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van Breugel SJ, Low I, Christie ML, Pokorny MR, Nagarajan R, Holtkamp HU, Srinivasa K, Amirapu S, Nieuwoudt MK, Simpson MC, Zargar-Shoshtari K, Aguergaray C. Raman spectroscopy system for real-time diagnosis of clinically significant prostate cancer tissue. JOURNAL OF BIOPHOTONICS 2023; 16:e202200334. [PMID: 36715344 DOI: 10.1002/jbio.202200334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 05/17/2023]
Abstract
Prostate cancer (PCa) is a significant healthcare problem worldwide. Current diagnosis and treatment methods are limited by a lack of precise in vivo tissue analysis methods. Real-time cancer identification and grading could dramatically improve current protocols. Here, we report the testing of a thin optical probe using Raman spectroscopy (RS) and classification methods to detect and grade PCa accurately in real-time. We present the first clinical trial on fresh ex vivo biopsy cores from an 84 patient cohort. Findings from 2395 spectra measured on 599 biopsy cores show high accuracy for diagnosing and grading PCa. We can detect clinically significant PCa from benign and clinically insignificant PCa with 90% sensitivity and 80.2% specificity. We also demonstrate the ability to differentiate cancer grades with 90% sensitivity and specificity ≥82.8%. This work demonstrates the utility of RS for real-time PCa detection and grading during routine transrectal biopsy appointments.
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Affiliation(s)
- Suse J van Breugel
- The Photon Factory, University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Dodd-Walls Centre for Photonic and Quantum Technologies, University of Otago, Dunedin, New Zealand
| | - Irene Low
- Counties Manukau District Healthboard, Auckland, New Zealand
| | - Mary L Christie
- Counties Manukau District Healthboard, Auckland, New Zealand
| | - Morgan R Pokorny
- Counties Manukau District Healthboard, Auckland, New Zealand
- Auckland District Healthboard, Auckland, New Zealand
| | - Ramya Nagarajan
- Counties Manukau District Healthboard, Auckland, New Zealand
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Hannah U Holtkamp
- The Photon Factory, University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Komal Srinivasa
- Auckland District Healthboard, Auckland, New Zealand
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Satya Amirapu
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Michel K Nieuwoudt
- The Photon Factory, University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Dodd-Walls Centre for Photonic and Quantum Technologies, University of Otago, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington, New Zealand
| | - M Cather Simpson
- The Photon Factory, University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Dodd-Walls Centre for Photonic and Quantum Technologies, University of Otago, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
| | - Kamran Zargar-Shoshtari
- Counties Manukau District Healthboard, Auckland, New Zealand
- Auckland District Healthboard, Auckland, New Zealand
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Claude Aguergaray
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd-Walls Centre for Photonic and Quantum Technologies, University of Otago, Dunedin, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
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2
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David S, Tran T, Dallaire F, Sheehy G, Azzi F, Trudel D, Tremblay F, Omeroglu A, Leblond F, Meterissian S. In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:036009. [PMID: 37009577 PMCID: PMC10062385 DOI: 10.1117/1.jbo.28.3.036009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival. AIM Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer. APPROACH The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis. RESULTS Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940 cm - 1 and the symmetric ring breathing at 1004 cm - 1 associated with phenylalanine. CONCLUSIONS Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.
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Affiliation(s)
- Sandryne David
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Trang Tran
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Feryel Azzi
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
| | - Francine Tremblay
- McGill University Health Center, Department of Surgery, Montreal, Quebec, Canada
| | - Atilla Omeroglu
- McGill University Health Center, Department of Pathology, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Sarkis Meterissian
- McGill University Health Center, Department of Surgery, Montreal, Quebec, Canada
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Zheng X, Wu G, Lv G, Yin L, Lv X. Rapid discrimination of hepatic echinococcosis patients' serum using vibrational spectroscopy combined with support vector machines. Photodiagnosis Photodyn Ther 2022; 40:103027. [PMID: 35882291 DOI: 10.1016/j.pdpdt.2022.103027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/16/2022] [Accepted: 07/20/2022] [Indexed: 12/14/2022]
Abstract
Echinococcosis is a severe zoonotic parasitic disease, and it is continuing to be a significant public health issue. The course of the disease is usually slow, and patients often remain asymptomatic for years. There is no standardized and widely accepted treatment, so early and accurate diagnosis is essential. Herein, this study utilized vibrational spectroscopic techniques, namely Raman and Fourier Transform Infrared (FTIR) spectroscopy, to quickly and accurately distinguish hepatic echinococcosis (HE) patients' serum from the healthy group. Serum samples were collected from HE patients as well as healthy control subjects, and then the Raman and FTIR spectra of the two groups were recorded. After a series of pre-processing, support vector machines (SVMs) were then used to establish the classification models for the two spectral data sets. The performance of each diagnostic model was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation methods, respectively. For the distinction between HE and healthy groups, these two spectroscopic techniques had achieved satisfactory classification results, and the diagnostic capabilities of the Raman technique were comparable to that of the FTIR method. The results demonstrate that vibrational spectroscopy has great potential in the rapid and accurate detection of HE and is expected to make up for the shortcomings of the existing clinical diagnosis methods.
