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Kaufmann M, Vaysse PM, Savage A, Kooreman LFS, Janssen N, Varma S, Ren KYM, Merchant S, Engel CJ, Olde Damink SWM, Smidt ML, Shousha S, Chauhan H, Karali E, Kazanc E, Poulogiannis G, Fichtinger G, Tauber B, Leff DR, Pringle SD, Rudan JF, Heeren RMA, Porta Siegel T, Takáts Z, Balog J. Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study. Br J Cancer 2024:10.1038/s41416-024-02739-y. [PMID: 39294437 DOI: 10.1038/s41416-024-02739-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. METHODS A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. RESULTS Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites. CONCLUSIONS The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach.
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Affiliation(s)
- Martin Kaufmann
- Department of Surgery, Queen's University, Kingston, ON, Canada
- Gastrointestinal Diseases Research Unit, Kingston Health Sciences Centre, Kingston, ON, Canada
| | - Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands
- Department of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands
- Department of Otorhinolaryngology, Head & Neck Surgery, MUMC+, Maastricht, NL, Netherlands
| | - Adele Savage
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Loes F S Kooreman
- Department of Pathology, MUMC+, Maastricht, NL, Netherlands
- GROW School for Oncology and Reproduction, MUMC+, Maastricht, NL, Netherlands
| | - Natasja Janssen
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Sonal Varma
- Department of Pathology, Queen's University, Kingston, ON, Canada
| | - Kevin Yi Mi Ren
- Department of Pathology, Queen's University, Kingston, ON, Canada
| | - Shaila Merchant
- Department of Surgery, Queen's University, Kingston, ON, Canada
| | - Cecil Jay Engel
- Department of Surgery, Queen's University, Kingston, ON, Canada
| | - Steven W M Olde Damink
- Department of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands
- Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, Aachen, Germany
- NUTRIM School of Nutrition and Translational Research in Metabolism Faculty of Health, Maastricht University, Maastricht, NL, Netherlands
| | - Marjolein L Smidt
- Department of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands
- GROW School for Oncology and Reproduction, MUMC+, Maastricht, NL, Netherlands
| | | | - Hemali Chauhan
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Evdoxia Karali
- Signalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK
| | - Emine Kazanc
- Signalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK
| | - George Poulogiannis
- Signalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK
| | | | - Boglárka Tauber
- Qamcom Research & Technology Central Europe, Budapest, Hungary
| | - Daniel R Leff
- Department of Surgery and Cancer, Biosurgery and Surgical Technology, Imperial College London, London, UK
| | | | - John F Rudan
- Department of Surgery, Queen's University, Kingston, ON, Canada
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands
| | - Zoltán Takáts
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Júlia Balog
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Waters Research Center, Budapest, Hungary.
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Brorsen LF, McKenzie JS, Pinto FE, Glud M, Hansen HS, Haedersdal M, Takats Z, Janfelt C, Lerche CM. Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning. Exp Dermatol 2024; 33:e15141. [PMID: 39036889 DOI: 10.1111/exd.15141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/23/2024]
Abstract
Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.
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Affiliation(s)
- Lauritz F Brorsen
- Department of Dermatology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
| | - James S McKenzie
- Department of Digestion, Metabolism and Reproduction, Imperial College London, London, UK
| | - Fernanda E Pinto
- Department of Dermatology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Martin Glud
- Department of Dermatology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Harald S Hansen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Merete Haedersdal
- Department of Dermatology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Zoltan Takats
- Department of Digestion, Metabolism and Reproduction, Imperial College London, London, UK
| | - Christian Janfelt
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
| | - Catharina M Lerche
- Department of Dermatology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
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Connolly L, Fooladgar F, Jamzad A, Kaufmann M, Syeda A, Ren K, Abolmaesumi P, Rudan JF, McKay D, Fichtinger G, Mousavi P. ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry. Int J Comput Assist Radiol Surg 2024; 19:1129-1136. [PMID: 38600411 DOI: 10.1007/s11548-024-03106-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains. METHODS In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks. RESULTS Through extensive ablation studies, we show that we can surpass previous benchmarks of margin evaluation in BCC surgery using iKnife data, achieving an area under the receiver operating characteristic curve (AUC) of 81%. We also depict the attention maps of the developed DL models to evaluate the biological relevance of the embedding space CONCLUSIONS: We propose a new method for characterizing tissue at the surgical margins, using mass spectrometry data from cancer surgery.
