1
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Kendall WY, Tian Q, Zhao S, Mirminachi S, O'Kane E, Joseph A, Dufault D, Miller DA, Shi C, Roper J, Wax A. Deep learning classification of ex vivo human colon tissues using spectroscopic optical coherence tomography. JOURNAL OF BIOPHOTONICS 2024:e202400082. [PMID: 38955358 DOI: 10.1002/jbio.202400082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 07/04/2024]
Abstract
Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.
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Affiliation(s)
- Wesley Y Kendall
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Qinyi Tian
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Shi Zhao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Seyedbabak Mirminachi
- Division of Gastroenterology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Erin O'Kane
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Abel Joseph
- Division of Gastroenterology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Darin Dufault
- Division of Gastroenterology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - David A Miller
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Chanjuan Shi
- Department of Pathology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jatin Roper
- Division of Gastroenterology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Cell Biology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Adam Wax
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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2
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Chang S, Krzyzanowska H, Bowden AK. Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2024; 17:289-311. [PMID: 38424030 DOI: 10.1146/annurev-anchem-061622-014208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy.
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Affiliation(s)
- Shuang Chang
- 1Vanderbilt Biophotonics Center, Vanderbilt University, Nashville, Tennessee, USA;
- 2Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Halina Krzyzanowska
- 1Vanderbilt Biophotonics Center, Vanderbilt University, Nashville, Tennessee, USA;
- 2Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Audrey K Bowden
- 1Vanderbilt Biophotonics Center, Vanderbilt University, Nashville, Tennessee, USA;
- 2Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- 3Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
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3
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Plekhanov AA, Kozlov DS, Shepeleva AA, Kiseleva EB, Shimolina LE, Druzhkova IN, Plekhanova MA, Karabut MM, Gubarkova EV, Gavrina AI, Krylov DP, Sovetsky AA, Gamayunov SV, Kuznetsova DS, Zaitsev VY, Sirotkina MA, Gladkova ND. Tissue Elasticity as a Diagnostic Marker of Molecular Mutations in Morphologically Heterogeneous Colorectal Cancer. Int J Mol Sci 2024; 25:5337. [PMID: 38791375 PMCID: PMC11120711 DOI: 10.3390/ijms25105337] [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: 03/20/2024] [Revised: 04/25/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024] Open
Abstract
The presence of molecular mutations in colorectal cancer (CRC) is a decisive factor in selecting the most effective first-line therapy. However, molecular analysis is routinely performed only in a limited number of patients with remote metastases. We propose to use tissue stiffness as a marker of the presence of molecular mutations in CRC samples. For this purpose, we applied compression optical coherence elastography (C-OCE) to calculate stiffness values in regions corresponding to specific CRC morphological patterns (n = 54). In parallel to estimating stiffness, molecular analysis from the same zones was performed to establish their relationships. As a result, a high correlation between the presence of KRAS/NRAS/BRAF driver mutations and high stiffness values was revealed regardless of CRC morphological pattern type. Further, we proposed threshold stiffness values for label-free targeted detection of molecular alterations in CRC tissues: for KRAS, NRAS, or BRAF driver mutation-above 803 kPa (sensitivity-91%; specificity-80%; diagnostic accuracy-85%), and only for KRAS driver mutation-above 850 kPa (sensitivity-90%; specificity-88%; diagnostic accuracy-89%). To conclude, C-OCE estimation of tissue stiffness can be used as a clinical diagnostic tool for preliminary screening of genetic burden in CRC tissues.