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Affiliation(s)
- Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
| | - Guodong Lv
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Longfei Yin
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xiaoyi Lv
- School of Software, Xinjiang University, Urumqi 830091, China
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Lucas IT, Bazin D, Daudon M. Raman opportunities in the field of pathological calcifications. CR CHIM 2022. [DOI: 10.5802/crchim.110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Picot F, Shams R, Dallaire F, Sheehy G, Trang T, Grajales D, Birlea M, Trudel D, Ménard C, Kadoury S, Leblond F. Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220045GRR. [PMID: 36045491 PMCID: PMC9433338 DOI: 10.1117/1.jbo.27.9.095003] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/16/2022] [Indexed: 05/28/2023]
Abstract
SIGNIFICANCE The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. AIM To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. APPROACH A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. RESULTS A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). CONCLUSIONS PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.
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Affiliation(s)
- Fabien Picot
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Roozbeh Shams
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Medical Laboratory, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Tran Trang
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - David Grajales
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Medical Laboratory, Montreal, Quebec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Cynthia Ménard
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Medical Laboratory, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
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Grajales D, Picot F, Shams R, Dallaire F, Sheehy G, Alley S, Barkati M, Delouya G, Carrier JF, Birlea M, Trudel D, Leblond F, Ménard C, Kadoury S. Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220064GR. [PMID: 36085571 PMCID: PMC9459023 DOI: 10.1117/1.jbo.27.9.095004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/12/2022] [Indexed: 06/01/2023]
Abstract
SIGNIFICANCE The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. AIM To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). APPROACH In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. RESULTS RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ). CONCLUSIONS In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.
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Affiliation(s)
- David Grajales
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Fabien Picot
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Roozbeh Shams
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Stephanie Alley
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Maroie Barkati
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Guila Delouya
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Jean-Francois Carrier
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
- Institut du Cancer de Montréal, Montreal, Québec, Canada
| | - Cynthia Ménard
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Samuel Kadoury
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
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Wilson BC, Eu D. Optical Spectroscopy and Imaging in Surgical Management of Cancer Patients. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202100009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Brian C. Wilson
- Princess Margaret Cancer Centre/University Health Network 101 College Street Toronto Ontario Canada
- Department of Medical Biophysics, Faculty of Medicine University of Toronto Canada
| | - Donovan Eu
- Department of Otolaryngology‐Head and Neck Surgery‐Surgical Oncology, Princess Margaret Cancer Centre/University Health Network University of Toronto Canada
- Department of Otolaryngology‐Head and Neck Surgery National University Hospital System Singapore
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8
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Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance. Cancers (Basel) 2022; 14:cancers14051144. [PMID: 35267451 PMCID: PMC8909093 DOI: 10.3390/cancers14051144] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/23/2022] Open
Abstract
Simple Summary Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology. Abstract Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.
