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Herrando AI, Castillo-Martin M, Galzerano A, Fernández L, Vieira P, Azevedo J, Parvaiz A, Cicchi R, Shcheslavskiy VI, Silva PG, Lagarto JL. Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues. BIOMEDICAL OPTICS EXPRESS 2024; 15:3507-3522. [PMID: 38867800 PMCID: PMC11166421 DOI: 10.1364/boe.505220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/08/2023] [Accepted: 11/12/2023] [Indexed: 06/14/2024]
Abstract
Advancements in optical imaging techniques have revolutionized the field of biomedical research, allowing for the comprehensive characterization of tissues and their underlying biological processes. Yet, there is still a lack of tools to provide quantitative and objective characterization of tissues that can aid clinical assessment in vivo to enhance diagnostic and therapeutic interventions. Here, we present a clinically viable fiber-based imaging system combining time-resolved spectrofluorimetry and reflectance spectroscopy to achieve fast multiparametric macroscopic characterization of tissues. An essential feature of the setup is its ability to perform dual wavelength excitation in combination with recording time-resolved fluorescence data in several spectral intervals. Initial validation of this bimodal system was carried out in freshly resected human colorectal cancer specimens, where we demonstrated the ability of the system to differentiate normal from malignant tissues based on their autofluorescence and reflectance properties. To further highlight the complementarity of autofluorescence and reflectance measurements and demonstrate viability in a clinically relevant scenario, we also collected in vivo data from the skin of a volunteer. Altogether, integration of these modalities in a single platform can offer multidimensional characterization of tissues, thus facilitating a deeper understanding of biological processes and potentially advancing diagnostic and therapeutic approaches in various medical applications.
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Affiliation(s)
- Alberto I. Herrando
- Biophotonics Platform, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
- Digestive Unit, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | | | - Antonio Galzerano
- Digestive Unit, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - Laura Fernández
- Digestive Unit, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - Pedro Vieira
- Digestive Unit, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - José Azevedo
- Digestive Unit, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - Amjad Parvaiz
- Digestive Unit, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - Riccardo Cicchi
- National Institute of Optics (CNR-INO), Largo Enrico Fermi 6, 50125 Florence, Italy
| | - Vladislav I. Shcheslavskiy
- Becker and Hickl GmbH, Nunsdorfer Ring 7-9, 12277 Berlin, Germany
- Privolzhsky Research Medical University, Minina and Pozharskogo Sq, 10/1, 603005 Nizhny Novgorod, Russia
| | - Pedro G. Silva
- Biophotonics Platform, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - João L. Lagarto
- Biophotonics Platform, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
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Kasprzycka W, Szumigraj W, Wachulak P, Trafny EA. New approaches for low phototoxicity imaging of living cells and tissues. Bioessays 2024; 46:e2300122. [PMID: 38514402 DOI: 10.1002/bies.202300122] [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: 07/04/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/23/2024]
Abstract
Fluorescence microscopy is a powerful tool used in scientific and medical research, but it is inextricably linked to phototoxicity. Neglecting phototoxicity can lead to erroneous or inconclusive results. Recently, several reports have addressed this issue, but it is still underestimated by many researchers, even though it can lead to cell death. Phototoxicity can be reduced by appropriate microscopic techniques and carefully designed experiments. This review focuses on recent strategies to reduce phototoxicity in microscopic imaging of living cells and tissues. We describe digital image processing and new hardware solutions. We point out new modifications of microscopy methods and hope that this review will interest microscopy hardware engineers. Our aim is to underscore the challenges and potential solutions integral to the design of microscopy systems. Simultaneously, we intend to engage biologists, offering insight into the latest technological advancements in imaging that can enhance their understanding and practice.
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Affiliation(s)
- Wiktoria Kasprzycka
- Biomedical Engineering Centre, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
| | - Wiktoria Szumigraj
- Biomedical Engineering Centre, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
| | - Przemysław Wachulak
- Laser Technology Division, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
| | - Elżbieta Anna Trafny
- Biomedical Engineering Centre, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
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3
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Lin Y, Mos P, Ardelean A, Bruschini C, Charbon E. Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging. Sci Rep 2024; 14:3286. [PMID: 38331957 PMCID: PMC10853568 DOI: 10.1038/s41598-024-52966-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/25/2024] [Indexed: 02/10/2024] Open
Abstract
Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. Inspired by the concept of Edge Artificial Intelligence (Edge AI), we propose a robust approach that enables fast FLI with no degradation of accuracy. This approach couples a recurrent neural network (RNN), which is trained to estimate the fluorescence lifetime directly from raw timestamps without building histograms, to SPAD TCSPC systems, thereby drastically reducing transfer data volumes and hardware resource utilization, and enabling real-time FLI acquisition. We train two variants of the RNN on a synthetic dataset and compare the results to those obtained using center-of-mass method (CMM) and least squares fitting (LS fitting). Results demonstrate that two RNN variants, gated recurrent unit (GRU) and long short-term memory (LSTM), are comparable to CMM and LS fitting in terms of accuracy, while outperforming them in the presence of background noise by a large margin. To explore the ultimate limits of the approach, we derive the Cramer-Rao lower bound of the measurement, showing that RNN yields lifetime estimations with near-optimal precision. To demonstrate real-time operation, we build a FLI microscope based on an existing SPAD TCSPC system comprising a 32[Formula: see text]32 SPAD sensor named Piccolo. Four quantized GRU cores, capable of processing up to 4 million photons per second, are deployed on the Xilinx Kintex-7 FPGA that controls the Piccolo. Powered by the GRU, the FLI setup can retrieve real-time fluorescence lifetime images at up to 10 frames per second. The proposed FLI system is promising and ideally suited for biomedical applications, including biological imaging, biomedical diagnostics, and fluorescence-assisted surgery, etc.
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Affiliation(s)
- Yang Lin
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Neuchâtel, 2002, Switzerland
| | - Paul Mos
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Neuchâtel, 2002, Switzerland
| | - Andrei Ardelean
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Neuchâtel, 2002, Switzerland
| | - Claudio Bruschini
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Neuchâtel, 2002, Switzerland
| | - Edoardo Charbon
- Advanced Quantum Architecture Laboratory, École polytechnique fédérale de Lausanne, Neuchâtel, 2002, Switzerland.
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Gouzou D, Taimori A, Haloubi T, Finlayson N, Wang Q, Hopgood JR, Vallejo M. Applications of machine learning in time-domain fluorescence lifetime imaging: a review. Methods Appl Fluoresc 2024; 12:022001. [PMID: 38055998 PMCID: PMC10851337 DOI: 10.1088/2050-6120/ad12f7] [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: 06/30/2023] [Revised: 09/25/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.