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Affiliation(s)
- Anton A. Plekhanov
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Dmitry S. Kozlov
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Anastasia A. Shepeleva
- Nizhny Novgorod Regional Oncologic Hospital, 11/1 Delovaya St., 603126 Nizhny Novgorod, Russia
| | - Elena B. Kiseleva
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Liubov E. Shimolina
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Irina N. Druzhkova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Maria A. Plekhanova
- Nizhny Novgorod Regional Oncologic Hospital, 11/1 Delovaya St., 603126 Nizhny Novgorod, Russia
- Nizhny Novgorod City Polyclinic #1, 5 Marshala Zhukova Sq., 603107 Nizhny Novgorod, Russia
| | - Maria M. Karabut
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Ekaterina V. Gubarkova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Alena I. Gavrina
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Dmitry P. Krylov
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Alexander A. Sovetsky
- Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanova St., 603950 Nizhny Novgorod, Russia
| | - Sergey V. Gamayunov
- Nizhny Novgorod Regional Oncologic Hospital, 11/1 Delovaya St., 603126 Nizhny Novgorod, Russia
| | - Daria S. Kuznetsova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Vladimir Y. Zaitsev
- Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanova St., 603950 Nizhny Novgorod, Russia
| | - Marina A. Sirotkina
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
| | - Natalia D. Gladkova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Sq., 603950 Nizhny Novgorod, Russia
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4
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Schulte B, Göb M, Singh AP, Lotz S, Draxinger W, Heimke M, Pieper M, Heinze T, Wedel T, Rahlves M, Huber R, Ellrichmann M. High-resolution rectoscopy using MHz optical coherence tomography: a step towards real time 3D endoscopy. Sci Rep 2024; 14:4672. [PMID: 38409328 PMCID: PMC10897148 DOI: 10.1038/s41598-024-55338-5] [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: 12/06/2023] [Accepted: 02/22/2024] [Indexed: 02/28/2024] Open
Abstract
Colonoscopy and endoscopic ultrasound play pivotal roles in the assessment of rectal diseases, especially rectal cancer and inflammatory bowel diseases. Optical coherence tomography (OCT) offers a superior depth resolution, which is a critical factor for individualizing the therapeutic concept and evaluating the therapy response. We developed two distinct rectoscope prototypes, which were integrated into a 1300 nm MHz-OCT system constructed at our facility. The rapid rotation of the distal scanning probe at 40,000 revolutions per minute facilitates a 667 Hz OCT frame rate, enabling real-time endoscopic imaging of large areas. The performance of these OCT-rectoscopes was assessed in an ex vivo porcine colon and a post mortem human in-situ colon. The OCT-rectoscope consistently distinguished various layers of the intestinal wall, identified gut-associated lymphatic tissue, and visualized a rectal polyp during the imaging procedure with 3D-reconstruction in real time. Subsequent histological examination confirmed these findings. The body donor was preserved using an ethanol-glycerol-lysoformin-based technique for true-to-life tissue consistency. We could demonstrate that the novel MHZ-OCT-rectoscope effectively discriminates rectal wall layers and crucial tissue characteristics in a post mortem human colon in-situ. This real-time-3D-OCT holds promise as a valuable future diagnostic tool for assessing disease state and therapy response on-site in rectal diseases.
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Affiliation(s)
- Berenice Schulte
- Interdisciplinary Endoscopy, Medical Department 1, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Madita Göb
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | | | - Simon Lotz
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | | | - Marvin Heimke
- Center of Clinical Anatomy, Institute of Anatomy, Christian-Albrechts University Kiel, Kiel, Germany
| | - Mario Pieper
- Institute of Anatomy, University of Luebeck, Luebeck, Germany
- Airway Research Center North (ARCN), German Center for Lung Research (DZL), Luebeck, Germany
| | - Tillmann Heinze
- Center of Clinical Anatomy, Institute of Anatomy, Christian-Albrechts University Kiel, Kiel, Germany
| | - Thilo Wedel
- Center of Clinical Anatomy, Institute of Anatomy, Christian-Albrechts University Kiel, Kiel, Germany
| | - Maik Rahlves
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | - Robert Huber
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | - Mark Ellrichmann
- Interdisciplinary Endoscopy, Medical Department 1, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
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5
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Singh AP, Göb M, Ahrens M, Eixmann T, Schulte B, Schulz-Hildebrandt H, Hüttmann G, Ellrichmann M, Huber R, Rahlves M. Virtual Hall sensor triggered multi-MHz endoscopic OCT imaging for stable real-time visualization. OPTICS EXPRESS 2024; 32:5809-5825. [PMID: 38439298 DOI: 10.1364/oe.514636] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/18/2024] [Indexed: 03/06/2024]
Abstract
Circumferential scanning in endoscopic imaging is crucial across various disciplines, and optical coherence tomography (OCT) is often the preferred choice due to its high-speed, high-resolution, and micron-scale imaging capabilities. Moreover, real-time and high-speed 3D endoscopy is a pivotal technology for medical screening and precise surgical guidance, among other applications. However, challenges such as image jitter and non-uniform rotational distortion (NURD) are persistent obstacles that hinder real-time visualization during high-speed OCT procedures. To address this issue, we developed an innovative, low-cost endoscope that employs a brushless DC motor for scanning, and a sensorless technique for triggering and synchronizing OCT imaging with the scanning motor. This sensorless approach uses the motor's electrical feedback (back electromotive force, BEMF) as a virtual Hall sensor to initiate OCT image acquisition and synchronize it with a Fourier Domain Mode-Locked (FDML)-based Megahertz OCT system. Notably, the implementation of BEMF-triggered OCT has led to a substantial reduction in image jitter and NURD (<4 mrad), thereby opening up a new window for real-time visualization capabilities. This approach suggests potential benefits across various applications, aiming to provide a more accurate, deployable, and cost-effective solution. Subsequent studies can explore the adaptability of this system to specific clinical scenarios and its performance under practical endoscopic conditions.
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6
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Fan Y, Liu S, Gao E, Guo R, Dong G, Li Y, Gao T, Tang X, Liao H. The LMIT: Light-mediated minimally-invasive theranostics in oncology. Theranostics 2024; 14:341-362. [PMID: 38164160 PMCID: PMC10750201 DOI: 10.7150/thno.87783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 01/03/2024] Open
Abstract
Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.