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Wang L, Patskovsky S, Gauthier-Soumis B, Meunier M. Porous Au-Ag Nanoparticles from Galvanic Replacement Applied as Single-Particle SERS Probe for Quantitative Monitoring. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2105209. [PMID: 34761520 DOI: 10.1002/smll.202105209] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/30/2021] [Indexed: 06/13/2023]
Abstract
Plasmonic nanostructures have raised the interest of biomedical applications of surface-enhanced Raman scattering (SERS). To improve the enhancement and produce sensitive SERS probes, porous Au-Ag alloy nanoparticles (NPs) are synthesized by dealloying Au-Ag alloy NP-precursors with Au or Ag core in aqueous colloidal environment through galvanic replacement reaction. The novel designed core-shell Au-Ag alloy NP-precursors facilitate controllable synthesis of porous nanostructure, and dealloying degree during the reaction has significant effect on structural and spectral properties of dealloyed porous NPs. Narrow-dispersed dealloyed NPs are obtained using NPs of Au/Ag ratio from 10/90 to 40/60 with Au and Ag core to produce solid core@porous shell and porous nanoshells, having rough surface, hollowness, and porosity around 30-60%. The clean nanostructure from colloidal synthesis exhibits a redshifted plasmon peak up to near-infrared region, and the large accessible surface induces highly localized surface plasmon resonance and generates robust SERS activity. Thus, the porous NPs produce intensely enhanced Raman signal up to 68-fold higher than 100 nm AuNP enhancement at single-particle level, and the estimated Raman enhancement around 7800, showing the potential for highly sensitive SERS probes. The single-particle SERS probes are effectively demonstrated in quantitative monitoring of anticancer drug Doxorubicin release.
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Affiliation(s)
- Lu Wang
- Laser Processing and Plasmonics Laboratory, Department of Engineering Physics, Polytechnique Montréal, C.P. 6079, Succ. Centre-ville, Montréal, Québec, H3C 3A7, Canada
| | - Sergiy Patskovsky
- Laser Processing and Plasmonics Laboratory, Department of Engineering Physics, Polytechnique Montréal, C.P. 6079, Succ. Centre-ville, Montréal, Québec, H3C 3A7, Canada
| | - Bastien Gauthier-Soumis
- Laser Processing and Plasmonics Laboratory, Department of Engineering Physics, Polytechnique Montréal, C.P. 6079, Succ. Centre-ville, Montréal, Québec, H3C 3A7, Canada
| | - Michel Meunier
- Laser Processing and Plasmonics Laboratory, Department of Engineering Physics, Polytechnique Montréal, C.P. 6079, Succ. Centre-ville, Montréal, Québec, H3C 3A7, Canada
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Becker L, Janssen N, Layland SL, Mürdter TE, Nies AT, Schenke-Layland K, Marzi J. Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring. Cancers (Basel) 2021; 13:cancers13225682. [PMID: 34830837 PMCID: PMC8616063 DOI: 10.3390/cancers13225682] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/05/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.
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Affiliation(s)
- Lucas Becker
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
| | - Nicole Janssen
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Shannon L Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
| | - Thomas E Mürdter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Anne T Nies
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Katja Schenke-Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
- Cardiovascular Research Laboratories, Department of Medicine/Cardiology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90073, USA
| | - Julia Marzi
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
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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.
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12
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Ye J, Li J, Lu M, Qi X, Li B, Wei H, Li Y, Zou M. Dual-wavelength excitation combined Raman spectroscopy for detection of highly fluorescent samples. APPLIED OPTICS 2021; 60:6918-6927. [PMID: 34613173 DOI: 10.1364/ao.431564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
As fluorescence is the major limitation in Raman scattering, near-infrared excitation wavelength (>780nm) is preferred for fluorescence suppression in Raman spectroscopy. To reduce the risk of fluorescence interference, we developed a dual-wavelength excitation combined Raman spectroscopy (DWECRS) system at 785 and 830 nm. By a common optical path, each laser beam was focused on the same region of the sample by a single objective lens, and the dual-wavelength excitation Raman spectra were detected by a single CCD detector; in addition, 785 and 830 nm excitation Raman spectra can be directly constructed as combined Raman spectrum in the DWECRS system. The results of pure peanut oil and glycerol indicate that the combined Raman spectrum cannot only reduce fluorescence interference but also keep a high signal-to-noise ratio in the high-wavenumber region. The results of dye-ethanol solutions with different concentrations show that the handheld DWECRS system can be used as a smart method to dodge strong fluorescence. Furthermore, we developed a peak intensity ratio method with the DWECRS system to distinguish different types of edible oils. The peak intensity ratio distribution chart of edible oils showed each oil normalized center was relatively independent and nonoverlapped, which can be used as the basis of edible oil classification analysis. In the future, the DWECRS system has potential to be used as a tool for more complex applications.