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Affiliation(s)
- Dorian Gouzou
- Dorian Gouzou and Marta Vallejo are with Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
| | - Ali Taimori
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Tarek Haloubi
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Neil Finlayson
- Neil Finlayson is with Institute for Integrated Micro and Nano Systems, School of Engineering, University ofEdinburgh, Edinburgh EH9 3FF, United Kingdom
| | - Qiang Wang
- Qiang Wang is with Centre for Inflammation Research, University of Edinburgh, Edinburgh, EH16 4TJ, United Kingdom
| | - James R Hopgood
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Marta Vallejo
- Dorian Gouzou and Marta Vallejo are with Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
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Huang J, Tang X, Chen Z, Li X, Zhang Y, Huang X, Zhang D, An G, Lee HJ. Rapid azoospermia classification by stimulated Raman scattering and second harmonic generation microscopy. BIOMEDICAL OPTICS EXPRESS 2023; 14:5569-5582. [PMID: 38021145 PMCID: PMC10659792 DOI: 10.1364/boe.501623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023]
Abstract
Disease diagnosis and classification pose significant challenges due to the limited capabilities of traditional methods to obtain molecular information with spatial distribution. Optical imaging techniques, utilizing (auto)fluorescence and nonlinear optical signals, introduce new dimensions for biomarkers exploration that can improve diagnosis and classification. Nevertheless, these signals often cover only a limited number of species, impeding a comprehensive assessment of the tissue microenvironment, which is crucial for effective disease diagnosis and therapy. To address this challenge, we developed a multimodal platform, termed stimulated Raman scattering and second harmonic generation microscopy (SRASH), capable of simultaneously providing both chemical bonds and structural information of tissues. Applying SRASH imaging to azoospermia patient samples, we successfully identified lipids, protein, and collagen contrasts, unveiling molecular and structural signatures for non-obstructive azoospermia. This achievement is facilitated by LiteBlendNet-Dx (LBNet-Dx), our diagnostic algorithm, which achieved an outstanding 100% sample-level accuracy in classifying azoospermia, surpassing conventional imaging modalities. As a label-free technique, SRASH imaging eliminates the requirement for sample pre-treatment, demonstrating great potential for clinical translation and enabling molecular imaging-based diagnosis and therapy.
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Affiliation(s)
- Jie Huang
- Zhejiang Polytechnic Institute, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
- College of Biomedical Engineering & Instrument Science; Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Xiaobin Tang
- Interdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang University; Hangzhou 310027, China
| | - Zhicong Chen
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; The Third Affiliated Hospital of Guangzhou Medical University; Guangzhou 510150, China
| | - Xiaomin Li
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; The Third Affiliated Hospital of Guangzhou Medical University; Guangzhou 510150, China
| | - Yongqing Zhang
- Interdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang University; Hangzhou 310027, China
| | - Xiangjie Huang
- College of Biomedical Engineering & Instrument Science; Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Delong Zhang
- Interdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang University; Hangzhou 310027, China
| | - Geng An
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; The Third Affiliated Hospital of Guangzhou Medical University; Guangzhou 510150, China
| | - Hyeon Jeong Lee
- College of Biomedical Engineering & Instrument Science; Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
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Shcheslavskiy VI, Yuzhakova DV, Sachkova DA, Shirmanova MV, Becker W. Macroscopic temporally and spectrally resolved fluorescence imaging enhanced by laser-wavelength multiplexing. OPTICS LETTERS 2023; 48:5309-5312. [PMID: 37831854 DOI: 10.1364/ol.501923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
We present a laser scanning system for macroscopic samples that records fully resolved decay curves in individual pixels, resolves the images in 16 wavelength channels, and records simultaneously at several laser wavelengths. By using confocal detection, the system delivers images that are virtually free of lateral scattering and out-of-focus haze. Image formats can be up to 256 × 256 pixels and up to 1024 time channels. We demonstrate the performance of the system both on model experiments with fluorescent micro-beads and on the tumor model in the living mice.
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Barroso M, Monaghan MG, Niesner R, Dmitriev RI. Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding. Adv Drug Deliv Rev 2023; 201:115081. [PMID: 37647987 PMCID: PMC10543546 DOI: 10.1016/j.addr.2023.115081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/20/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
Organoid models have been used to address important questions in developmental and cancer biology, tissue repair, advanced modelling of disease and therapies, among other bioengineering applications. Such 3D microenvironmental models can investigate the regulation of cell metabolism, and provide key insights into the mechanisms at the basis of cell growth, differentiation, communication, interactions with the environment and cell death. Their accessibility and complexity, based on 3D spatial and temporal heterogeneity, make organoids suitable for the application of novel, dynamic imaging microscopy methods, such as fluorescence lifetime imaging microscopy (FLIM) and related decay time-assessing readouts. Several biomarkers and assays have been proposed to study cell metabolism by FLIM in various organoid models. Herein, we present an expert-opinion discussion on the principles of FLIM and PLIM, instrumentation and data collection and analysis protocols, and general and emerging biosensor-based approaches, to highlight the pioneering work being performed in this field.
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Affiliation(s)
- Margarida Barroso
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - Michael G Monaghan
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 02, Ireland
| | - Raluca Niesner
- Dynamic and Functional In Vivo Imaging, Freie Universität Berlin and Biophysical Analytics, German Rheumatism Research Center, Berlin, Germany
| | - Ruslan I Dmitriev
- Tissue Engineering and Biomaterials Group, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, C. Heymanslaan 10, 9000 Ghent, Belgium; Ghent Light Microscopy Core, Ghent University, 9000 Ghent, Belgium.
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Scholler J, Mandache D, Mathieu MC, Lakhdar AB, Darche M, Monfort T, Boccara C, Olivo-Marin JC, Grieve K, Meas-Yedid V, la Guillaume EBA, Thouvenin O. Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning. J Med Imaging (Bellingham) 2023; 10:034504. [PMID: 37274760 PMCID: PMC10234284 DOI: 10.1117/1.jmi.10.3.034504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/29/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose The adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration. Approach We designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual 1.3 × 1.3 mm 2 images and compared with standard H&E histology diagnosis. Results Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level (1.3 × 1.3 mm 2 ) and above 96% at the specimen level (above cm 2 ). Conclusions Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.