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Affiliation(s)
- Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Shuai Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Enze Gao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Rui Guo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Guozhao Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Yangxi Li
- Dept. of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 100084
| | - Tianxin Gao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Hongen Liao
- Dept. of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 100084
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7
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Kendall WY, Tian Q, Zhao S, Mirminachi S, Joseph A, Dufault D, Shi C, Roper J, Wax A. Deep learning classification of ex vivo human colon tissues using spectroscopic OCT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.04.555974. [PMID: 37732221 PMCID: PMC10508742 DOI: 10.1101/2023.09.04.555974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Screening programs for colorectal cancer (CRC) have had a profound impact on the morbidity and mortality of this disease by detecting and removing early cancers and precancerous adenomas with colonoscopy. However, CRC continues to be the third leading cause of cancer-related mortality in both men and woman, partly because of limitations in colonoscopy-based screening. Thus, novel strategies to improve the efficiency and effectiveness of screening colonoscopy are urgently needed. Here, we propose to address this need using an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). The depth resolved images obtained with OCT are analyzed as a function of wavelength to measure optical tissue properties. The optical properties can be used as input to machine learning algorithms as a means to classify adenomatous tissue in the colon. In this study, biopsied tissue samples from the colonic epithelium are analyzed ex vivo using spectroscopic OCT and tissue classifications are generated using a novel deep learning architecture, informed by machine learning methods including LSTM and KNN. The overall classification accuracy obtained was 88.9%, 76.0% and 97.9% in discriminating tissue type for these methods. Further, we apply an approach using false coloring of en face OCT images based on SOCT parameters and deep learning predictions to enable visual identification of tissue type. This study advances the spectroscopic OCT towards clinical utility for analyzing colonic epithelium for signs of adenoma.
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8
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Wolff LI, Hachgenei E, Goßmann P, Druzenko M, Frye M, König N, Schmitt RH, Chrysos A, Jöchle K, Truhn D, Kather JN, Lambertz A, Gaisa NT, Jonigk D, Ulmer TF, Neumann UP, Lang SA, Amygdalos I. Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo. J Cancer Res Clin Oncol 2023; 149:7877-7885. [PMID: 37046121 PMCID: PMC10374764 DOI: 10.1007/s00432-023-04742-x] [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: 02/14/2023] [Accepted: 04/02/2023] [Indexed: 04/14/2023]
Abstract
PURPOSE Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. METHODS Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. RESULTS Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. CONCLUSION Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
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Affiliation(s)
- Laura I Wolff
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Enno Hachgenei
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Paul Goßmann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Mariia Druzenko
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Maik Frye
- Department of Production Quality, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Niels König
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Robert H Schmitt
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
- Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
| | - Alexandros Chrysos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Katharina Jöchle
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Internal Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav, Carus Technical University Dresden, Dresden, Germany
| | - Andreas Lambertz
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Nadine T Gaisa
- Institute for Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Danny Jonigk
- Institute for Pathology, University Hospital RWTH Aachen, Aachen, Germany
- German Center of Lungs Research (DZL, BREATH), Gießen, Germany
| | - Tom F Ulmer
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Ulf P Neumann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Sven A Lang
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Iakovos Amygdalos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany.
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9
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Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, Kehagias D, Kaplanis C, Kotis K, Anagnostopoulos CN, Mulita F. The role of deep learning in diagnosing colorectal cancer. PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:266-273. [PMID: 37937113 PMCID: PMC10626379 DOI: 10.5114/pg.2023.129494] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 11/09/2023]
Abstract
Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.
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Affiliation(s)
- Dimitrios Bousis
- Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | | | - Dimitrios Kehagias
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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10
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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11
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Plekhanov AA, Sirotkina MA, Gubarkova EV, Kiseleva EB, Sovetsky AA, Karabut MM, Zagainov VE, Kuznetsov SS, Maslennikova AV, Zagaynova EV, Zaitsev VY, Gladkova ND. Towards targeted colorectal cancer biopsy based on tissue morphology assessment by compression optical coherence elastography. Front Oncol 2023; 13:1121838. [PMID: 37064146 PMCID: PMC10100073 DOI: 10.3389/fonc.2023.1121838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
Identifying the precise topography of cancer for targeted biopsy in colonoscopic examination is a challenge in current diagnostic practice. For the first time we demonstrate the use of compression optical coherence elastography (C-OCE) technology as a new functional OCT modality for differentiating between cancerous and non-cancerous tissues in colon and detecting their morphological features on the basis of measurement of tissue elastic properties. The method uses pre-determined stiffness values (Young’s modulus) to distinguish between different morphological structures of normal (mucosa and submucosa), benign tumor (adenoma) and malignant tumor tissue (including cancer cells, gland-like structures, cribriform gland-like structures, stromal fibers, extracellular mucin). After analyzing in excess of fifty tissue samples, a threshold stiffness value of 520 kPa was suggested above which areas of colorectal cancer were detected invariably. A high Pearson correlation (r =0.98; p <0.05), and a negligible bias (0.22) by good agreement of the segmentation results of C-OCE and histological (reference standard) images was demonstrated, indicating the efficiency of C-OCE to identify the precise localization of colorectal cancer and the possibility to perform targeted biopsy. Furthermore, we demonstrated the ability of C-OCE to differentiate morphological subtypes of colorectal cancer – low-grade and high-grade colorectal adenocarcinomas, mucinous adenocarcinoma, and cribriform patterns. The obtained ex vivo results highlight prospects of C-OCE for high-level colon malignancy detection. The future endoscopic use of C-OCE will allow targeted biopsy sampling and simultaneous rapid analysis of the heterogeneous morphology of colon tumors.