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Abstract
Raman spectroscopy has shown great potential in detecting nonmelanoma skin cancer accurately and quickly; however, little direct evidence exists on the sensitivity of measurements to the underlying anatomy. Here, we aimed to correlate Raman measurements directly to the underlying tissue anatomy. We acquired Raman spectra of ex vivo skin tissue from 25 patients undergoing Mohs surgery with a fiber probe. We utilized a previously developed biophysical model to extract key biomarkers in the skin from the Raman spectra. We then examined the correlations between the biomarkers and the major skin structures (including the dermis, sebaceous glands, hair follicles, fat, and two types of nonmelanoma skin cancer—basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). SCC had a significantly different concentration of keratin, collagen, and nucleic acid than normal structures, while ceramide differentiated BCC from normal structures. Our findings identified the key proteins, lipids, and nucleic acids that discriminate nonmelanoma tumors and healthy skin using Raman spectroscopy. These markers may be promising surgical guidance tools for detecting tumors in resection margins.
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Doherty T, McKeever S, Al-Attar N, Murphy T, Aura C, Rahman A, O'Neill A, Finn SP, Kay E, Gallagher WM, Watson RWG, Gowen A, Jackman P. Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection. Analyst 2021; 146:4195-4211. [PMID: 34060548 DOI: 10.1039/d1an00075f] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.
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Affiliation(s)
- Trevor Doherty
- Technological University Dublin, School of Computer Science, City Campus, Grangegorman Lower, Dublin 7, Ireland.
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Design of a Multimodal Imaging System and Its First Application to Distinguish Grey and White Matter of Brain Tissue. A Proof-of-Concept-Study. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11114777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Multimodal imaging gains increasing popularity for biomedical applications. This article presents the design of a novel multimodal imaging system. The centerpiece is a light microscope operating in the incident and transmitted light mode. Additionally, Raman spectroscopy and VIS/NIR reflectance spectroscopy are adapted. The proof-of-concept is realized to distinguish between grey matter (GM) and white matter (WM) of normal mouse brain tissue. Besides Raman and VIS/NIR spectroscopy, the following optical microscopy techniques are applied in the incident light mode: brightfield, darkfield, and polarization microscopy. To complement the study, brightfield images of a hematoxylin and eosin (H&E) stained cryosection in the transmitted light mode are recorded using the same imaging system. Data acquisition based on polarization microscopy and Raman spectroscopy gives the best results regarding the tissue differentiation of the unstained section. In addition to the discrimination of GM and WM, both modalities are suited to highlight differences in the density of myelinated axons. For Raman spectroscopy, this is achieved by calculating the sum of two intensity peak ratios (I2857 + I2888)/I2930 in the high-wavenumber region. For an optimum combination of the modalities, it is recommended to apply the molecule-specific but time-consuming Raman spectroscopy to smaller regions of interest, which have previously been identified by the microscopic modes.
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16
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Fan Y, Chen C, Xie X, Yang B, Wu W, Yue F, Lv X, Chen C. Rapid noninvasive screening of cerebral ischemia and cerebral infarction based on tear Raman spectroscopy combined with multiple machine learning algorithms. Lasers Med Sci 2021; 37:417-424. [PMID: 33970383 DOI: 10.1007/s10103-021-03273-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/10/2021] [Indexed: 11/30/2022]
Abstract
Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future.
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Affiliation(s)
- Yangyang Fan
- College of Software, Xinjiang University, Urumqi, 830046, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
- Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China
| | - Xiaodong Xie
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Ophthalmology, Urumqi, 830001, China.
| | - Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Wei Wu
- College of Software, Xinjiang University, Urumqi, 830046, China
| | - Feilong Yue
- College of Software, Xinjiang University, Urumqi, 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China.
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
- Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China.
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
- Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China
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Artemyev DN, Kukushkin VI, Avraamova ST, Aleksandrov NS, Kirillov YA. Using the Method of "Optical Biopsy" of Prostatic Tissue to Diagnose Prostate Cancer. Molecules 2021; 26:molecules26071961. [PMID: 33807257 PMCID: PMC8036841 DOI: 10.3390/molecules26071961] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/21/2021] [Accepted: 03/26/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Analytical discrimination models of Raman spectra of prostate cancer tissue were constructed by using the projections onto latent structures data analysis (PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers, and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. Abstract The possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.
