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Hua W, Zhang W, Brown H, Wu J, Fang X, Shahi M, Chen R, Zhang H, Jiao B, Wang N, Xu H, Fu M, Wang X, Zhang J, Zhang X, Wang Q, Zhu W, Ye D, Garcia DM, Chaichana K, Cooks RG, Ouyang Z, Mao Y, Quinones-Hinojosa A. Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry. Proc Natl Acad Sci U S A 2024; 121:e2318843121. [PMID: 38805277 PMCID: PMC11161794 DOI: 10.1073/pnas.2318843121] [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: 10/29/2023] [Accepted: 04/25/2024] [Indexed: 05/30/2024] Open
Abstract
The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.
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Affiliation(s)
- Wei Hua
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Wenpeng Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Hannah Brown
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Junhan Wu
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Xinqi Fang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Mahdiyeh Shahi
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Rong Chen
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Haoyue Zhang
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
| | - Bin Jiao
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
| | - Nan Wang
- PurSpecTechnologies, Beijing100084, China
| | - Hao Xu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Minjie Fu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Xiaowen Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Jinsen Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Qijun Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Wei Zhu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
| | - Dan Ye
- The Molecular and Cell Biology Lab, Institute of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai200232, China
| | | | | | - R. Graham Cooks
- Department of Chemistry, Purdue University, West Lafayette, IN47907
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai200040, China
- National Center for Neurological Disorders, Shanghai200040, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai200040, China
- Neurosurgical Institute of Fudan University, Shanghai200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai200040, China
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Raghunathan R, Vasquez M, Zhang K, Zhao H, Wong STC. Label-free optical imaging for brain cancer assessment. Trends Cancer 2024; 10:557-570. [PMID: 38575412 PMCID: PMC11168891 DOI: 10.1016/j.trecan.2024.03.005] [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: 01/01/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/06/2024]
Abstract
Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.
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Affiliation(s)
- Raksha Raghunathan
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Matthew Vasquez
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Katherine Zhang
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Hong Zhao
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA; Departments of Radiology, Pathology, and Laboratory Medicine and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
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3
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Burström G, Amini M, El-Hajj VG, Arfan A, Gharios M, Buwaider A, Losch MS, Manni F, Edström E, Elmi-Terander A. Optical Methods for Brain Tumor Detection: A Systematic Review. J Clin Med 2024; 13:2676. [PMID: 38731204 PMCID: PMC11084501 DOI: 10.3390/jcm13092676] [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: 04/11/2024] [Revised: 04/28/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.
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Affiliation(s)
- Gustav Burström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Misha Amini
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Victor Gabriel El-Hajj
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Arooj Arfan
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Maria Gharios
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Ali Buwaider
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Merle S. Losch
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, 2627 Delft, The Netherlands
| | - Francesca Manni
- Department of Electrical Engineering, Eindhoven University of Technology (TU/e), 5612 Eindhoven, The Netherlands;
| | - Erik Edström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
| | - Adrian Elmi-Terander
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
- Department of Surgical Sciences, Uppsala University, 751 35 Uppsala, Sweden
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4
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Achkasova K, Kukhnina L, Moiseev A, Kiseleva E, Bogomolova A, Loginova M, Gladkova N. Detection of acute and early-delayed radiation-induced changes in the white matter of the rat brain based on numerical processing of optical coherence tomography data. JOURNAL OF BIOPHOTONICS 2024; 17:e202300458. [PMID: 38253332 DOI: 10.1002/jbio.202300458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/25/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Detection of radiation-induced changes of the brain white matter is important for brain neoplasms repeated surgery. We investigated the influence of irradiation on the scattering properties of the white matter using optical coherence tomography (OCT). Healthy Wistar rats undergone the irradiation of the brain right hemisphere. At seven time points from the irradiation procedure (2-14 weeks), an ex vivo OCT study was performed with subsequent calculation of attenuation coefficient values in the corpus callosum followed by immunohistochemical analysis. As a result, we discovered acute and early-delayed changes characterized by the edema of different severity, accompanied by a statistically significant decrease in attenuation coefficient values. In particular, these changes were found at 2 weeks after irradiation in the irradiated hemisphere, while at 6- and 12-week time points they affected both irradiated and contralateral hemisphere. Thus, radiation-induced changes occurring in white matter during the first 3 months after irradiation can be detected by OCT.