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Affiliation(s)
- Anton A. Plekhanov
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
- *Correspondence: Anton A. Plekhanov,
| | - Marina A. Sirotkina
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Ekaterina V. Gubarkova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Elena B. Kiseleva
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Alexander A. Sovetsky
- Laboratory of Wave Methods for Studying Structurally Inhomogeneous Media, Institute of Applied Physics Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Maria M. Karabut
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Vladimir E. Zagainov
- Department of Faculty Surgery and Transplantation, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
- Department of Pathology, Nizhny Novgorod Regional Oncologic Hospital, Nizhny Novgorod, Russia
| | - Sergey S. Kuznetsov
- Department of Pathology, Nizhny Novgorod Regional Oncologic Hospital, Nizhny Novgorod, Russia
| | - Anna V. Maslennikova
- Department of Oncology, Radiation Therapy and Radiation Diagnostics, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Elena V. Zagaynova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Vladimir Y. Zaitsev
- Laboratory of Wave Methods for Studying Structurally Inhomogeneous Media, Institute of Applied Physics Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Natalia D. Gladkova
- Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
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12
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Chenhui Yao,
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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13
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Li Y, Fan Y, Liao H. Self-supervised speckle noise reduction of optical coherence tomography without clean data. BIOMEDICAL OPTICS EXPRESS 2022; 13:6357-6372. [PMID: 36589594 PMCID: PMC9774848 DOI: 10.1364/boe.471497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
Optical coherence tomography (OCT) is widely used in clinical diagnosis due to its non-invasive, real-time, and high-resolution characteristics. However, the inherent speckle noise seriously degrades the image quality, which might damage the fine structures in OCT, thus affecting the diagnosis results. In recent years, supervised deep learning-based denoising methods have shown excellent denoising ability. To train a deep denoiser, a large number of paired noisy-clean images are required, which is difficult to achieve in clinical practice, since acquiring a speckle-free OCT image requires dozens of repeated scans and image registration. In this research, we propose a self-supervised strategy that helps build a despeckling model by training it to map neighboring pixels in a single noisy OCT image. Adjacent pixel patches are randomly selected from the original OCT image to generate two similar undersampled images, which are respectively used as the input and target images for training a deep neural network. To ensure both the despeckling and the structure-preserving effects, a multi-scale pixel patch sampler and corresponding loss functions are adopted in our practice. Through quantitative evaluation and qualitative visual comparison, we found that the proposed method performs better than state-of-the-art methods regarding despeckling effects and structure preservation. Besides, the proposed method is much easier to train and deploy without the need for clean OCT images, which has great significance in clinical practice.
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Affiliation(s)
- Yangxi Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Yingwei Fan
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, 100081, China
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
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14
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Liu G, Zhao J, Tian G, Li S, Lu Y. Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis. Front Oncol 2022; 12:925924. [PMID: 36518311 PMCID: PMC9742812 DOI: 10.3389/fonc.2022.925924] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 11/11/2022] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND In recent years, the rapid development of artificial intelligence (AI) technology has created a new diagnostic and therapeutic opportunity for colorectal cancer (CRC). Numerous academic and clinical studies have demonstrated that high-level auxiliary diagnosis and treatment systems based on AI technology can significantly improve the readability of medical data, objectively provide a reliable and comprehensive reference for physicians, reduce the experience gap between physicians, and aid physicians in making more accurate diagnosis decisions. In this study, we used bibliometric techniques to visually analyze the literature about AI in the CRC field and summarize the current situation and research hotspots in this field. METHODS The relevant literature on AI in the field of CRC research was obtained from the Web of Science Core Collection (WoSCC) database. The software CiteSpace was utilized to analyze the number of papers, countries, institutions, authors, journals, cited literature, and keywords of the included literature and generate a visual knowledge map. The present study aims to evaluate the origin, current hotspots, and research trends of AI in CRC using bibliometric analysis. RESULTS As of March 2022, 64 nations/regions, 230 institutions, 245 journals, and 300 authors had published 562 AI-related articles in the field of CRC. Since 2016, each year has seen an exponential increase. China and the United States were the largest contributors, with the largest number of beneficial research institutions and the closest collaboration relationship. The World Journal of Gastroenterology is this field's most widely published journal. Diagnosis and treatment research, gene and immunology research, intestinal polyp research, tumor grading research, gastrointestinal endoscopy research, and prognosis research comprised the six topics derived from high-frequency keyword cluster analysis. CONCLUSION In recent years, field research has been a popular topic of discussion. The results of our bibliometric analysis allow us to comprehend better the current situation and trend of this research field, and the quantitative data indicators can serve as a guide for the research and application of global scholars.