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Affiliation(s)
- Dmitry N. Artemyev
- Laser and Biotechnical Systems Department, Samara National Research University, 443086 Samara, Russia;
| | - Vladimir I. Kukushkin
- Laboratory of Non-Equilibrium Electronic Processes, Institute of Solid State Physics Russian Academy of Sciences, 142432 Chernogolovka, Russia
- Correspondence: ; Tel.: +7-905-502-9277
| | - Sofia T. Avraamova
- Department of Pathological Anatomy, The First Sechenov Moscow State Medical University under Ministry of Health of the Russian Federation, 119146 Moscow, Russia; (S.T.A.); (N.S.A.)
| | - Nikolay S. Aleksandrov
- Department of Pathological Anatomy, The First Sechenov Moscow State Medical University under Ministry of Health of the Russian Federation, 119146 Moscow, Russia; (S.T.A.); (N.S.A.)
| | - Yuri A. Kirillov
- Laboratory of Clinical Morphology, Research Institute of Human Morphology, 117418 Moscow, Russia;
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Hubbard TJE, Dudgeon AP, Ferguson DJ, Shore AC, Stone N. Utilization of Raman spectroscopy to identify breast cancer from the water content in surgical samples containing blue dye. TRANSLATIONAL BIOPHOTONICS 2021. [DOI: 10.1002/tbio.202000023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Thomas J. E. Hubbard
- Institute of Biomedical and Clinical Science University of Exeter Medical School Exeter UK
- NIHR Exeter Clinical Research Facility, Royal Devon and Exeter Hospital Exeter UK
- Biomedical Physics Group, Department of Physics and Astronomy University of Exeter Exeter UK
- Royal Devon and Exeter Hospital Exeter UK
| | - Alexander P. Dudgeon
- Biomedical Physics Group, Department of Physics and Astronomy University of Exeter Exeter UK
| | - Douglas J. Ferguson
- Institute of Biomedical and Clinical Science University of Exeter Medical School Exeter UK
- NIHR Exeter Clinical Research Facility, Royal Devon and Exeter Hospital Exeter UK
- Royal Devon and Exeter Hospital Exeter UK
| | - Angela C. Shore
- Institute of Biomedical and Clinical Science University of Exeter Medical School Exeter UK
- NIHR Exeter Clinical Research Facility, Royal Devon and Exeter Hospital Exeter UK
| | - Nicholas Stone
- Institute of Biomedical and Clinical Science University of Exeter Medical School Exeter UK
- NIHR Exeter Clinical Research Facility, Royal Devon and Exeter Hospital Exeter UK
- Biomedical Physics Group, Department of Physics and Astronomy University of Exeter Exeter UK
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19
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Picot F, Daoust F, Sheehy G, Dallaire F, Chaikho L, Bégin T, Kadoury S, Leblond F. Data consistency and classification model transferability across biomedical Raman spectroscopy systems. TRANSLATIONAL BIOPHOTONICS 2020. [DOI: 10.1002/tbio.202000019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Fabien Picot
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - François Daoust
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Guillaume Sheehy
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Frédérick Dallaire
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Layal Chaikho
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
| | - Théophile Bégin
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
| | - Samuel Kadoury
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Frédéric Leblond
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
- Institut du Cancer de Montréal Montreal Quebec Canada
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20
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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.
