clinical research and perhaps could benefit the reasonable and accurate design of future SERS studies.
Collapse
Affiliation(s)
- Xi Xi
- Department of Biopharmacy, School of Pharmaceutical Sciences, Jilin University, Changchun, China
| | - Chongyang Liang
- School of pharmaceutical science, Institute of Frontier Medical Science, Jilin University, Changchun, China
| |
Collapse
|
17
|
Ogrinc N, Saudemont P, Takats Z, Salzet M, Fournier I. Cancer Surgery 2.0: Guidance by Real-Time Molecular Technologies. Trends Mol Med 2021; 27:602-615. [PMID: 33965341 DOI: 10.1016/j.molmed.2021.04.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/30/2021] [Accepted: 04/02/2021] [Indexed: 12/14/2022]
Abstract
In vivo cancer margin delineation during surgery remains a major challenge. Despite the availability of several image guidance techniques and intraoperative assessment, clear surgical margins and debulking efficiency remain scarce. For this reason, there is particular interest in developing rapid intraoperative tools with high sensitivity and specificity to help guide cancer surgery in vivo. Recently, several emerging technologies including intraoperative mass spectrometry have paved the way for molecular guidance in a clinical setting. We evaluate these techniques and assess their relevance for intraoperative surgical guidance and how they can transform the future of molecular cancer surgery, diagnostics, patient management and care.
Collapse
Affiliation(s)
- Nina Ogrinc
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Philippe Saudemont
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Zoltan Takats
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Michel Salzet
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
| | - Isabelle Fournier
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
| |
Collapse
|
18
|
Pence IJ, O’Brien CM, Masson LE, Mahadevan-Jansen A. Application driven assessment of probe designs for Raman spectroscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:852-871. [PMID: 33680546 PMCID: PMC7901321 DOI: 10.1364/boe.413436] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/13/2020] [Accepted: 12/29/2020] [Indexed: 05/23/2023]
Abstract
In vivo Raman spectroscopy has been utilized for the non-invasive, non-destructive assessment of tissue pathophysiology for a variety of applications largely through the use of fiber optic probes to interface with samples of interest. Fiber optic probes can be designed to optimize the collection of Raman-scattered photons from application-dependent depths, and this critical consideration should be addressed when planning a study. Herein we investigate four distinct probe geometries for sensitivity to superficial and deep signals through a Monte Carlo model that incorporates Raman scattering and fluorescence. Experimental validation using biological tissues was performed to accurately recapitulate in vivo scenarios. Testing in biological tissues agreed with modeled results and revealed that microlens designs had slightly enhanced performance at shallow depths (< 1 mm), whereas all of the beampath-modified designs yielded more signal from deep within tissue. Simulation based on fluence maps generated using ray-tracing in the absence of optical scattering had drastically different results as a function of depth for each probe compared to the biological simulation. The contrast in simulation results between the non-scattering and biological tissue phantoms underscores the importance of considering the optical properties of a given application when designing a fiber optic probe. The model presented here can be easily extended for optimization of entirely novel probe designs prior to fabrication, reducing time and cost while improving data quality.
Collapse
|
19
|
Shu C, Zheng W, Lin K, Lim C, Huang Z. Label-Free Follow-Up Surveying of Post-Treatment Efficacy and Recurrence in Nasopharyngeal Carcinoma Patients with Fiberoptic Raman Endoscopy. Anal Chem 2021; 93:2053-2061. [PMID: 33406834 DOI: 10.1021/acs.analchem.0c03778] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Recurrent nasopharyngeal carcinoma (NPC) is the main cause of poor prognosis for NPC patients after chemo- and radiotherapies. Subsequent long-term follow-ups of post-treatment patients are crucial for the early discovery of tumor recurrence with timely intervention. Current clinical imaging methods based on tissue morphology encounter difficulties in differentiating recurrent tumors from post-treatment inflammation and fibrosis. In this work, we apply a unique fiberoptic Raman endoscopy technique to address the challenges for label-free follow-up surveying of post-treatment NPC patients and accurate detection of tumor recurrence. Significant Raman spectral differences can be observed among normal, NPC, and nonrecurring post-treatment patients. Raman endoscopy provides diagnostic accuracy of 100% for detecting recurrent NPC from early post-treatment inflammation and diagnostic accuracy of 98.21% for separating recurrent NPC from long-term post-treatment fibrosis. Further quantitative Raman modeling on in vivo nasopharyngeal tissue Raman data acquired unveils the changes of major tissue biochemicals (e.g., triolein, elastin, keratin, fibrillar collagen, and type IV collagen) associated with primary NPC and post-treatment recurrent NPC tissue compared to normal nasopharyngeal tissue. This work demonstrates that fiberoptic Raman endoscopy can be a clinically powerful diagnostic tool for rapid, label-free post-treatment surveying and recurrent tumor detection in NPC patients at the molecular level.