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Affiliation(s)
- Guangwei Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
| | - Jun Zhao
- Department of Pharmacy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangye Tian
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Shuai Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Yun Lu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
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15
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Li Z, Poon W, Ye Z, Qi F, Park BH, Yin Y. Magnetic Field-Modulated Plasmonic Scattering of Hybrid Nanorods for FFT-Weighted OCT Imaging in NIR-II. ACS NANO 2022; 16:12738-12746. [PMID: 35925674 DOI: 10.1021/acsnano.2c04590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We report a method for fast Fourier transform (FFT)-weighted optical coherence tomography (OCT) in the second biological tissue transparency window by actively modulating the plasmonic scattering of Fe3O4@Au hybrid nanorods using magnetic fields. Instead of tracking the nanoparticles' lateral displacement in conventional magnetomotive OCT imaging, we monitor the nanorod rotation and optical signal changes under an alternating magnetic field in real time. The coherent rotation of the nanorods with the field produces periodic OCT signals, and the FFT is then used to convert the periodic OCT signals in the time domain to a single peak in the frequency domain. This allows automatic screening of nanorod signals from the random biological noises and reconstruction of FFT-weighted images using a computer program based on a time-sequence image set. Compared with conventional magnetomotive OCT, the FFT-weighted imaging technique creates enhanced OCT images with dB-scale contrast over an order of magnitude higher than the original images.
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Affiliation(s)
- Zhiwei Li
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Wesley Poon
- Department of Bioengineering, University of California, Riverside, California 92521, United States
| | - Zuyang Ye
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Fenglian Qi
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - B Hyle Park
- Department of Bioengineering, University of California, Riverside, California 92521, United States
| | - Yadong Yin
- Department of Chemistry, University of California, Riverside, California 92521, United States
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16
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Amygdalos I, Hachgenei E, Burkl L, Vargas D, Goßmann P, Wolff LI, Druzenko M, Frye M, König N, Schmitt RH, Chrysos A, Jöchle K, Ulmer TF, Lambertz A, Knüchel-Clarke R, Neumann UP, Lang SA. Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04263-z. [PMID: 35960377 DOI: 10.1007/s00432-022-04263-z] [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: 06/01/2022] [Accepted: 08/02/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN). METHODS Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out. RESULTS A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively. CONCLUSION Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.
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Affiliation(s)
- Iakovos Amygdalos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Enno Hachgenei
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Luisa Burkl
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - David Vargas
- Institute for Histopathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Paul Goßmann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Laura I Wolff
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Mariia Druzenko
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Maik Frye
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Niels König
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Robert H Schmitt
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.,Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
| | - Alexandros Chrysos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Katharina Jöchle
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Tom F Ulmer
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Andreas Lambertz
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ruth Knüchel-Clarke
- Institute for Histopathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Ulf P Neumann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Sven A Lang
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
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17
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Luo H, Li S, Zeng Y, Cheema H, Otegbeye E, Ahmed S, Chapman WC, Mutch M, Zhou C, Zhu Q. Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning. JOURNAL OF BIOPHOTONICS 2022; 15:e202100349. [PMID: 35150067 PMCID: PMC9581715 DOI: 10.1002/jbio.202100349] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/16/2022] [Accepted: 02/09/2022] [Indexed: 05/02/2023]
Abstract
Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent advances in convolutional neural networks (CNN) have enabled application in ophthalmology, cardiology, and gastroenterology malignancy detection with high sensitivity and specificity. Here, we describe a miniaturized OCT catheter and a residual neural network (ResNet)-based deep learning model manufactured and trained to perform automatic image processing and real-time diagnosis of the OCT images. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~7 μm, and an axial resolution of ~6 μm. A customized ResNet is utilized to classify OCT catheter colorectal images. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.