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Affiliation(s)
- Jules Scholler
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | - Diana Mandache
- AQUYRE Bioscences-LLTech SAS, Paris, France
- Institut Pasteur, Bioimage Analysis Unit, Paris, France
| | - Marie Christine Mathieu
- Gustave Roussy Cancer Campus, Department of Medical Biology and Pathology, Villejuif, France
| | | | - Marie Darche
- Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France
| | - Tual Monfort
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | - Claude Boccara
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | | | - Kate Grieve
- Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France
- Quinze-Vingts National Eye Hospital, Paris, France
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Lim SY, Yoon HM, Kook MC, Jang JI, So PTC, Kang JW, Kim HM. Stomach tissue classification using autofluorescence spectroscopy and machine learning. Surg Endosc 2023:10.1007/s00464-023-10053-6. [PMID: 37055665 DOI: 10.1007/s00464-023-10053-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/26/2023] [Indexed: 04/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Determination of stomach tumor location and invasion depth requires delineation of gastric histological structure, which has hitherto been widely accomplished by histochemical staining. In recent years, alternative histochemical evaluation methods have been pursued to accelerate intraoperative diagnosis, often by bypassing the time-consuming step of dyeing. Owing to strong endogenous signals from coenzymes, metabolites, and proteins, autofluorescence spectroscopy is a favorable candidate technique to achieve this aim. MATERIALS AND METHODS We investigated stomach tissue slices and block specimens using a fast fluorescence imaging scanner. To obtain histological information from broad and structureless fluorescence spectra, we analyzed tens of thousands of spectra with multiple machine-learning algorithms and built a tissue classification model trained with dissected gastric tissues. RESULTS A machine-learning-based spectro-histological model was built based on the autofluorescence spectra measured from stomach tissue samples with delineated and validated histological structures. The scores from a principal components analysis were employed as input features, and prediction accuracy was confirmed to be 92.0%, 90.1%, and 91.4% for mucosa, submucosa, and muscularis propria, respectively. We investigated the tissue samples in both sliced and block forms using a fast fluorescence imaging scanner. CONCLUSION We successfully demonstrated differentiation of multiple tissue layers of well-defined specimens with the guidance of a histologist. Our spectro-histology classification model is applicable to histological prediction for both tissue blocks and slices, even though only sliced samples were trained.
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Affiliation(s)
- Soo Yeong Lim
- Department of Chemistry, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea
| | - Hong Man Yoon
- Division of Convergence Technology, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10408, Republic of Korea
| | - Myeong-Cherl Kook
- Division of Convergence Technology, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10408, Republic of Korea
| | - Jin Il Jang
- Department of Chemistry, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea
| | - Peter T C So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Hyung Min Kim
- Department of Chemistry, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea.
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Zhang M, Liao J, Jia Z, Qin C, Zhang L, Wang H, Liu Y, Jiang C, Han M, Li J, Wang K, Wang X, Bu H, Yao J, Liu Y. High Dynamic Range Dual-Modal White Light Imaging Improves the Accuracy of Tumor Bed Sampling After Neoadjuvant Therapy for Breast Cancer. Am J Clin Pathol 2023; 159:293-303. [PMID: 36799717 DOI: 10.1093/ajcp/aqac167] [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: 10/06/2022] [Accepted: 12/01/2022] [Indexed: 02/18/2023] Open
Abstract
OBJECTIVES Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens. METHODS The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue. RESULTS The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P < .01). CONCLUSIONS HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P < .01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling.
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Affiliation(s)
- Meng Zhang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jun Liao
- AI Lab, Tencent, Shenzhen, China
| | - Zhanli Jia
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | | | - Lingling Zhang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Han Wang
- AI Lab, Tencent, Shenzhen, China
| | - Yao Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | | | - Mengxue Han
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jinze Li
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Kun Wang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xinran Wang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hong Bu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | | | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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Hettie KS, Chin FT. NIRDye 812: A molecular platform tailored for multimodal bioimaging applications of targeted fluorescence- and photoacoustic-guided surgery. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY. B, BIOLOGY 2023; 242:112683. [PMID: 36934549 DOI: 10.1016/j.jphotobiol.2023.112683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 02/16/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023]
Abstract
The primary treatment for malignant tumors remains to be surgical removal of the diseased tissue. The presence or absence of residual diseased tissue at the tumor margin is the strongest predictor of postoperative prognosis and recurrence. Accordingly, reliance on the ability of surgeons to visually distinguish diseased tissue from healthy tissue unambiguously in real time is crucial. Near infrared-I (NIRI) fluorescence-emitting targeting biomolecular constructs such as anticancer antibody-fluorophore conjugates, namely cetuximab-IRDye® 800CW (CTB-IRDye® 800CW), are FDA-approved for clinical trial usage in the fluorescence-guided resection of diseased tissue due to affording improved direct visualization of tumor tissue when compared to the use of either the unaided eye under standard white light illumination (WLI) surgical techniques or non-targeting fluorophores. Unfortunately, though helpful, CTB-IRDye® 800CW affords limited (i) identification of diseased tissue and (ii) tumor margin delineation, because the immunoconjugate generates suboptimal tumor-to-background ratios (TBRs) as a result of its spectral/photophysical profiles poorly aligning with the fixed optical windows of pre-/clinical setups. As such, CTB-IRDye® 800CW is more prone to affording incomplete resection compared to if TBRs were higher due to otherwise. To aid in accurately identifying deep-seated diseased tissue, photoacoustic (PA) tomography has been implemented alongside CTB-IRDye® 800CW to achieve PA signals that could result in higher TBRs. However, in clinical trial practice, using IRDye® 800CW for PA imaging also yields subpar TBRs due to it affording low PA signals. To overcome such limitations, we developed NIRDye 812, a structurally-modified topological equivalent of IRDye® 800CW, to confer it the capability to yield both higher TBRs and superior PA signal than that of the equivalent CTB-conjugate and fluorophore IRDye® 800CW itself, respectively. To do so, we substituted the oxygen atom at its meso-position with a sulfur atom. CTB-NIRDye 812 demonstrated a red-shifted absorption wavelength at 796 nm and a peak NIR-I fluorescence emission wavelength at 820 nm, which better dovetails with the fixed windows of preinstalled fixed emission filters within commercial pre-/clinical NIR-I fluorescence imaging instruments. Overall, CTB-NIRDye 812 provided a ∼ 2-fold increase in TBRs compared to those of CTB-IRDye® 800CW in vivo. Also, NIRDye 812 displayed an ∼60% higher PA signal than that of IRDye® 800CW. Collectively, we achieved our goal of improving upon the spectral/photophysical and PA properties of IRDye® 800CW via introducing a subtle modification to its electronic core such that its CTB immunoconjugate could potentially allow for fast track or breakthrough designation by the FDA due to its near-identical structure displaying considerably improved efficacy.
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Affiliation(s)
- Kenneth S Hettie
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA; Department of Otolaryngology - Head & Neck Surgery, Stanford University, Stanford, CA 94305, USA.
| | - Frederick T Chin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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12
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Schweitzer D, Haueisen J, Klemm M. Suppression of natural lens fluorescence in fundus autofluorescence measurements: review of hardware solutions. BIOMEDICAL OPTICS EXPRESS 2022; 13:5151-5170. [PMID: 36425615 PMCID: PMC9664869 DOI: 10.1364/boe.462559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 06/16/2023]
Abstract
Fluorescence lifetime imaging ophthalmoscopy (FLIO), a technique for investigating metabolic changes in the eye ground, can reveal the first signs of diseases related to metabolism. The fluorescence of the natural lens overlies the fundus fluorescence. Although the influence of natural lens fluorescence can be somewhat decreased with mathematical models, excluding this influence during the measurement by using hardware enables more exact estimation of the fundus fluorescence. Here, we analyze four 1-photon excitation hardware solutions to suppress the influence of natural lens fluorescence: aperture stop separation, confocal scanning laser ophthalmoscopy, combined confocal scanning laser ophthalmoscopy and aperture stop separation, and dual point confocal scanning laser ophthalmoscopy. The effect of each principle is demonstrated in examples. The best suppression is provided by the dual point principle, realized with a confocal scanning laser ophthalmoscope. In this case, in addition to the fluorescence of the whole eye, the fluorescence of the anterior part of the eye is detected from a non-excited spot of the fundus. The intensity and time-resolved fluorescence spectral data of the fundus are derived through the subtraction of the simultaneously measured fluorescence of the excited and non-excited spots. Advantages of future 2-photon fluorescence excitation are also discussed. This study provides the first quantitative evaluation of hardware principles to suppress the fluorescence of the natural lens during measurements of fundus autofluorescence.