Collapse
Affiliation(s)
- Chi Shu
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
| | - Chweeming Lim
- Department of Otolaryngology, Singapore General Hospital, Duke-NUS Graduate Medical School, Singapore 169608
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
| |
Collapse
|
20
|
Heng HPS, Shu C, Zheng W, Lin K, Huang Z. Advances in real‐time fiber‐optic Raman spectroscopy for early cancer diagnosis: Pushing the frontier into clinical endoscopic applications. TRANSLATIONAL BIOPHOTONICS 2020. [DOI: 10.1002/tbio.202000018] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Howard Peng Sin Heng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
- NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore
| | - Chi Shu
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
- NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore
| |
Collapse
|
21
|
Song D, Chen T, Wang S, Chen S, Li H, Yu F, Zhang J, Zhang Z. Study on the biochemical mechanisms of the micro-wave ablation treatment of lung cancer by ex vivo confocal Raman microspectral imaging. Analyst 2020; 145:626-635. [PMID: 31782420 DOI: 10.1039/c9an01524h] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
As a highly invasive and the most prevalent malignancy, lung cancer remains the leading cause of cancer-associated mortality worldwide, especially in China. Microwave ablation (MWA) is an effective, safe, and the least invasive ablative treatment modality, which has been increasingly used for the management of unrespectable lung tumors. However, the underlying biochemical mechanisms of MWA treatment remain to be incompletely elucidated. Therefore, to illustrate the complex biochemical responses of lung squamous cell carcinoma (LSCC) to MWA treatment, confocal Raman micro-spectral imaging (CRMI) was applied in combination with multivariate analysis. A total of twelve LSCC tissues were acquired from patients undergoing clinical treatment, and their spectral characteristics were analyzed to determine significant spectral variations following cancer progression and MWA treatment in comparison with healthy lung tissues. Point-scanned Raman datasets were acquired from sectioned tissue samples in both pre-therapy (Pre-MWA group) and post-therapy groups (Post-MWA group) and further analyzed using K-means cluster analysis (KCA) and principal component analysis (PCA) to highlight the detailed compositional variations of the biochemical constituents. The spectral variations of essential amino acids (such as phenylalanine and tryptophan), collagen, and nucleic acids in the cancerous tissues of the Post-MWA group were significantly enhanced compared to those in the Pre-MWA group. The acquired information further confirmed a remarkable increase in the content of nucleic acid, protein, and lipid in the cancerous tissue following MWA treatment and, a comparative spectral imaging investigation indicated that MWA had no noticeable adverse effects on the paracancerous tissues. Thus, the findings not only illustrated the underlying biochemical variability in lung cancer during MWA treatment but also further confirmed the feasibility of a combined analytical procedure for assessing the biochemical responses during thermal ablation, which could be applied to prominently enhance the effectiveness of MWA in lung cancer treatment in clinical settings.
Collapse
Affiliation(s)
- Dongliang Song
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710069, China.
| | | | | | | | | | | | | | | |
Collapse
|
22
|
Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System. Gastroenterol Res Pract 2020; 2020:8015024. [PMID: 32508914 PMCID: PMC7245655 DOI: 10.1155/2020/8015024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 04/18/2020] [Indexed: 12/24/2022] Open
Abstract
Aim To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC. Method Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained. Results The nonlesional tissues showed peaks at 891 cm−1, 1103 cm−1, 1417 cm−1, 1206 cm−1, 1234 cm−1, 1479 cm−1, 1560 cm−1, and 1678 cm−1. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm−1 was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P < 0.05). Considering the peak intensity ratio of I1560 cm−1/I1103 cm−1 as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 98.8%, 93.9%, and 91.9%, respectively. Considering the integral energy ratio (noncontinuous frequency band and continuous frequency band) as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 99.2-99.6%, 93.9-97.0%, and 95.5%, respectively. Conclusions The integral energy ratio of the Raman spectrum could be considered an effective indicator for the diagnosis of EGC.