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Affiliation(s)
- Hongbo Luo
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Yifeng Zeng
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Hassam Cheema
- Department of Anatomic & Molecular Pathology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ebunoluwa Otegbeye
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Safee Ahmed
- Department of Anatomic & Clinical Pathology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - William C. Chapman
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Matthew Mutch
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Chao Zhou
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Quing Zhu
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
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18
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Li M, Gong J, Bao Y, Huang D, Peng J, Tong T. Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing CT radiomics and deep-learning features of primary lesions and peripheral lymph nodes. Int J Cancer 2022; 152:31-41. [PMID: 35484979 DOI: 10.1002/ijc.34053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/10/2022] [Accepted: 04/21/2022] [Indexed: 11/11/2022]
Abstract
Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In this study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76±0.08 and 0.91±0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Yichao Bao
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Junjie Peng
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
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Yuan W, Feng Y, Chen D, Gharibani P, Chen JDZ, Yu H, Li X. In vivo assessment of inflammatory bowel disease in rats with ultrahigh-resolution colonoscopic OCT. BIOMEDICAL OPTICS EXPRESS 2022; 13:2091-2102. [PMID: 35519259 PMCID: PMC9045891 DOI: 10.1364/boe.453396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 06/12/2023]
Abstract
A technology capable of high-resolution, label-free imaging of subtle pathology in vivo during colonoscopy is imperative for the early detection of disease and the performance of accurate biopsies. While colonoscopic OCT has been developed to visualize colonic microstructures beyond the mucosal surface, its clinical potential remains limited by sub-optimal resolution (∼6.5 µm in tissue), inadequate imaging contrast, and a lack of high-resolution OCT criteria for lesion detection. In this study, we developed an ultrahigh-resolution (UHR) colonoscopic OCT and evaluated its ability to volumetrically visualize and identify the pathological features of inflammatory bowel disease (IBD) in a rat model. Owing to its improved resolution (∼1.7 µm in tissue) and enhanced contrast, UHR colonoscopic OCT can accurately delineate fine colonic microstructures and identify the pathophysiological characteristics of IBD in vivo. By using a quantitative optical attenuation map, UHR colonoscopic OCT is able to differentiate diseased tissue (such as crypt distortion and microabscess) from normal colonic mucosa over a large field of view in vivo. Our results suggest the clinical potential of UHR colonoscopic OCT for in vivo assessment of IBD pathology.
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Affiliation(s)
- Wu Yuan
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Yan Feng
- Department of Pathology and Laboratory Medicine, Pennsylvania Hospital, Penn Medicine, Philadelphia, PA 19107, USA
| | - Defu Chen
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Payam Gharibani
- Division of Gastroenterology and Hepatology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Jiande D. Z. Chen
- Division of Gastroenterology and Hepatology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Huimin Yu
- Division of Gastroenterology and Hepatology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Xingde Li
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9025470. [PMID: 34754327 PMCID: PMC8572604 DOI: 10.1155/2021/9025470] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 12/30/2022]
Abstract
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
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21
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Zou Y, Zeng Y, Li S, Zhu Q. Machine learning model with physical constraints for diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:5720-5735. [PMID: 34692211 PMCID: PMC8515969 DOI: 10.1364/boe.432786] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/06/2021] [Accepted: 08/09/2021] [Indexed: 05/02/2023]
Abstract
A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under-determined problem, and its quality suffers by model mismatches, complex boundary conditions, tissue-probe contact, noise etc. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) a neural network based on auto-encoder is adopted for DOT reconstruction, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of existing models. In a phantom study, compared with the Born conjugate gradient descent (Born-CGD) reconstruction method, the ML-PC method decreases the mean percentage error of the reconstructed maximum absorption coefficient from 16.41% to 13.4% for high contrast phantoms and from 23.42% to 9.06% for low contrast phantoms, with improved depth distribution of the target absorption maps. In a clinical study, better contrast was obtained between malignant and benign breast lesions, with the ratio of the medians of the maximum absorption coefficient improved from 1.63 to 2.22.
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Affiliation(s)
- Yun Zou
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Yifeng Zeng
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
- Department of Radiology, Washington University School of Medicine, St. Louis 63110, USA
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22
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Coleman MJ, Selmic LE, Samuelson JP, Jennings R, Huang PC, McLaughlin EM, Wavreille VA, Dornbusch JA, Lapsley J, Howard J, Cheng E, Kalamaras A, Hearon K, Cray M, Grimes J, Wustefeld-Janssens B, Kennedy K, Skinner O, Amsellem P, Boppart SA. Diagnostic accuracy of optical coherence tomography for surgical margin assessment of feline injection-site sarcoma. Vet Comp Oncol 2021; 19:632-640. [PMID: 34427379 DOI: 10.1111/vco.12766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/31/2021] [Accepted: 08/18/2021] [Indexed: 02/05/2023]
Abstract
The invasive, locally aggressive nature of feline injection-site sarcomas (FISSs) poses a unique challenge for surgeons to obtain complete margins with surgical excision. Optical coherence tomography (OCT), an imaging technology that uses light waves to generate real-time views of tissue architecture, provides an emerging solution to this dilemma by allowing fast, high-resolution scanning of surgical margins. The purpose of this study was to use OCT to assess surgical margins of FISS and to evaluate the diagnostic accuracy of OCT for detecting residual cancer using six evaluators of varying experience. Five FISSs were imaged with OCT to create a training set of OCT images that were compared with histopathology. Next, 25 FISSs were imaged with OCT prior to histopathology. Six evaluators of varying experience participated in a training session on OCT imaging after which each of the evaluators was given a dataset that included OCT images and videos to score on a scale from cancerous to non-cancerous. Diagnostic accuracy statistics were calculated. The overall sensitivity and specificity for classification of OCT images by evaluators were 78.9% and 77.6%, respectively. Correct classification rate of OCT images was associated with experience, while individual sensitivities and specificities had more variation between experience groups. This study demonstrates the ability of evaluators to correctly classify OCT images with overall low levels of experience and training and also illustrates areas where increased training can improve accuracy of evaluators in interpretation of OCT surgical margin images.