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Affiliation(s)
- D. Schweitzer
- Department of Ophthalmology, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - J. Haueisen
- Institute of Biomedical Engineering and Informatics, POB 100565, 98694 Ilmenau, Germany
| | - M. Klemm
- Institute of Biomedical Engineering and Informatics, POB 100565, 98694 Ilmenau, Germany
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13
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Weyers BW, Birkeland AC, Marsden MA, Tam A, Bec J, Frusciante RP, Gui D, Bewley AF, Abouyared M, Marcu L, Farwell DG. Intraoperative delineation of p16+ oropharyngeal carcinoma of unknown primary origin with fluorescence lifetime imaging: Preliminary report. Head Neck 2022; 44:1765-1776. [PMID: 35511208 PMCID: PMC9979707 DOI: 10.1002/hed.27078] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/23/2022] [Accepted: 04/22/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND This study evaluated whether fluorescence lifetime imaging (FLIm), coupled with standard diagnostic workups, could enhance primary lesion detection in patients with p16+ head and neck squamous cell carcinoma of the unknown primary (HNSCCUP). METHODS FLIm was integrated into transoral robotic surgery to acquire optical data on six HNSCCUP patients' oropharyngeal tissues. An additional 55-patient FLIm dataset, comprising conventional primary tumors, trained a machine learning classifier; the output predicted the presence and location of HNSCCUP for the six patients. Validation was performed using histopathology. RESULTS Among the six HNSCCUP patients, p16+ occult primary was surgically identified in three patients, whereas three patients ultimately had no identifiable primary site in the oropharynx. FLIm correctly detected HNSCCUP in all three patients (ROC-AUC: 0.90 ± 0.06), and correctly predicted benign oropharyngeal tissue for the remaining three patients. The mean sensitivity was 95% ± 3.5%, and specificity 89% ± 12.7%. CONCLUSIONS FLIm may be a useful diagnostic adjunct for detecting HNSCCUP.
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Affiliation(s)
- Brent W. Weyers
- Department of Biomedical Engineering, University of California, Davis, Davis, California, USA
| | - Andrew C. Birkeland
- Department of Otolaryngology – Head & Neck Surgery, University of California, Davis, Davis, California, USA
| | - Mark A. Marsden
- Department of Biomedical Engineering, University of California, Davis, Davis, California, USA
| | - Athena Tam
- Department of Biomedical Engineering, University of California, Davis, Davis, California, USA
| | - Julien Bec
- Department of Biomedical Engineering, University of California, Davis, Davis, California, USA
| | - Roberto P. Frusciante
- Department of Biomedical Engineering, University of California, Davis, Davis, California, USA
| | - Dorina Gui
- Department of Pathology and Laboratory Medicine, University of California, Davis, Davis, California, USA
| | - Arnaud F. Bewley
- Department of Otolaryngology – Head & Neck Surgery, University of California, Davis, Davis, California, USA
| | - Marianne Abouyared
- Department of Otolaryngology – Head & Neck Surgery, University of California, Davis, Davis, California, USA
| | - Laura Marcu
- Department of Biomedical Engineering, University of California, Davis, Davis, California, USA
| | - Donald Gregory Farwell
- Department of Otolaryngology – Head & Neck Surgery, University of California, Davis, Davis, California, USA,Department of Otorhinolaryngology – Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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14
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Gonzalez‐Montoro A, Vera‐Donoso CD, Konstantinou G, Sopena P, Martinez M, Ortiz JB, Carles M, Benlloch J, Gonzalez A. Nuclear‐medicine probes: where we are and where we are going. Med Phys 2022; 49:4372-4390. [PMID: 35526220 PMCID: PMC9545507 DOI: 10.1002/mp.15690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/08/2022] [Accepted: 04/26/2022] [Indexed: 11/10/2022] Open
Abstract
Nuclear medicine probes turned into the key for the identification and precise location of sentinel lymph nodes and other occult lesions (i.e., tumors) by using the systemic administration of radiotracers. Intraoperative nuclear probes are key in the surgical management of some malignancies as well as in the determination of positive surgical margins, thus reducing the extent and potential surgery morbidity. Depending on their application, nuclear probes are classified into two main categories, namely, counting and imaging. Although counting probes present a simple design, are handheld (to be moved rapidly), and provide only acoustic signals when detecting radiation, imaging probes, also known as cameras, are more hardware‐complex and also able to provide images but at the cost of an increased intervention time as displacing the camera has to be done slowly. This review article begins with an introductory section to highlight the relevance of nuclear‐based probes and their components as well as the main differences between ionization‐ (semiconductor) and scintillation‐based probes. Then, the most significant performance parameters of the probe are reviewed (i.e., sensitivity, contrast, count rate capabilities, shielding, energy, and spatial resolution), as well as the different types of probes based on the target radiation nature, namely: gamma (γ), beta (β) (positron and electron), and Cherenkov. Various available intraoperative nuclear probes are finally compared in terms of performance to discuss the state‐of‐the‐art of nuclear medicine probes. The manuscript concludes by discussing the ideal probe design and the aspects to be considered when selecting nuclear‐medicine probes.