Collapse
|
23
|
Hubbard TJE, Shore A, Stone N. Raman spectroscopy for rapid intra-operative margin analysis of surgically excised tumour specimens. Analyst 2020; 144:6479-6496. [PMID: 31616885 DOI: 10.1039/c9an01163c] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Raman spectroscopy, a form of vibrational spectroscopy, has the ability to provide sensitive and specific biochemical analysis of tissue. This review article provides an in-depth analysis of the suitability of different Raman spectroscopy techniques in providing intra-operative margin analysis in a range of solid tumour pathologies. Surgical excision remains the primary treatment of a number of solid organ cancers. Incomplete excision of a tumour and positive margins on histopathological analysis is associated with a worse prognosis, the need for adjuvant therapies with significant side effects and a resulting financial burden. The provision of intra-operative margin analysis of surgically excised tumour specimens would be beneficial for a number of pathologies, as there are no widely adopted and accurate methods of margin analysis, beyond histopathology. The limitations of Raman spectroscopic studies to date are discussed and future work necessary to enable translation to clinical use is identified. We conclude that, although there remain a number of challenges in translating current techniques into a clinically effective tool, studies so far demonstrate that Raman Spectroscopy has the attributes to successfully perform highly accurate intra-operative margin analysis in a clinically relevant environment.
Collapse
|
24
|
Qiu S, Li M, Liu J, Chen X, Lin T, Xu Y, Chen Y, Weng Y, Pan Y, Feng S, Lin X, Zhang L, Lin D. Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy. BIOMEDICAL OPTICS EXPRESS 2020; 11:1819-1833. [PMID: 32341850 PMCID: PMC7173897 DOI: 10.1364/boe.388785] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/22/2020] [Accepted: 02/28/2020] [Indexed: 05/08/2023]
Abstract
To explore the effect in nasopharyngeal carcinoma (NPC) cells after treatment with chemodrugs, Raman profiles were characterized by laser tweezer Raman spectroscopy. Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment include changes in protein structure (e.g. 1655 cm-1), changes in DNA/RNA content and structure (e.g. 830 cm-1), destruction of DNA/RNA base pairs (e.g. 785 cm-1), and reduction in lipids (e.g. 970 cm-1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.
Collapse
Affiliation(s)
- Sufang Qiu
- Department of Radiation Oncology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
- Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fuzhou 350014, China
- These authors contributed equally to this work
| | - Miaomiao Li
- Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
- These authors contributed equally to this work
| | - Jun Liu
- Cancer Bio-immunotherapy Center, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
- Department of Medical Oncology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
| | - Xiaochuan Chen
- Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
| | - Ting Lin
- Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
| | - Yunchao Xu
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Yang Chen
- Department of Laboratory Medicine, Fujian Medical University, Fuzhou 350004, China
| | - Youliang Weng
- Department of Radiation Oncology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
| | - Yuhui Pan
- Department of Radiation Oncology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou 350014, China
| | - Shangyuan Feng
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Xiandong Lin
- Laboratory of Radiation Oncology and Radiobiology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou 350014, China
| | - Lurong Zhang
- Laboratory of Radiation Oncology and Radiobiology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou 350014, China
| | - Duo Lin
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| |
Collapse
|
25
|
Abramczyk H, Imiela A, Brożek-Płuska B, Kopeć M, Surmacki J, Śliwińska A. Aberrant Protein Phosphorylation in Cancer by Using Raman Biomarkers. Cancers (Basel) 2019; 11:E2017. [PMID: 31847192 PMCID: PMC6966530 DOI: 10.3390/cancers11122017] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 12/03/2019] [Accepted: 12/11/2019] [Indexed: 12/30/2022] Open
Abstract