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Affiliation(s)
- Mary J Coleman
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Laura E Selmic
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Jonathan P Samuelson
- Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Ryan Jennings
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Pin-Chieh Huang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Eric M McLaughlin
- Center for Biostatistics, The Ohio State University, Columbus, Ohio, USA
| | - Vincent A Wavreille
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Josephine A Dornbusch
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Janis Lapsley
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - James Howard
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Edward Cheng
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Alex Kalamaras
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Kendra Hearon
- Department of Surgery, Metropolitan Veterinary Specialists, Philadelphia, Pennsylvannia, USA
| | - Megan Cray
- Department of Surgery, Angell Animal Medical Center, Boston, Massachusetts, USA
| | - Janet Grimes
- Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA
| | - Brandan Wustefeld-Janssens
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Texas A & M University, College Station, Texas, USA
| | - Katie Kennedy
- Department of Surgery, Animal Medical Center, New York City, New York, USA
| | - Owen Skinner
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Missouri, Columbia, Missouri, USA
| | - Pierre Amsellem
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Minnesota, Twin Cities, Minnesota, USA
| | - Stephen A Boppart
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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23
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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24
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Li Y, Fan Y, Hu C, Mao F, Zhang X, Liao H. Intelligent optical diagnosis and treatment system for automated image-guided laser ablation of tumors. Int J Comput Assist Radiol Surg 2021; 16:2147-2157. [PMID: 34363584 DOI: 10.1007/s11548-021-02457-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/06/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE For tumor resections near critical structures, accurate identification of tumor boundaries and maximum removal are the keys to improve surgical outcome and patient survival rate, especially in neurosurgery. In this paper, we propose an intelligent optical diagnosis and treatment system for tumor removal, with automated lesion localization and laser ablation. METHODS The proposed system contains a laser ablation module, an optical coherence tomography (OCT) unit, and a robotic arm along with a stereo camera. The robotic arm can move the OCT sample arm and the laser ablation front-end to the suspected lesion area. The corresponding diagnosis and treatment procedures include computer-aided lesion segmentation using OCT, automated ablation planning, and laser control. The ablation process is controlled by a deflectable mirror, and a non-common-path ablation planning algorithm based on the transformation from lesion positions to mirror deflection angles is presented. RESULTS Phantom and animal experiments are carried out for system verification. The robot could reach the planned position with high precision, which is approximately 1.16 mm. Tissue classification with OCT images achieves 91.7% accuracy. The error of OCT-guided automated laser ablation is approximately 0.74 mm. Experiments on mouse brain tumors show that the proposed system is capable of clearing lesions efficiently and precisely. We also conducted an ex vivo porcine brain experiment to verify the whole process of the system. CONCLUSION An intelligent optical diagnosis and treatment system is proposed for tumor removal. Experimental results show that the proposed system and method are promising for precise and intelligent theranostics. Compared to conventional cancer diagnosis and treatment, the proposed system allows for automated operations monitored in real-time, with higher precision and efficiency.
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Affiliation(s)
- Yangxi Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yingwei Fan
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Chengquan Hu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Fan Mao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Xinran Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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25
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Lui CG, Kim W, Dewey JB, Macías-Escrivá FD, Ratnayake K, Oghalai JS, Applegate BE. In vivo functional imaging of the human middle ear with a hand-held optical coherence tomography device. BIOMEDICAL OPTICS EXPRESS 2021; 12:5196-5213. [PMID: 34513251 PMCID: PMC8407818 DOI: 10.1364/boe.430935] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/02/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
We describe an optical coherence tomography and vibrometry system designed for portable hand-held usage in the otology clinic on awake patients. The system provides clinically relevant point-of-care morphological imaging with 14-44 µm resolution and functional vibratory measures with sub-nanometer sensitivity. We evaluated various new approaches for extracting functional information including a multi-tone stimulus, a continuous chirp stimulus, and alternating air and bone stimulus. We also explored the vibratory response over an area of the tympanic membrane (TM) and generated TM thickness maps. Our results suggest that the system can provide real-time in vivo imaging and vibrometry of the ear and could prove useful for investigating otologic pathology in the clinic setting.