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Affiliation(s)
- A. Gonzalez‐Montoro
- Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain
| | | | | | - P. Sopena
- Servicio de Medicina Nuclear Área clínica de Imagen Médica, La Fe Hospital Valencia 46026 Spain
| | - M. Martinez
- Urology Department La Fe Hospital Valencia 46026 Spain
| | - J. B. Ortiz
- Urology Department La Fe Hospital Valencia 46026 Spain
| | - M. Carles
- Biomedical Imaging Research Group La Fe Hospital Valencia 46026 Spain
| | - J.M. Benlloch
- Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain
| | - A.J. Gonzalez
- Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain
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15
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Rasmussen SM, Nielsen T, Hager H, Schousboe LP. Spatial analysis of photoplethysmography in cutaneous squamous cell carcinoma. Sci Rep 2022; 12:7318. [PMID: 35513459 PMCID: PMC9072381 DOI: 10.1038/s41598-022-10924-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/30/2022] [Indexed: 11/30/2022] Open
Abstract
The primary treatment of the common malignancy squamous cell carcinoma is surgical removal. In this process, sufficient tissue removal is balanced against unnecessary mutilation. We recently presented a remote photoplethysmography algorithm, which revealed significant differences between processed video recordings of cancer biopsy areas and surrounding tissue. The aim of this study was to investigate whether spatial analyses of photoplethysmography data correlate with post-excision pathological analyses and thus have potential to assist in tumour delineation. Based on high speed video recordings of 11 patients with squamous cell carcinoma, we examined different parameters derived from temporal remote photoplethysmography variations. Signal characteristics values in sites matching histological sections were compared with pathological measures. Values were ranked and statistically tested with a Kendall correlation analysis. A moderate, negative correlation was found between signal oscillations and the width and transversal area of squamous cell carcinoma in the frequencies below 1 Hz and specifically from 0.02 to 0.15 Hz. We have presented a correlation between frequency content and prevalence of cancer based on regular video recordings of squamous cell carcinoma. We believe this is supported by published findings on malignant melanoma. Our findings indicate that photoplethysmography can be used to distinguish SCC from healthy skin.
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Affiliation(s)
| | - Thomas Nielsen
- Department of Electrical and Computer Engineering, Aarhus University, 8000, Aarhus N, Denmark
| | - Henrik Hager
- Department of Clinical Pathology, Vejle Hospital, 7100, Vejle, Denmark
- Department of Regional Health Research, University of Southern Denmark, 5000, Odense, Denmark
| | - Lars Peter Schousboe
- Department of Otolaryngology, Southdanish University Hospital, 7100, Vejle, Denmark
- Department of Regional Health Research, University of Southern Denmark, 5000, Odense, Denmark
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16
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Dolganova IN, Varvina DA, Shikunova IA, Alekseeva AI, Karalkin PA, Kuznetsov MR, Nikitin PV, Zotov AK, Mukhina EE, Katyba GM, Zaytsev KI, Tuchin VV, Kurlov VN. Proof of concept for the sapphire scalpel combining tissue dissection and optical diagnosis. Lasers Surg Med 2021; 54:611-622. [PMID: 34918347 DOI: 10.1002/lsm.23509] [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: 07/13/2021] [Revised: 10/18/2021] [Accepted: 11/27/2021] [Indexed: 11/08/2022]
Abstract
OBJECTIVES The development of compact diagnostic probes and instruments with an ability to direct access to organs and tissues and integration of these instruments into surgical workflows is an important task of modern physics and medicine. The need for such tools is essential for surgical oncology, where intraoperative visualization and demarcation of tumor margins define further prognosis and survival of patients. In this paper, the possible solution for this intraoperative imaging problem is proposed and its feasibility to detect tumorous tissue is studied experimentally. METHODS For this aim, the sapphire scalpel was developed and fabricated using the edge-defined film-fed growth technique aided by mechanical grinding, polishing, and chemical sharpening of the cutting edge. It possesses optical transparency, mechanical strength, chemical inertness, and thermal resistance alongside the presence of the as-grown hollow capillary channels in its volume for accommodating optical fibers. The rounding of the cutting edge exceeds the same for metal scalpels and can be as small as 110 nm. Thanks to these features, sapphire scalpel combines tissue dissection with light delivering and optical diagnosis. The feasibility for the tumor margin detection was studied, including both gelatin-based tissue phantoms and ex vivo freshly excised specimens of the basal cell carcinoma from humans and the glioma model 101.8 from rats. These tumors are commonly diagnosed either non-invasively or intraoperatively using different modalities of fluorescence spectroscopy and imaging, which makes them ideal candidates for our feasibility test. For this purpose, fiber-based spectroscopic measurements of the backscattered laser radiation and the fluorescence signals were carried out in the visible range. RESULTS Experimental studies show the feasibility of the proposed sapphire scalpel to provide a 2-mm-resolution of the tumor margins' detection, along with an ability to distinguish the tumor invasion region, which results from analysis of the backscattered optical fields and the endogenous or exogenous fluorescence data. CONCLUSIONS Our findings justified a strong potential of the sapphire scalpel for surgical oncology. However, further research and engineering efforts are required to optimize the sapphire scalpel geometry and the optical diagnosis protocols to meet the requirements of oncosurgery, including diagnosis and resection of neoplasms with different localizations and nosologies.
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Affiliation(s)
- Irina N Dolganova
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka, Russia.,Institute for Regenerative Medicine, Sechenov University, Moscow, Russia.,Bauman Moscow State Technical University, Moscow, Russia
| | - Daria A Varvina
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia.,International School "Medicine of the Future", Sechenov University, Moscow, Russia
| | - Irina A Shikunova
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka, Russia
| | - Anna I Alekseeva
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia.,Research Institute of Human Morphology, Moscow, Russia
| | - Pavel A Karalkin
- Institute for Cluster Oncology, Sechenov University, Moscow, Russia.,Hertsen Moscow Oncology Research Institute, National Medical Research Radiological Centre, Moscow, Russia
| | | | - Pavel V Nikitin
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia
| | - Arsen K Zotov
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka, Russia.,Bauman Moscow State Technical University, Moscow, Russia.,Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
| | | | - Gleb M Katyba
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka, Russia.,Bauman Moscow State Technical University, Moscow, Russia.,Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
| | - Kirill I Zaytsev
- Institute for Regenerative Medicine, Sechenov University, Moscow, Russia.,Bauman Moscow State Technical University, Moscow, Russia.,Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
| | - Valery V Tuchin
- Science Medical Center, Saratov State University, Saratov, Russia.,Institute of Precision Mechanics and Control of the Russian Academy of Sciences, Saratov, Russia.,National Research Tomsk University, Tomsk, Russia
| | - Vladimir N Kurlov
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka, Russia.,Institute for Regenerative Medicine, Sechenov University, Moscow, Russia.,Bauman Moscow State Technical University, Moscow, Russia
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17
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Single-Fiber Diffuse Reflectance Spectroscopy and Spatial Frequency Domain Imaging in Surgery Guidance: A Study on Optical Phantoms. MATERIALS 2021; 14:ma14247502. [PMID: 34947102 PMCID: PMC8708622 DOI: 10.3390/ma14247502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/06/2021] [Accepted: 12/03/2021] [Indexed: 11/25/2022]
Abstract
Diffuse reflectance spectroscopy (DRS) and imaging are increasingly being used in surgical guidance for tumor margin detection during endoscopic operations. However, the accuracy of the boundary detection with optical techniques may depend on the acquisition parameters, and its evaluation is in high demand. In this work, using optical phantoms with homogeneous and heterogeneous distribution of chromophores mimicking normal and pathological bladder tissues, the accuracy of tumor margin detection using single-fiber diffuse reflectance spectroscopy and spatial frequency domain imaging was evaluated. We also showed how the diffuse reflectance response obtained at different spatial frequencies with the spatial frequency domain imaging technique could be used not only to quantitatively map absorption and scattering coefficients of normal tissues and tumor-like heterogeneities but also to estimate the tumor depth localization. The demonstrated results could be helpful for proper analysis of the DRS data measured in vivo and for translation of optical techniques for tumor margin detection to clinics.