(1) Background: Novel methods are required for analysing post-translational modifications of protein phosphorylation by visualizing biochemical landscapes of proteins in human normal and cancerous tissues and cells. (2) Methods: A label-free Raman method is presented for detecting spectral changes that arise in proteins due to phosphorylation in the tissue of human breasts, small intestines, and brain tumours, as well as in the normal human astrocytes and primary glioblastoma U-87 MG cell lines. Raman spectroscopy and Raman imaging are effective tools for monitoring and analysing the vibrations of functional groups involved in aberrant phosphorylation in cancer without any phosphorecognition of tag molecules. (3) Results: Our results based on 35 fresh human cancer and normal tissues prove that the aberrant tyrosine phosphorylation monitored by the unique spectral signatures of Raman vibrations is a universal characteristic in the metabolic regulation in different types of cancers. Overexpressed tyrosine phosphorylation in the human breast, small intestine and brain tissues and in the human primary glioblastoma U-87 MG cell line was monitored by using Raman biomarkers. (4) We showed that the bands at 1586 cm-1 and 829 cm-1, corresponding to phosphorylated tyrosine, play a pivotal role as a Raman biomarker of the phosphorylation status in aggressive cancers. We found that the best Raman biomarker of phosphorylation is the 1586/829 ratio showing the statistical significance at p Values of ≤ 0.05. (5) Conclusions: Raman spectroscopy and imaging have the potential to be used as screening functional assays to detect phosphorylated target proteins and will help researchers to understand the role of phosphorylation in cellular processes and cancer progression. The abnormal and excessive high level of tyrosine phosphorylation in cancer samples compared with normal samples was found in the cancerous human tissue of breasts, small intestines and brain tumours, as well as in the mitochondria and lipid droplets of the glioblastoma U-87 MG cell line. Detailed insights are presented into the intracellular oncogenic metabolic pathways mediated by phosphorylated tyrosine.
Collapse
Affiliation(s)
- Halina Abramczyk
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Anna Imiela
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Beata Brożek-Płuska
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Monika Kopeć
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Jakub Surmacki
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Agnieszka Śliwińska
- Faculty of Medicine, Medical University of Lodz, Chair of Department of Nucleic Acids Biochemistry, Pomorska 251, 92-213 Lodz, Poland;
| |
Collapse
|
26
|
Krafft C, Popp J. Medical needs for translational biophotonics with the focus on Raman‐based methods. TRANSLATIONAL BIOPHOTONICS 2019. [DOI: 10.1002/tbio.201900018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena Germany
- Institute of Physical Chemistry and Abbe Center of PhotonicsFriedrich Schiller University Jena Jena Germany
| |
Collapse
|
27
|
Duan P, Li J, Yang W, Li X, Long M, Feng X, Zhang Y, Chen C, Morais CLM, Martin FL, Luo J, Liu D, Xiong C. Fourier transform infrared and Raman-based biochemical profiling of different grades of pure foetal-type hepatoblastoma. JOURNAL OF BIOPHOTONICS 2019; 12:e201800304. [PMID: 30993892 DOI: 10.1002/jbio.201800304] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/05/2019] [Accepted: 04/07/2019] [Indexed: 06/09/2023]
Abstract
The biomolecular events resulting from the progression of hepatoblastoma remain to be elucidated. Fourier-transform infrared (FTIR) and Raman spectroscopies are capable of noninvasively and accurately capturing the biochemical properties of biological tissue from its pathological status. Our aim was to probe critial biomolecular changes of liver accompanying the progression of pure foetal hepatoblastoma (PFH) by FTIR and Raman spectroscopies. Herein, biochemical alterations were both evident in the FTIR spectra (regions of 3100-2800 cm-1 and 1800-900 cm-1 ) and the Raman spectra (region of 1800-400 cm-1 ) among normal, borderline and malignant liver tissues. Compared with normal tissues, the ratios of protein-to-lipid, α-helix-to-β-sheet, RNA-to-DNA, CH3 methyl-to-CH2 methylene, glucose-to-phospholipids, and unsaturated-to-saturated lipids intensities were significantly higher in malignant tissues, while the ratios of RNA-to-Amide II, DNA-to-Amide II, glycogen-to-cholesterol and Amide I-to-Amide II intensities were remarkably lower. These biochemical alterations in the transition from normal to malignant have profound implications not only for cyto-pathological classification but also for molecular understanding of PFH progression. The successive changes of the spectral characteristics have been shown to be consistent with the development of PFH, indicating that FTIR and Raman spectroscopies are excellent tools to interrogate the biochemical features of different grades of PFH.