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Affiliation(s)
- Christopher G. Lui
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
- These authors contributed equally to this work
| | - Wihan Kim
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
- These authors contributed equally to this work
| | - James B. Dewey
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
| | - Frank D. Macías-Escrivá
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
| | - Kumara Ratnayake
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
| | - John S. Oghalai
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
| | - Brian E. Applegate
- Department of Otolaryngology - Head and Neck Surgery, Keck School of Medicine, University of Southern California, 1450 San Pablo Street, Suite 5708, Los Angeles, CA 90033, USA
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Denney Research Center (DRB) 140, Los Angeles, CA 90089, USA
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26
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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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27
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Zeng Y, Chapman WC, Lin Y, Li S, Mutch M, Zhu Q. Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography. JOURNAL OF BIOPHOTONICS 2021; 14:e202000276. [PMID: 33064368 PMCID: PMC8196414 DOI: 10.1002/jbio.202000276] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/08/2020] [Accepted: 10/11/2020] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.
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Affiliation(s)
- Yifeng Zeng
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
| | - William C Chapman
- Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Yixiao Lin
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
| | - Matthew Mutch
- Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
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28
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Kose U, Deperlioglu O, Alzubi J, Patrut B. Future of Medical Decision Support Systems. DEEP LEARNING FOR MEDICAL DECISION SUPPORT SYSTEMS 2021. [PMCID: PMC7298991 DOI: 10.1007/978-981-15-6325-6_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Utku Kose
- Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
| | - Omer Deperlioglu
- Department of Computer Technologies, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Jafar Alzubi
- Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan
| | - Bogdan Patrut
- Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, Iasi, Romania
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29
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Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. A comprehensive review of deep learning in colon cancer. Comput Biol Med 2020; 126:104003. [DOI: 10.1016/j.compbiomed.2020.104003] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/28/2020] [Accepted: 08/28/2020] [Indexed: 12/17/2022]
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30
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Cao R, Yang F, Ma SC, Liu L, Zhao Y, Li Y, Wu DH, Wang T, Lu WJ, Cai WJ, Zhu HB, Guo XJ, Lu YW, Kuang JJ, Huan WJ, Tang WM, Huang K, Huang J, Yao J, Dong ZY. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer. Am J Cancer Res 2020; 10:11080-11091. [PMID: 33042271 PMCID: PMC7532670 DOI: 10.7150/thno.49864] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/25/2020] [Indexed: 12/21/2022] Open
Abstract
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
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31
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Fabelo C, Selmic LE, Huang PC, Samuelson JP, Reagan JK, Kalamaras A, Wavreille V, Monroy GL, Marjanovic M, Boppart SA. Evaluating optical coherence tomography for surgical margin assessment of canine mammary tumours. Vet Comp Oncol 2020; 19:697-706. [PMID: 32562330 DOI: 10.1111/vco.12632] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/11/2020] [Accepted: 06/17/2020] [Indexed: 12/20/2022]
Abstract
Optical coherence tomography (OCT) uses near-infrared light waves to generate real-time, high-resolution images on the microscopic scale similar to low power histopathology. Previous studies have demonstrated the use of OCT for real-time surgical margin assessment for human breast cancer. The use of OCT for canine mammary tumours (CMT) could allow intra-operative visualisation of residual tumour at the surgical margins. The purpose of this study was to assess OCT imaging for the detection of incomplete tumour resection following CMT surgery. We hypothesized that the OCT images would have comparable features to histopathological images of tissues at the surgical margins of CMT resections along with a high sensitivity of OCT detection of incomplete surgical excision of CMT. Thirty surgical specimens were obtained from nineteen client-owned dogs undergoing surgical resection of CMT. OCT image appearance and characteristics of adipose tissue, skin, mammary tissue and mammary tumour at the surgical margins were distinct and different. The OCT images of normal and abnormal tissues at the surgical margins were utilized to develop a dataset of OCT images for observer evaluation. The sensitivity and specificity for ex vivo images were 83.3% and 82.0% (observer 1) and 70.0% and 67.9% (observer 2). The sensitivity and specificity for in vivo images were 70.0% and 89.3% (observer 1) and 76.7% and 67.9% (observer 2). These results indicate a potential use of OCT for surgical margin assessment for CMT to optimize surgical intervention and clinical outcomes. Improved training and experience of observers may improve sensitivity and specificity.
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Affiliation(s)
- Carolina Fabelo
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Ohio State University, Columbus, Ohio, USA
| | - Laura E Selmic
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Ohio State University, Columbus, Ohio, USA
| | - Pin-Cheh Huang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Jonathan P Samuelson
- Department of Veterinary Clinical Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jennifer K Reagan
- Department of Surgery, Seattle Veterinary Specialists-Downtown, Seattle, Washington, USA
| | - Alexandra Kalamaras
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Ohio State University, Columbus, Ohio, USA
| | - Vincent Wavreille
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Ohio State University, Columbus, Ohio, USA
| | - Guillermo L Monroy
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Marina Marjanovic
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Stephen A Boppart
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
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