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Affiliation(s)
| | | | | | | | | | - Kevin Ren
- Queen's University, Kingston, ON, Canada
| | | | | | - Doug McKay
- Queen's University, Kingston, ON, Canada
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Brorsen LF, McKenzie JS, Tullin MF, Bendtsen KMS, Pinto FE, Jensen HE, Haedersdal M, Takats Z, Janfelt C, Lerche CM. Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning. Sci Rep 2024; 14:11091. [PMID: 38750270 PMCID: PMC11096391 DOI: 10.1038/s41598-024-62023-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Abstract
Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables detailed, spatially resolved metabolomic analysis of tissue samples. Integrated with machine learning, MALDI-MSI could yield detailed information pertaining to the metabolic alterations characteristic for SCC. These insights have the potential to enhance SCC diagnosis and therapy, improving patient outcomes while tackling the growing disease burden. This study employs MALDI-MSI data, labelled according to histology, to train a supervised machine learning model (logistic regression) for the recognition and delineation of SCC. The model, based on data acquired from discrete tumor sections (n = 25) from a mouse model of SCC, achieved a predictive accuracy of 92.3% during cross-validation on the labelled data. A pathologist unacquainted with the dataset and tasked with evaluating the predictive power of the model in the unlabelled regions, agreed with the model prediction for over 99% of the tissue areas. These findings highlight the potential value of integrating MALDI-MSI with machine learning to characterize and delineate SCC, suggesting a promising direction for the advancement of mass spectrometry techniques in the clinical diagnosis of SCC and related keratinocyte carcinomas.
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Affiliation(s)
- Lauritz F Brorsen
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Nielsine Nielsens Vej 9, 2400, Copenhagen, Denmark.
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark.
| | - James S McKenzie
- Department of Digestion, Metabolism and Reproduction, Imperial College London, London, UK
| | - Mette F Tullin
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
| | - Katja M S Bendtsen
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Fernanda E Pinto
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Nielsine Nielsens Vej 9, 2400, Copenhagen, Denmark
| | - Henrik E Jensen
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Haedersdal
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Nielsine Nielsens Vej 9, 2400, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Zoltan Takats
- Department of Digestion, Metabolism and Reproduction, Imperial College London, London, UK
| | - Christian Janfelt
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
| | - Catharina M Lerche
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Nielsine Nielsens Vej 9, 2400, Copenhagen, Denmark
- Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark
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Cafarella C, Mangraviti D, Rigano F, Dugo P, Mondello L. Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications. J Sep Sci 2024; 47:e2400155. [PMID: 38772742 DOI: 10.1002/jssc.202400155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.
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Affiliation(s)
- Cinzia Cafarella
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Domenica Mangraviti
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Francesca Rigano
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Paola Dugo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
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Vaysse PM, van den Hout MFCM, Engelen SME, Keymeulen KBMI, Bemelmans MHA, Heeren RMA, Olde Damink SWM, Porta Siegel T. Lipid profiling of electrosurgical vapors for real-time assistance of soft tissue sarcoma resection. J Surg Oncol 2024; 129:499-508. [PMID: 38050894 DOI: 10.1002/jso.27502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/03/2023] [Accepted: 10/15/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND Soft tissue sarcomas (STS) constitute a heterogeneous group of rare tumor entities. Treatment relies on challenging patient-tailored surgical resection. Real-time intraoperative lipid profiling of electrosurgical vapors by rapid evaporative ionization mass spectrometry (REIMS) may aid in achieving successful surgical R0 resection (i.e., microscopically negative-tumor margin resection). Here, we evaluate the ex vivo accuracy of REIMS to discriminate and identify various STS from normal surrounding tissue. METHODS Twenty-seven patients undergoing surgery for STS at Maastricht University Medical Center+ were included in the study. Samples of resected STS specimens were collected and analyzed ex vivo using REIMS. Electrosurgical cauterization of tumor and surrounding was generated successively in both cut and coagulation modes. Resected specimens were subsequently processed for gold standard histopathological review. Multivariate statistical analysis (principal component analysis-linear discriminant analysis) and leave-one patient-out cross-validation were employed to compare the classifications predicted by REIMS lipid profiles to the pathology classifications. Electrosurgical vapors produced during sarcoma resection were analyzed in vivo using REIMS. RESULTS In total, 1200 histopathologically-validated ex vivo REIMS lipid profiles were generated from 27 patients. Ex vivo REIMS lipid profiles classified STS and normal tissues with 95.5% accuracy. STS, adipose and muscle tissues were classified with 98.3% accuracy. Well-differentiated liposarcomas and adipose tissues could not be discriminated based on their respective lipid profiles. Distinction of leiomyosarcomas from other STS could be achieved with 96.6% accuracy. In vivo REIMS analyses generated intense mass spectrometric signals. CONCLUSION Lipid profiling by REIMS is able to discriminate and identify STS with high accuracy and therefore constitutes a potential asset to improve surgical resection of STS in the future.