Collapse
Affiliation(s)
- Peng Duan
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Reproductive Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyi Li
- State Key Laboratory of Tribology, Tsinghua University, Beijing, China
| | - Weiyingxue Yang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiandong Li
- Department of Clinical Laboratory, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Manman Long
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Feng
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuge Zhang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunling Chen
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Camilo L M Morais
- Lancashire Teaching Hospitals NHS Trust, Preston, UK
- Biocel Ltd, Hull, UK
| | | | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing, China
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing, China
| | - Chengliang Xiong
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Reproductive Medicine, Wuhan Tongji Reproductive Medicine Hospital, Wuhan, China
| |
Collapse
|
28
|
Ralbovsky NM, Lednev IK. Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 219:463-487. [PMID: 31075613 DOI: 10.1016/j.saa.2019.04.067] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/20/2019] [Accepted: 04/24/2019] [Indexed: 05/14/2023]
Abstract
Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.
Collapse
Affiliation(s)
- Nicole M Ralbovsky
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA.
| |
Collapse
|
29
|
Žuvela P, Lin K, Shu C, Zheng W, Lim CM, Huang Z. Fiber-Optic Raman Spectroscopy with Nature-Inspired Genetic Algorithms Enhances Real-Time in Vivo Detection and Diagnosis of Nasopharyngeal Carcinoma. Anal Chem 2019; 91:8101-8108. [PMID: 31135136 DOI: 10.1021/acs.analchem.9b00173] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Raman spectroscopy is an optical vibrational spectroscopic technique capable of probing specific biochemical structures and conformation of tissue and cells in biomedical systems. This work aims to assess the clinical utility of a fiber-optic Raman spectroscopy with nature-inspired genetic algorithms for enhancing in vivo detection and diagnosis of nasopharyngeal carcinoma (NPC) patients. The Raman diagnostic platform is developed based on simultaneous fingerprint (FP) and high-wavenumber (HW) fiber-optic Raman endoscopy associated with genetic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA). A total of 2126 in vivo FP/HW Raman spectra (598 NPC, 1528 normal) acquired from 113 tissue sites of 14 NPC patients and 48 healthy subjects during nasopharyngeal endoscopic examinations. Distinct Raman peaks have been identified (853 cm-1 - proteins, 1209 cm-1 - phenylalanine, 1265 cm-1 - proteins, 1335 cm-1 - proteins and nucleic acids, 1554 cm-1 - tryptophan, porphyrin, 2885 cm-1 - lipids, 2940 cm-1 - proteins, 3009 cm-1 - lipids, and 3250 cm-1 - water) that are related to the significant biochemical changes ( p < 1 × 10-5) in NPC compared to normal tissue. Raman diagnostic performance is evaluated through the leave-one-object (tissue site)-out cross-validation (LOOCV) method. A statistically significant GA-PLS-LDA model ( p < 1 × 10-5) on FP/HW Raman yields a CV diagnostic accuracy of 98.23% (111/113), sensitivity of 93.33% (28/30), and specificity of 100% (83/83) for NPC classification. This work demonstrates that the fiber-optic FP/HW Raman diagnostic platform developed has great promise for improving real-time in vivo detection and diagnosis of NPC at the molecular level during clinical nasopharyngeal endoscopy.
Collapse
Affiliation(s)
- Petar Žuvela
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering , National University of Singapore , 9 Engineering Drive 1 , Singapore 117576
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering , National University of Singapore , 9 Engineering Drive 1 , Singapore 117576
| | - Chi Shu
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering , National University of Singapore , 9 Engineering Drive 1 , Singapore 117576
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering , National University of Singapore , 9 Engineering Drive 1 , Singapore 117576
| | - Chwee Ming Lim
- Department of Otolaryngology, Head and Neck Surgery , National University of Singapore and National University Health System , Singapore 119074
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering , National University of Singapore , 9 Engineering Drive 1 , Singapore 117576
| |
Collapse
|
30
|
Accuracy of Raman spectroscopy in discrimination of nasopharyngeal carcinoma from normal samples: a systematic review and meta-analysis. J Cancer Res Clin Oncol 2019; 145:1811-1821. [DOI: 10.1007/s00432-019-02934-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 05/10/2019] [Indexed: 12/30/2022]
|
31
|
Lin X, Wang L, Lin H, Lin D, Lin J, Liu X, Qiu S, Xu Y, Chen G, Feng S. A novel urine analysis technique combining affinity chromatography with Au nanoparticle based surface enhanced Raman spectroscopy for potential applications in non-invasive cancer screening. JOURNAL OF BIOPHOTONICS 2019; 12:e201800327. [PMID: 30447050 DOI: 10.1002/jbio.201800327] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 10/22/2018] [Accepted: 11/14/2018] [Indexed: 05/20/2023]
Abstract
Modified nucleoside in urine samples is one of the most common biomarkers for cancer screening. Therefore, we developed a novel detection method for modified nucleoside detection in human urine. In this work, the modified nucleoside from real cancer patient's urine samples was first separated and purified using the affinity chromatography (AC) technology relying on its specific adsorption capacity. Then, surface-enhanced Raman spectroscopy (SERS) technology with the capability of single molecular detection was used to sensitively characterize the biomolecular features of modified nucleoside. A total of 141 high-quality SERS spectra of urinary modified nucleoside can be obtained from 50 gastric cancer patients and 43 breast cancer patients, as well as 48 healthy volunteers. Using principal component analysis combined with linear discriminant analysis (PCA-LDA), the diagnostic sensitivities for identifying gastric cancer vs normal, breast cancer vs normal, gastric cancer vs breast cancer were 84.0%, 76.7% and 82.0%, respectively, and the corresponding diagnostic specificities for each combination were 95.8%, 87.5% and 90.7%, respectively. These results show that this novel method based on urinary modified nucleoside detection combining AC and SERS technologies holds promising potential for developing a specific, non-invasive and label-free tool for cancer screening.