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18
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Mitrou A, Feng X, Khan A, Yaroslavsky AN. Feasibility of dual-contrast fluorescence imaging of pathological breast tissues. JOURNAL OF BIOPHOTONICS 2021; 14:e202100007. [PMID: 34010507 DOI: 10.1002/jbio.202100007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/23/2021] [Accepted: 05/18/2021] [Indexed: 06/12/2023]
Abstract
The combination of intravital dye, methylene blue (MB), with molecular cancer marker, pH low insertion peptide (pHLIP) conjugated with fluorescent Alexa532 (Alexa532-pHLIP), was evaluated for enhancing contrast of pathological breast tissue ex vivo. Fresh, thick breast specimens were stained sequentially with Alexa532-pHLIP and aqueous MB and imaged using dual-channel fluorescence microscopy. MB and Alexa532-pHLIP accumulated in the nuclei and cytoplasm of cancer cells, respectively. MB also stained nuclei of normal cells. Some Alexa532-pHLIP fluorescence emission was detected from connective tissue and benign cell membranes. Overall, Alexa532-pHLIP showed high affinity to cancer, while MB highlighted tissue morphology. The results indicate that MB and Alexa532-pHLIP provide complementary information and show promise for the detection of breast cancer.
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Affiliation(s)
- Androniki Mitrou
- Advanced Biophotonics Laboratory, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Xin Feng
- Advanced Biophotonics Laboratory, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Ashraf Khan
- Department of Pathology, University of Massachusetts Medical School-Baystate, Springfield, Massachusetts, USA
| | - Anna N Yaroslavsky
- Advanced Biophotonics Laboratory, University of Massachusetts Lowell, Lowell, Massachusetts, USA
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19
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Alfonso-Garcia A, Bec J, Weyers B, Marsden M, Zhou X, Li C, Marcu L. Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions. JOURNAL OF BIOPHOTONICS 2021; 14:e202000472. [PMID: 33710785 PMCID: PMC8579869 DOI: 10.1002/jbio.202000472] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/03/2021] [Accepted: 03/05/2021] [Indexed: 05/16/2023]
Abstract
Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.
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Affiliation(s)
- Alba Alfonso-Garcia
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Julien Bec
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Brent Weyers
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Mark Marsden
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Xiangnan Zhou
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Cai Li
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Laura Marcu
- Department of Biomedical Engineering, University of California, Davis, Davis, California
- Department Neurological Surgery, University of California, Davis, California
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20
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Yin L, Cao Z, Wang K, Tian J, Yang X, Zhang J. A review of the application of machine learning in molecular imaging. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:825. [PMID: 34268438 PMCID: PMC8246214 DOI: 10.21037/atm-20-5877] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/02/2020] [Indexed: 12/12/2022]
Abstract
Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.
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Affiliation(s)
- Lin Yin
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Zhen Cao
- Peking University First Hospital, Beijing, China
| | - Kun Wang
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Xing Yang
- Peking University First Hospital, Beijing, China
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21
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Marsden M, Weaver SS, Marcu L, Campbell MJ. Intraoperative Mapping of Parathyroid Glands Using Fluorescence Lifetime Imaging. J Surg Res 2021; 265:42-48. [PMID: 33878575 DOI: 10.1016/j.jss.2021.03.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/29/2021] [Accepted: 03/03/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Hypoparathyroidism is a common complication following thyroidectomy. There is a need for technology to aid surgeons in identifying the parathyroid glands. In contrast to near infrared technologies, fluorescence lifetime imaging (FLIm) is not affected by ambient light and may be valuable in identifying parathyroid tissue, but has never been evaluated in this capacity. METHODS We used FLIm to measure the UV induced (355 nm) time-resolved autofluorescence signatures (average lifetimes in 3 spectral emission channels) of thyroid, parathyroid, lymphoid and adipose tissue in 21 patients undergoing thyroid and parathyroid surgery. The Mann-Whitney U test was used to assess the ability of FLIm to discriminate normocellular parathyroid from each of the other tissues. Various machine learning classifiers (random forests, neural network, support vector machine) were then evaluated to recognize parathyroid through a leave-one-out cross-validation. RESULTS Statistically significant differences in average lifetime were observed between parathyroid and each of the other tissue types in spectral channels 2 and 3 respectively. The largest change was observed between adipose tissue and parathyroid (P < 0.001), while less pronounced but still significant changes were observed when comparing parathyroid with lymphoid tissue (P < 0.05) and thyroid (P < 0.01). A random forest classifier trained on average lifetimes was found to detect parathyroid tissue with 100% sensitivity and 93% specificity at the acquisition run level. CONCLUSION We found that FLIm derived parameters can distinguish the parathyroid glands and other adjacent tissue types and has promise in scanning the surgical field to identify parathyroid tissue in real-time.
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Affiliation(s)
- Mark Marsden
- University of California, Davis Department of Biomedical Engineering, Sacramento, California
| | | | - Laura Marcu
- University of California, Davis Department of Biomedical Engineering, Sacramento, California
| | - Michael J Campbell
- University of California, Davis Department of Surgery, Sacramento, California.
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22
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Marsden M, Weyers BW, Bec J, Sun T, Gandour-Edwards RF, Birkeland AC, Abouyared M, Bewley AF, Farwell DG, Marcu L. Intraoperative Margin Assessment in Oral and Oropharyngeal Cancer Using Label-Free Fluorescence Lifetime Imaging and Machine Learning. IEEE Trans Biomed Eng 2021; 68:857-868. [PMID: 32746066 PMCID: PMC8960054 DOI: 10.1109/tbme.2020.3010480] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
OBJECTIVE To demonstrate the diagnostic ability of label-free, point-scanning, fiber-based Fluorescence Lifetime Imaging (FLIm) as a means of intraoperative guidance during oral and oropharyngeal cancer removal surgery. METHODS FLIm point-measurements acquired from 53 patients (n = 67893 pre-resection in vivo, n = 89695 post-resection ex vivo) undergoing oral or oropharyngeal cancer removal surgery were used for analysis. Discrimination of healthy tissue and cancer was investigated using various FLIm-derived parameter sets and classifiers (Support Vector Machine, Random Forests, CNN). Classifier output for the acquired set of point-measurements was visualized through an interpolation-based approach to generate a probabilistic heatmap of cancer within the surgical field. Classifier output for dysplasia at the resection margins was also investigated. RESULTS Statistically significant change (P 0.01) between healthy and cancer was observed in vivo for the acquired FLIm signal parameters (e.g., average lifetime) linked with metabolic activity. Superior classification was achieved at the tissue region level using the Random Forests method (ROC-AUC: 0.88). Classifier output for dysplasia (% probability of cancer) was observed to lie between that of cancer and healthy tissue, highlighting FLIm's ability to distinguish various conditions. CONCLUSION The developed approach demonstrates the potential of FLIm for fast, reliable intraoperative margin assessment without the need for contrast agents. SIGNIFICANCE Fiber-based FLIm has the potential to be used as a diagnostic tool during cancer resection surgery, including Transoral Robotic Surgery (TORS), helping ensure complete resections and improve the survival rate of oral and oropharyngeal cancer patients.