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21
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Shams R, Picot F, Grajales D, Sheehy G, Dallaire F, Birlea M, Saad F, Trudel D, Menard C, Leblond F, Kadoury S. Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions. Int J Comput Assist Radiol Surg 2020; 15:867-876. [PMID: 32227280 DOI: 10.1007/s11548-020-02136-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 03/18/2020] [Indexed: 01/13/2023]
Abstract
PURPOSE Transrectal ultrasound (TRUS) image guidance is the standard of care for diagnostic and therapeutic interventions in prostate cancer (PCa) patients, but can lead to high false-negative rates, compromising downstream effectiveness of therapeutic choices. A promising approach to improve in-situ detection of PCa lies in using the optical properties of the tissue to discern cancer from healthy tissue. In this work, we present the first in-situ image-guided navigation system for a spatially tracked Raman spectroscopy probe integrated in a PCa workflow, capturing the optical tissue fingerprint. The probe is guided with fused TRUS/MR imaging and tested with both tissue-simulating phantoms and ex-vivo prostates. The workflow was designed to be integrated the clinical workflow for trans-perineal prostate biopsies, as well as for high-dose rate (HDR) brachytherapy. METHODS The proposed system developed in 3D Slicer includes an electromagnetically tracked Raman spectroscopy probe, along with tracked TRUS imaging automatically registered to diagnostic MRI. The proposed system is tested on both custom gelatin tissue-simulating optical phantoms and biological tissue phantoms. A random-forest classifier was then trained on optical spectrums from ex-vivo prostates following prostatectomy using our optical probe. Preliminary in-human results are presented with the Raman spectroscopy instrument to detect malignant tissue in-situ with histopathology confirmation. RESULTS In 5 synthetic gelatin and biological tissue phantoms, we demonstrate the ability of the image-guided Raman system by detecting over 95% of lesions, based on biopsy samples. The included lesion volumes ranged from 0.1 to 0.61 cc. We showed the compatibility of our workflow with the current HDR brachytherapy setup. In ex-vivo prostates of PCa patients, the system showed a 81% detection accuracy in high grade lesions. CONCLUSION Pre-clinical experiments demonstrated promising results for in-situ confirmation of lesion locations in prostates using Raman spectroscopy, both in phantoms and human ex-vivo prostate tissue, which is required for integration in HDR brachytherapy procedures.
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Affiliation(s)
| | | | | | | | | | - Mirela Birlea
- Centre Hospitalier de l'Universite de Montreal Research Center, Montreal, Canada
| | - Fred Saad
- Centre Hospitalier de l'Universite de Montreal Research Center, Montreal, Canada
| | - Dominique Trudel
- Centre Hospitalier de l'Universite de Montreal Research Center, Montreal, Canada
| | - Cynthia Menard
- Centre Hospitalier de l'Universite de Montreal Research Center, Montreal, Canada
| | | | - Samuel Kadoury
- Polytechnique Montreal, Montreal, Canada.
- Centre Hospitalier de l'Universite de Montreal Research Center, Montreal, Canada.
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22
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Editorial for: Bertoni et al. ex vivo fluorescence confocal microscopy: prostatic and periprostatic tissues atlas and evaluation of the learning curve. Virchows Arch 2020; 476:487-488. [PMID: 32006120 PMCID: PMC7156354 DOI: 10.1007/s00428-020-02763-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 01/13/2020] [Accepted: 01/23/2020] [Indexed: 11/29/2022]
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Ralbovsky NM, Lednev IK. Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chem Soc Rev 2020; 49:7428-7453. [DOI: 10.1039/d0cs01019g] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.
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Affiliation(s)
| | - Igor K. Lednev
- Department of Chemistry
- University at Albany
- SUNY
- Albany
- USA
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Bergholt MS, Serio A, Albro MB. Raman Spectroscopy: Guiding Light for the Extracellular Matrix. Front Bioeng Biotechnol 2019; 7:303. [PMID: 31737621 PMCID: PMC6839578 DOI: 10.3389/fbioe.2019.00303] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 10/16/2019] [Indexed: 12/12/2022] Open
Abstract
The extracellular matrix (ECM) consists of a complex mesh of proteins, glycoproteins, and glycosaminoglycans, and is essential for maintaining the integrity and function of biological tissues. Imaging and biomolecular characterization of the ECM is critical for understanding disease onset and for the development of novel, disease-modifying therapeutics. Recently, there has been a growing interest in the use of Raman spectroscopy to characterize the ECM. Raman spectroscopy is a label-free vibrational technique that offers unique insights into the structure and composition of tissues and cells at the molecular level. This technique can be applied across a broad range of ECM imaging applications, which encompass in vitro, ex vivo, and in vivo analysis. State-of-the-art confocal Raman microscopy imaging now enables label-free assessments of the ECM structure and composition in tissue sections with a remarkably high degree of biomolecular specificity. Further, novel fiber-optic instrumentation has opened up for clinical in vivo ECM diagnostic measurements across a range of tissue systems. A palette of advanced computational methods based on multivariate statistics, spectral unmixing, and machine learning can be applied to Raman data, allowing for the extraction of specific biochemical information of the ECM. Here, we review Raman spectroscopy techniques for ECM characterizations over a variety of exciting applications and tissue systems, including native tissue assessments (bone, cartilage, cardiovascular), regenerative medicine quality assessments, and diagnostics of disease states. We further discuss the challenges in the widespread adoption of Raman spectroscopy in biomedicine. The results of the latest discovery-driven Raman studies are summarized, illustrating the current and potential future applications of Raman spectroscopy in biomedicine.