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Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging institute (M4i), University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Otorhinolaryngology, Head & Neck Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Mari F C M van den Hout
- Department of Pathology, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sanne M E Engelen
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Marc H A Bemelmans
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging institute (M4i), University of Maastricht, Maastricht, The Netherlands
| | - Steven W M Olde Damink
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, Aachen, Germany
- NUTRIM School of Nutrition and Translational Research in Metabolism Faculty of Health, Maastricht University, Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging institute (M4i), University of Maastricht, Maastricht, The Netherlands
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Manoli E, Higginson J, Tolley N, Darzi A, Kinross J, Temelkuran B, Takats Z. Human robotic surgery with intraoperative tissue identification using rapid evaporation ionisation mass spectrometry. Sci Rep 2024; 14:1027. [PMID: 38200062 PMCID: PMC10781715 DOI: 10.1038/s41598-023-50942-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Instantaneous, continuous, and reliable information on the molecular biology of surgical target tissue could significantly contribute to the precision, safety, and speed of the intervention. In this work, we introduced a methodology for chemical tissue identification in robotic surgery using rapid evaporative ionisation mass spectrometry. We developed a surgical aerosol evacuation system that is compatible with a robotic platform enabling consistent intraoperative sample collection and assessed the feasibility of this platform during head and neck surgical cases, using two different surgical energy devices. Our data showed specific, characteristic lipid profiles associated with the tissue type including various ceramides, glycerophospholipids, and glycerolipids, as well as different ion formation mechanisms based on the energy device used. This platform allows continuous and accurate intraoperative mass spectrometry-based identification of ablated/resected tissue and in combination with robotic registration of images, time, and anatomical positions can improve the current robot-assisted surgical platforms and guide surgical strategy.
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Affiliation(s)
- Eftychios Manoli
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James Higginson
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Neil Tolley
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - James Kinross
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Burak Temelkuran
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- The Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Univ. Lille, INSERM U1192, Lille, France.
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Vlocskó M, Piffkó J, Janovszky Á. Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy. Cancers (Basel) 2023; 16:121. [PMID: 38201548 PMCID: PMC10777979 DOI: 10.3390/cancers16010121] [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: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.
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Affiliation(s)
| | | | - Ágnes Janovszky
- Department of Oral and Maxillofacial Surgery, Albert Szent-Györgyi Medical School, University of Szeged, Kálvária 57, H-6725 Szeged, Hungary; (M.V.); (J.P.)
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Gao H, Lin J, Jia X, Zhao Y, Wang S, Bai H, Ma Q. Real-time authentication of animal species origin of leather products using rapid evaporative ionization mass spectrometry and chemometric analysis. Talanta 2021; 225:122069. [PMID: 33592787 DOI: 10.1016/j.talanta.2020.122069] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 12/22/2020] [Accepted: 12/25/2020] [Indexed: 12/18/2022]
Abstract
Increasing accounts of fraud and persistent labeling problems have brought the authenticity of leather products into question. In this study, we developed an extremely simplified workflow for real-time, in situ, and unambiguous authentication of leather samples using rapid evaporative ionization mass spectrometry (REIMS) coupled with an electric soldering iron. Initially, authentic leather samples from cattle, sheep, pig, deer, ostrich, crocodile, and snake were used to create a chemometric model based on principal component analysis and linear discriminant analysis algorithms. The validated multivariate statistical model was then used to analyze and generate live classifications of commercial leather samples. In addition to REIMS analysis, the microstructures of leathers were characterized by scanning electron microscopy to provide complementary information. The current study is expected to provide a high-throughput tool with superior efficiency and accuracy for authenticating the identity of leathers and other consumer products of biogenic origin.
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Affiliation(s)
- Haiyan Gao
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China; Inner Mongolia Autonomous Region Institute of Product Quality Inspection, Huhhot 010070, China
| | - Jihong Lin
- Waters Corporation, Beijing 100176, China
| | | | - Yang Zhao
- National Quality Supervision and Testing Center for Leather Products, Beijing 100015, China
| | - Songying Wang
- Inner Mongolia Autonomous Region Institute of Product Quality Inspection, Huhhot 010070, China
| | - Hua Bai
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Qiang Ma
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China.
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