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Bu H. [New Trends of Development in Precision Pathological Diagnosis Promoted by Artificial Intelligence]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2021; 52:153-155. [PMID: 33829683 PMCID: PMC10408910 DOI: 10.12182/20210360206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Indexed: 02/05/2023]
Abstract
Precision pathological diagnosis plays a vital role in precision medicine. Both the limited resources available to pathologists and the incessant demands for further refinement and quantification of clinical diagnosis are posing new challenges for pathologists to meet the needs for precision pathological diagnosis. It is expected that artificial intelligence (AI) will be the powerful tool that will help find solutions to this problem from different angles. The author of this article elaborated on a number of ways in which AI can help promote precision pathological diagnosis, including AI-assisted precision extraction of tissue samples, AI-assisted precision histopathologic diagnosis, AI-assisted histological grading and quantitative scoring, AI-assisted precision assessment of tumor biomarkers, AI-assisted prediction of molecular features and precision interpretation of biological information based on hematoxylin-eosin (HE) stained images, the realization of in-depth precision diagnosis based on AI-assisted information integration, and AI-assisted accurate prediction of patient survival and prognosis based on HE-stained images. The paper presents to the readers the future of smart pathology that AI will help usher in.
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Affiliation(s)
- Hong Bu
- Institute of Clinical Pathology/Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, China
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Liu K, Lin S, Zhu S, Chen Y, Yin H, Li Z, Chen Z. Hyperspectral microscopy combined with DAPI staining for the identification of hepatic carcinoma cells. BIOMEDICAL OPTICS EXPRESS 2021; 12:173-180. [PMID: 33659073 PMCID: PMC7899502 DOI: 10.1364/boe.412158] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/29/2020] [Accepted: 11/30/2020] [Indexed: 06/12/2023]
Abstract
In this study, the DAPI staining is firstly reported for use in the identification of hepatic carcinoma cells based on hyperspectral microscopy. Nuclei in cancer cells usually contain more aneuploidies than that in normal cells, leading to the change of DNA content. Here, we stain hepatic carcinoma tissues and normal hepatic tissues with 4',6-diamidino-2-phenylindole (DAPI) which is sensitive to the DNA content as a fluorochrome binds to DNA. Consequently, the difference in DNA content between hepatic carcinoma cells and normal hepatic cells can be identified by the fluorescent spectral characteristics. Harnessing the hyperspectral microscopy, we find that the fluorescent properties of these two kinds of cells are different not only in the intensity but also in the spectral shape. These properties are exploited to train a support vector machine (SVM) model for classifying cells. The results show that the sensitivity and specificity for the identification of 1000 hepatic carcinoma samples are 99.3% and 99.1%, respectively.
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Affiliation(s)
- Kunxing Liu
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Sifan Lin
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Siqi Zhu
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou 510632, China
| | - Yao Chen
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Hao Yin
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Zhen Li
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou 510632, China
| | - Zhenqiang Chen
- Guangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou 510632, China
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Hettie KS, Teraphongphom NT, Ertsey R, Chin FT. Off-Peak Near-Infrared-II (NIR-II) Bioimaging of an Immunoconjugate Having Peak Fluorescence Emission in the NIR-I Spectral Region for Improving Tumor Margin Delineation. ACS APPLIED BIO MATERIALS 2020; 3:8658-8666. [PMID: 35019636 PMCID: PMC9826717 DOI: 10.1021/acsabm.0c01050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The primary treatment for malignant tumors remains to be resection. The strongest predictor of recurrence and postoperative prognosis is whether diseased tissue/cells remain(s) at the surgical margin. Cancer surgery entails surgeons having the capability to visually distinguish between subtle shades of color in attempts of differentiating between diseased tissue and healthy tissue under standard white-light illumination, as such tissue states appear identical at the meso-/macroscopic level. Accordingly, enhancing the capability of surgeons to do so such that they can accurately delineate the tumor margin is of paramount importance. Fluorescence-guided surgery facilitates in enhancing such capability by color-coding the surgical field with overlaid contrasting pseudo-colors from real-time intraoperative fluorescence emission via utilizing fluorescent constructs in tandem. Constructs undergoing clinical trials or that are FDA-approved provide peak fluorescence emission in the visible (405 - 700 nm) or near-infrared-I (NIR-I) spectral region (700-900 nm), whereby differentiation between tissue states progressively improves in sync with using constructs that emit longer wavelengths of light. Here, we repurpose the usage of such fluorescent constructs by establishing feasibility of a tumor-targeting immunoconjugate (cetuximab-IRDye800) having peak fluorescence emission at the NIR-I spectral region to provide improved tumor margin delineation by affording higher tumor-to-background ratios (TBRs) when measuring its off-peak fluorescence emission at the near-infrared-II (NIR-II) spectral region (1000-1700 nm) in in vivo applications. We prepared murine tumor models, administered such immunoconjugate, and imaged such models pre-/post-administration via utilizing imaging systems that separately afforded acquisition of fluorescence emission in the NIR-I or NIR-II spectral region. On doing so, we determined in vivo TBRs, ex vivo TBRs with/-out skin, and ex vivo biodistribution, all via measuring the fluorescence emission of the immunoconjugate at tumor site(s) at both spectral regions. Collectively, we established feasibility of using the immunoconjugate to afford improved tumor margin delineation by providing 2-fold higher TBRs via utilizing the NIR-II spectral region to capture off-peak fluorescence emission from a fluorescent construct having NIR-I peak fluorescence emission.