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Affiliation(s)
- Mads S. Bergholt
- Centre for Craniofacial and Regenerative Biology, King's College London, London, United Kingdom
| | - Andrea Serio
- Centre for Craniofacial and Regenerative Biology, King's College London, London, United Kingdom
| | - Michael B. Albro
- Department of Mechanical Engineering, Boston University, Boston, MA, United States
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25
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Peterson W, Hiramatsu K, Goda K. Sagnac-enhanced impulsive stimulated Raman scattering for highly sensitive low-frequency Raman spectroscopy. OPTICS LETTERS 2019; 44:5282-5285. [PMID: 31674988 DOI: 10.1364/ol.44.005282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/03/2019] [Indexed: 06/10/2023]
Abstract
The "fingerprint" (500-1800 cm-1) and "high-frequency" (2400-4000 cm-1) regions in Raman spectroscopy are commonly used for label-free chemical analysis, while the "low-frequency" region (<200 cm-1) is often overlooked, despite containing rich information. This is largely due to the challenge of measuring weak Raman signals that are obscured by strong Rayleigh scattering. Here we propose and experimentally demonstrate Sagnac-enhanced impulsive stimulated Raman scattering (SE-ISRS), a filter-free method for time-domain Raman spectroscopy that overcomes the challenge and provides low-frequency Raman spectra at all probe frequencies. Using SE-ISRS for simultaneous low-frequency and fingerprint region measurements, we demonstrate a >5× enhancement of the signal-to-noise ratio compared to conventional ISRS spectroscopy.
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26
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Kim E, Rubinstein SM, Nead KT, Wojcieszynski AP, Gabriel PE, Warner JL. The Evolving Use of Electronic Health Records (EHR) for Research. Semin Radiat Oncol 2019; 29:354-361. [DOI: 10.1016/j.semradonc.2019.05.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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27
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Eissa A, Zoeir A, Sighinolfi MC, Puliatti S, Bevilacqua L, Del Prete C, Bertoni L, Azzoni P, Reggiani Bonetti L, Micali S, Bianchi G, Rocco B. "Real-time" Assessment of Surgical Margins During Radical Prostatectomy: State-of-the-Art. Clin Genitourin Cancer 2019; 18:95-104. [PMID: 31784282 DOI: 10.1016/j.clgc.2019.07.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 07/14/2019] [Accepted: 07/15/2019] [Indexed: 01/18/2023]
Abstract
Histopathologic examination of the pathologic specimens using hematoxylin & eosin stains represents the backbone of the modern pathology. It is time-consuming; thus, "real-time" assessment of prostatic and periprostatic tissue has gained special interest in the diagnosis and management of prostate cancer. The current study focuses on the review of the different available techniques for "real-time" evaluation of surgical margins during radical prostatectomy (RP). We performed a comprehensive search of the Medline database to identify all the articles discussing "real-time" or intraoperative assessment of surgical margins during RP. Several filters were applied to the search to include only English articles performed on human subjects and published between January 2000 and March 2019. The search revealed several options for pathologic assessment of surgical margins including intraoperative frozen sections, confocal laser endomicroscopy, optical spectroscopy, photodynamic diagnosis, optical coherence tomography, multiphoton microscopy, structured illumination microscopy, 3D augmented reality, and ex vivo fluorescence confocal microscope. Frozen section represents the gold standard technique for real-time pathologic examinations of surgical margins during RP; however, several other options showed promising results in the initial clinical trials, and considering the rapid development in the field of molecular and cellular imaging, some of these options may serve as an alternative to frozen section.
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Affiliation(s)
- Ahmed Eissa
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy; Urology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Ahmed Zoeir
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy; Urology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | | | - Stefano Puliatti
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Luigi Bevilacqua
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Chiara Del Prete
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Laura Bertoni
- Department of Pathology, University of Modena and Reggio Emilia, Modena, Italy
| | - Paola Azzoni
- Department of Pathology, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Salvatore Micali
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Giampaolo Bianchi
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy
| | - Bernardo Rocco
- Department of Urology, University of Modena and Reggio Emilia, Modena, Italy.