Collapse
Affiliation(s)
- Xueliang Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Lingna Wang
- College of Chemistry and Materials Science, Fujian Normal University, Fuzhou, China
| | - Huijing Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Duo Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Jinyong Lin
- Radiation Oncology Department, Fujian Cancer Hospital, Fujian Medical University, Fuzhou, China
- Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fujian Cancer Hospital, Fuzhou, China
| | - Xiujie Liu
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Sufang Qiu
- Radiation Oncology Department, Fujian Cancer Hospital, Fujian Medical University, Fuzhou, China
- Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fujian Cancer Hospital, Fuzhou, China
| | - Yuanji Xu
- Radiation Oncology Department, Fujian Cancer Hospital, Fujian Medical University, Fuzhou, China
- Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fujian Cancer Hospital, Fuzhou, China
| | - Guannan Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Shangyuan Feng
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| |
Collapse
|
32
|
Abstract
PURPOSE The aim of the study is to use Raman spectroscopy to analyze the biochemical composition of medulloblastoma and normal tissues from the safety margin of the CNS and to find specific Raman biomarkers capable of differentiating between tumorous and normal tissues. METHODS The tissue samples consisted of medulloblastoma (grade IV) (n = 11). The tissues from the negative margins were used as normal controls. Raman images were generated by a confocal Raman microscope-WITec alpha 300 RSA. RESULTS Raman vibrational signatures can predict which tissue has tumorous biochemistry and can identify medulloblastoma. The Raman technique makes use of the fact that tumors contain large amounts of protein and far less lipids (fatty compounds), while healthy tissue is rich in both. CONCLUSION The ability of Raman spectroscopy and imaging to detect medulloblastoma tumors fills the niche in diagnostics. These powerful analytical techniques are capable of monitoring tissue morphology and biochemistry. Our results demonstrate that RS can be used to discriminate between normal and medulloblastoma tissues.
Collapse
Affiliation(s)
- Bartosz Polis
- Department of Neurosurgery and Neurotraumatology, Polish Mother's Memorial Hospital Research Institute, 281/289 Rzgowska St., 93-338, Lodz, Poland.
| | - Anna Imiela
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590, Lodz, Poland
| | - Lech Polis
- Department of Neurosurgery and Neurotraumatology, Polish Mother's Memorial Hospital Research Institute, 281/289 Rzgowska St., 93-338, Lodz, Poland
| | - Halina Abramczyk
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590, Lodz, Poland
| |
Collapse
|
33
|
Cordero E, Latka I, Matthäus C, Schie I, Popp J. In-vivo Raman spectroscopy: from basics to applications. JOURNAL OF BIOMEDICAL OPTICS 2018; 23:1-23. [PMID: 29956506 DOI: 10.1117/1.jbo.23.7.071210] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/23/2018] [Indexed: 05/20/2023]
Abstract
For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.