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Affiliation(s)
- Kenneth S. Hettie
- Department of Radiology, Stanford University School of Medicine, Stanford, California 94305, United States; Department of Otolaryngology - Head & Neck Surgery, Stanford University, Stanford, California 94305, United States
| | - Nutte Tarn Teraphongphom
- Department of Otolaryngology - Head & Neck Surgery, Stanford University, Stanford, California 94305, United States
| | - Robert Ertsey
- Department of Otolaryngology - Head & Neck Surgery, Stanford University, Stanford, California 94305, United States
| | - Frederick T. Chin
- Department of Radiology, Stanford University School of Medicine, Stanford, California 94305, United States
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Polesie S, McKee PH, Gardner JM, Gillstedt M, Siarov J, Neittaanmäki N, Paoli J. Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey. Front Med (Lausanne) 2020; 7:591952. [PMID: 33195357 PMCID: PMC7606983 DOI: 10.3389/fmed.2020.591952] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 09/21/2020] [Indexed: 12/11/2022] Open
Abstract
Background: Artificial intelligence (AI) has recently surfaced as a research topic in dermatology and dermatopathology. In a recent survey, dermatologists were overall positive toward a development with an increased use of AI, but little is known about the corresponding attitudes among pathologists working with dermatopathology. The objective of this investigation was to make an inventory of these attitudes. Participants and Methods: An anonymous and voluntary online survey was prepared and distributed to pathologists who regularly analyzed dermatopathology slides/images. The survey consisted of 39 question divided in five sections; (1) AI as a topic in pathology; (2) previous exposure to AI as a topic in general; (3) applications for AI in dermatopathology; (4) feelings and attitudes toward AI and (5) self-reported tech-savviness and demographics. The survey opened on March 13, 2020 and closed on May 5, 2020. Results: Overall, 718 responders (64.1% females) representing 91 countries were analyzed. While 81.5% of responders were aware of AI as an emerging topic in pathology, only 18.8% had either good or excellent knowledge about AI. In terms of diagnosis classification, 42.6% saw strong or very strong potential for automated suggestion of skin tumor diagnoses. The corresponding figure for inflammatory skin diseases was 23.0% (Padj < 0.0001). For specific applications, the highest potential was considered for automated detection of mitosis (79.2%), automated suggestion of tumor margins (62.1%) and immunostaining evaluation (62.7%). The potential for automated suggestion of immunostaining (37.6%) and genetic panels (48.3%) were lower. Age did not impact the overall attitudes toward AI. Only 6.0% of the responders agreed or strongly agreed that the human pathologist will be replaced by AI in the foreseeable future. For the entire group, 72.3% agreed or strongly agreed that AI will improve dermatopathology and 84.1% thought that AI should be a part of medical training. Conclusions: Pathologists are generally optimistic about the impact and potential benefit of AI in dermatopathology. The highest potential is expected for narrow specified tasks rather than a global automated suggestion of diagnoses. There is a strong need for education about AI and its use within dermatopathology.
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Affiliation(s)
- Sam Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
| | | | - Jerad M Gardner
- Department of Laboratory Medicine, Geisinger Medical Center, Danville, PA, United States
| | - Martin Gillstedt
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
| | - Jan Siarov
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden.,Department of Pathology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Noora Neittaanmäki
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden.,Department of Pathology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
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27
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Polesie S, McKee PH, Gardner JM, Gillstedt M, Siarov J, Neittaanmäki N, Paoli J. Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey. Front Med (Lausanne) 2020. [DOI: 10.3389/fmed.2020.591952 33195357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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28
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Sagar MAK, Cheng KP, Ouellette JN, Williams JC, Watters JJ, Eliceiri KW. Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia. Front Neurosci 2020; 14:931. [PMID: 33013309 PMCID: PMC7497798 DOI: 10.3389/fnins.2020.00931] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 08/11/2020] [Indexed: 12/22/2022] Open
Abstract
Automated computational analysis techniques utilizing machine learning have been demonstrated to be able to extract more data from different imaging modalities compared to traditional analysis techniques. One new approach is to use machine learning techniques to existing multiphoton imaging modalities to better interpret intrinsically fluorescent cellular signals to characterize different cell types. Fluorescence Lifetime Imaging Microscopy (FLIM) is a high-resolution quantitative imaging tool that can detect metabolic cellular signatures based on the lifetime variations of intrinsically fluorescent metabolic co-factors such as nicotinamide adenine dinucleotide [NAD(P)H]. NAD(P)H lifetime-based discrimination techniques have previously been used to develop metabolic cell signatures for diverse cell types including immune cells such as macrophages. However, FLIM could be even more effective in characterizing cell types if machine learning was used to classify cells by utilizing FLIM parameters for classification. Here, we demonstrate the potential for FLIM-based, label-free NAD(P)H imaging to distinguish different cell types using Artificial Neural Network (ANN)-based machine learning. For our biological use case, we used the challenge of differentiating microglia from other glia cell types in the brain. Microglia are the resident macrophages of the brain and spinal cord and play a critical role in maintaining the neural environment and responding to injury. Microglia are challenging to identify as most fluorescent labeling approaches cross-react with other immune cell types, are often insensitive to activation state, and require the use of multiple specialized antibody labels. Furthermore, the use of these extrinsic antibody labels prevents application in in vivo animal models and possible future clinical adaptations such as neurodegenerative pathologies. With the ANN-based NAD(P)H FLIM analysis approach, we found that microglia in cell culture mixed with other glial cells can be identified with more than 0.9 True Positive Rate (TPR). We also extended our approach to identify microglia in fixed brain tissue with a TPR of 0.79. In both cases the False Discovery Rate was around 30%. This method can be further extended to potentially study and better understand microglia’s role in neurodegenerative disease with improved detection accuracy.
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Affiliation(s)
- Md Abdul Kader Sagar
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin P Cheng
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Jonathan N Ouellette
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States
| | - Justin C Williams
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Jyoti J Watters
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin W Eliceiri
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States.,Morgridge Institute for Research, Madison, WI, United States
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Zaffino P, Moccia S, De Momi E, Spadea MF. A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future. Ann Biomed Eng 2020; 48:2171-2191. [PMID: 32601951 DOI: 10.1007/s10439-020-02553-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 06/17/2020] [Indexed: 12/19/2022]
Abstract
With the advent of Minimally Invasive Surgery (MIS), intra-operative imaging has become crucial for surgery and therapy guidance, allowing to partially compensate for the lack of information typical of MIS. This paper reviews the advancements in both classical (i.e. ultrasounds, X-ray, optical coherence tomography and magnetic resonance imaging) and more recent (i.e. multispectral, photoacoustic and Raman imaging) intra-operative imaging modalities. Each imaging modality was analyzed, focusing on benefits and disadvantages in terms of compatibility with the operating room, costs, acquisition time and image characteristics. Tables are included to summarize this information. New generation of hybrid surgical room and algorithms for real time/in room image processing were also investigated. Each imaging modality has its own (site- and procedure-specific) peculiarities in terms of spatial and temporal resolution, field of view and contrasted tissues. Besides the benefits that each technique offers for guidance, considerations about operators and patient risk, costs, and extra time required for surgical procedures have to be considered. The current trend is to equip surgical rooms with multimodal imaging systems, so as to integrate multiple information for real-time data extraction and computer-assisted processing. The future of surgery is to enhance surgeons eye to minimize intra- and after-surgery adverse events and provide surgeons with all possible support to objectify and optimize the care-delivery process.
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Affiliation(s)
- Paolo Zaffino
- Department of Experimental and Clinical Medicine, Universitá della Magna Graecia, Catanzaro, Italy
| | - Sara Moccia
- Department of Information Engineering (DII), Universitá Politecnica delle Marche, via Brecce Bianche, 12, 60131, Ancona, AN, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milano, MI, Italy
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Universitá della Magna Graecia, Catanzaro, Italy
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