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28
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Yu M, Yan H, Xia J, Zhu L, Zhang T, Zhu Z, Lou X, Sun G, Dong M. Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy. Photodiagnosis Photodyn Ther 2019; 26:430-435. [PMID: 31082525 DOI: 10.1016/j.pdpdt.2019.05.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 04/30/2019] [Accepted: 05/09/2019] [Indexed: 10/26/2022]
Abstract
With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with each block including 1 convolutional layer and 1 max-pooling layer are used to extract the nonlinear feature representations from Raman spectra. The derived features form a representative vector, which is fed into a fully-connected network for performing classification task. Experimental results demonstrated that the proposed method achieved high sensitivity (99.31%) and specificity (94.44%). To show the superiority for the ConvNets classifier, comparison results with the state-of-the-art methods show it had a competitive classification accuracy. Moreover, these promising results may pave the way to apply the deep ConvNets model in the fiber optic Raman instrument for intra-operative evaluation of TSCC resection margins and improve patient survival.
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Affiliation(s)
- Mingxin Yu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
| | - Hao Yan
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
| | - Jiabin Xia
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
| | - Lianqing Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
| | - Tao Zhang
- Department of stomatology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, China.
| | - Zhihui Zhu
- Department of stomatology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, China.
| | - Xiaoping Lou
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
| | - Guangkai Sun
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
| | - Mingli Dong
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, No. 6 Hongxia Road, Chaoyang District, Beijing 100015, China.
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29
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Desroches J, Lemoine É, Pinto M, Marple E, Urmey K, Diaz R, Guiot MC, Wilson BC, Petrecca K, Leblond F. Development and first in-human use of a Raman spectroscopy guidance system integrated with a brain biopsy needle. JOURNAL OF BIOPHOTONICS 2019; 12:e201800396. [PMID: 30636032 DOI: 10.1002/jbio.201800396] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 05/22/2023]
Abstract
Navigation-guided brain biopsies are the standard of care for diagnosis of several brain pathologies. However, imprecise targeting and tissue heterogeneity often hinder obtaining high-quality tissue samples, resulting in poor diagnostic yield. We report the development and first clinical testing of a navigation-guided fiberoptic Raman probe that allows surgeons to interrogate brain tissue in situ at the tip of the biopsy needle prior to tissue removal. The 900 μm diameter probe can detect high spectral quality Raman signals in both the fingerprint and high wavenumber spectral regions with minimal disruption to the neurosurgical workflow. The probe was tested in three brain tumor patients, and the acquired spectra in both normal brain and tumor tissue demonstrated the expected spectral features, indicating the quality of the data. As a proof-of-concept, we also demonstrate the consistency of the acquired Raman signal with different systems and experimental settings. Additional clinical development is planned to further evaluate the performance of the system and develop a statistical model for real-time tissue classification during the biopsy procedure.
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Affiliation(s)
- Joannie Desroches
- Department of Engineering Physics, Polytechnique Montreal, Montreal, Québec, Canada
- Laboratory of Radiological Optics, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Québec, Canada
| | - Émile Lemoine
- Department of Engineering Physics, Polytechnique Montreal, Montreal, Québec, Canada
- Laboratory of Radiological Optics, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Québec, Canada
| | - Michael Pinto
- Department of Engineering Physics, Polytechnique Montreal, Montreal, Québec, Canada
| | - Eric Marple
- Research and development, EMVision LLC, Loxahatchee, Florida
| | - Kirk Urmey
- Research and development, EMVision LLC, Loxahatchee, Florida
| | - Roberto Diaz
- Brain Tumour Research Centre, Montreal Neurological Institute and Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Marie-Christine Guiot
- Division of Neuropathology, Department of Pathology, Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada
| | - Brian C Wilson
- Laser Biophysics group, Princess Margaret Cancer Centre-University Health Network/University of Toronto, Toronto, Ontario, Canada
| | - Kevin Petrecca
- Brain Tumour Research Centre, Montreal Neurological Institute and Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Frédéric Leblond
- Department of Engineering Physics, Polytechnique Montreal, Montreal, Québec, Canada
- Laboratory of Radiological Optics, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Québec, Canada
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