Collapse
Affiliation(s)
- Eliana Cordero
- Leibniz-Institut für Photonische Technologien e.V., Germany
| | - Ines Latka
- Leibniz-Institut für Photonische Technologien e.V., Germany
| | - Christian Matthäus
- Leibniz-Institut für Photonische Technologien e.V., Germany
- Institut für Physikalische Chemie, Friedrich-Schiller-Univ. Jena, Germany
- Abbe Ctr. of Photonics, Germany
| | - Iwan Schie
- Leibniz-Institut für Photonische Technologien e.V., Germany
| | - Jürgen Popp
- Leibniz-Institut für Photonische Technologien e.V., Germany
- Institute für Physikalische Chemie, Friedrich-Schiller-Univ. Jena, Germany
| |
Collapse
|
34
|
Wang J, Zheng W, Lin K, Huang Z. Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique. JOURNAL OF BIOPHOTONICS 2018; 11. [PMID: 28985038 DOI: 10.1002/jbio.201700113] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 10/04/2017] [Indexed: 05/08/2023]
Abstract
This study aims to characterize biochemical and morphological variations of the clinically relevant anatomical locations of in vivo oral tissue (ie, alveolar process, lateral tongue and floor of the mouth) by using hybrid Raman spectroscopy (RS) and optical coherence tomography (OCT) technique. A total of 1049 in vivo fingerprint (FP: 800-1800 cm-1 ) and high wavenumber (HW: 2800-3600 cm-1 ) Raman spectra were acquired from different oral tissue (alveolar process = 331, lateral tongue = 339 and floor of mouth = 379) of 26 normal subjects in the oral cavity under the OCT imaging guidance. The total Raman dataset were split into 2 parts: 80% for training and 20% for testing. Tissue optical attenuation coefficients of alveolar process, lateral tongue and the floor of the mouth were derived from OCT images, revealing the inter-anatomical morphological differences; while RS uncovers subtle FP/HW Raman spectral differences among different oral tissues that can be attributed to the differences in inter- and intra-cellular proteins, lipids, DNA and water structures and conformations, enlightening biochemical variability of different oral tissues at the molecular level. Partial least squares-discriminant analysis implemented on the training dataset show that the integrated tissue optical attenuation coefficients and FP/HW Raman spectra provide diagnostic sensitivities of 99.6%, 82.3%, 50.2%, and specificities of 97.0%, 75.1%, 92.1%, respectively, which are superior to using either RS (sensitivities of 90.2%, 77.5%, 48.8%, and specificities of 95.8%, 72.1%, 88.8%) or optical attenuation coefficients derived from OCT (sensitivities of 75.0%, 78.2%, 47.2%, and specificities of 96.2%, 67.7%, 85.0%) for the differentiation among alveolar process, lateral tongue and the floor of the mouth. Furthermore, the diagnostic algorithms applied to the independent testing dataset based on hybrid RS-OCT technique gives predictive diagnostic sensitivities of 100%, 76.5%, 51.3%, and specificities of 95.1%, 77.6%, 89.6%, respectively, for the classifications among alveolar process, lateral tongue and the floor of the mouth, which performs much better than either RS or optical attenuation coefficient derived from OCT imaging. This work suggests that inter-anatomical morphological and biochemical variability are significant which should be considered as an important parameter in the interpretation and rendering of hybrid RS-OCT technique for oral tissue diagnosis and characterization.
Collapse
Affiliation(s)
- Jianfeng Wang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Wei Zheng
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
| | - Kan Lin
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
| | - Zhiwei Huang
- Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| |
Collapse
|
35
|
SEKINE R, SATO S, TANAKA JI, KAGOSHIMA H, AOKI T, MURAKAMI M. Potential Application of Raman Spectroscopy for Real-time Diagnosis and Classification of Colorectal Cancer. ACTA ACUST UNITED AC 2018. [DOI: 10.15369/sujms.30.381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Ryuichi SEKINE
- Department of Surgery, Division of General and Gastroenterological Surgery, Showa University Fujigaoka Hospital
| | - Sumito SATO
- Department of Surgery, Division of General and Gastroenterological Surgery, Showa University Fujigaoka Hospital
| | - Jun-ichi TANAKA
- Department of Surgery, Division of General and Gastroenterological Surgery, Showa University Fujigaoka Hospital
| | | | - Takeshi AOKI
- Department of Surgery, Division of General and Gastroenterological Surgery, Showa University School of Medicine
| | - Masahiko MURAKAMI
- Department of Surgery, Division of General and Gastroenterological Surgery, Showa University School of Medicine
| |
Collapse
|