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Affiliation(s)
- Ksenia Achkasova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Liudmila Kukhnina
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Alexander Moiseev
- Laboratory of Highly Sensitive Optical Measurements, Institute of Applied Physics of Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Elena Kiseleva
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Alexandra Bogomolova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Maria Loginova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Natalia Gladkova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
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5
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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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6
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Moiseev A, Sherstnev E, Kiseleva E, Achkasova K, Potapov A, Yashin K, Sirotkina M, Gelikonov G, Matkivsky V, Shilyagin P, Ksenofontov S, Bederina E, Medyanik I, Zagaynova E, Gladkova N. Depth-resolved method for attenuation coefficient calculation from optical coherence tomography data for improved biological structure visualization. JOURNAL OF BIOPHOTONICS 2023; 16:e202100392. [PMID: 37551154 DOI: 10.1002/jbio.202100392] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 08/09/2023]
Abstract
Optical coherence tomography (OCT) is a promising tool for intraoperative tissue morphology determination. Several studies suggest that attenuation coefficient derived from the OCT images, can differentiate between tissues of different morphology, such as normal and pathological structures of the brain, skin, and other tissues. In the present study, the depth-resolved method for attenuation coefficient calculation was adopted for the real-world situation of the depth-dependent OCT sensitivity and additive imaging noise with nonzero mean. It was shown that in the case of sharp focusing (~10 μm spot full width at half maximum [FWHM] or smaller at 1.3 μm central wavelength) only the proposed method for depth-dependent sensitivity compensation does not introduce misleading artifacts into the calculated attenuation coefficient distribution. At the same time, the scanning beam focus spot with FWHM greater than 10 μm at 1.3 μm central wavelength allows one to use multiple approaches to the attenuation coefficient calculation without introducing noticeable bias. This feature may hinder the need for robust corrections for the depth-resolved attenuation coefficient estimations from the community.
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Affiliation(s)
- Alexander Moiseev
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Evgeny Sherstnev
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Elena Kiseleva
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Ksenia Achkasova
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Arseniy Potapov
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | | | - Marina Sirotkina
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Grigory Gelikonov
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Vasily Matkivsky
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Pavel Shilyagin
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Sergey Ksenofontov
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Evgenia Bederina
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Igor Medyanik
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Elena Zagaynova
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
- Nizhny Novgorod State University, Nizhny Novgorod, Russia
| | - Natalia Gladkova
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia
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7
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Al-Adli NN, Young JS, Scotford K, Sibih YE, Payne J, Berger MS. Advances in Intraoperative Glioma Tissue Sampling and Infiltration Assessment. Brain Sci 2023; 13:1637. [PMID: 38137085 PMCID: PMC10741454 DOI: 10.3390/brainsci13121637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/06/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
Gliomas are infiltrative brain tumors that often involve functional tissue. While maximal safe resection is critical for maximizing survival, this is challenged by the difficult intraoperative discrimination between tumor-infiltrated and normal structures. Surgical expertise is essential for identifying safe margins, and while the intraoperative pathological review of frozen tissue is possible, this is a time-consuming task. Advances in intraoperative stimulation mapping have aided surgeons in identifying functional structures and, as such, has become the gold standard for this purpose. However, intraoperative margin assessment lacks a similar consensus. Nonetheless, recent advances in intraoperative imaging techniques and tissue examination methods have demonstrated promise for the accurate and efficient assessment of tumor infiltration and margin delineation within the operating room, respectively. In this review, we describe these innovative technologies that neurosurgeons should be aware of.
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Affiliation(s)
- Nadeem N. Al-Adli
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA 94131, USA; (N.N.A.-A.); (J.S.Y.); (K.S.); (J.P.)
- School of Medicine, Texas Christian University, Fort Worth, TX 76109, USA
| | - Jacob S. Young
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA 94131, USA; (N.N.A.-A.); (J.S.Y.); (K.S.); (J.P.)
| | - Katie Scotford
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA 94131, USA; (N.N.A.-A.); (J.S.Y.); (K.S.); (J.P.)
| | - Youssef E. Sibih
- School of Medicine, University of California San Francisco, San Francisco, CA 94131, USA;
| | - Jessica Payne
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA 94131, USA; (N.N.A.-A.); (J.S.Y.); (K.S.); (J.P.)
| | - Mitchel S. Berger
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA 94131, USA; (N.N.A.-A.); (J.S.Y.); (K.S.); (J.P.)
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8
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Li K, Wu Q, Feng S, Zhao H, Jin W, Qiu H, Gu Y, Chen D. In situ detection of human glioma based on tissue optical properties using diffuse reflectance spectroscopy. JOURNAL OF BIOPHOTONICS 2023; 16:e202300195. [PMID: 37589177 DOI: 10.1002/jbio.202300195] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/18/2023] [Accepted: 08/15/2023] [Indexed: 08/18/2023]
Abstract
Safely maximizing brain cancer removal without injuring adjacent healthy tissue is crucial for optimal treatment outcomes. However, it is challenging to distinguish cancer from noncancer intraoperatively. This study aimed to explore the feasibility of diffuse reflectance spectroscopy (DRS) as a label-free and real-time detection technology for discrimination between brain cancer and noncancer tissues. Fifty-five fresh cancer and noncancer specimens from 19 brain surgeries were measured with DRS, and the results were compared with co-registered clinical standard histopathology. Tissue optical properties were quantitatively obtained from the diffuse reflectance spectra and compared among different types of brain tissues. A machine learning-based classifier was trained to differentiate cancerous versus noncancerous tissues. Our method could achieve a sensitivity of 93% and specificity of 95% for discriminating high-grade glioma from normal white matter. Our results showed that DRS has the potential to be used for label-free, real-time in vivo cancer detection during brain surgery.
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Affiliation(s)
- Kerui Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Qijia Wu
- Department of Neurosurgery, First Medical Center of PLA General Hospital, Beijing, China
| | - Shiyu Feng
- Department of Neurosurgery, First Medical Center of PLA General Hospital, Beijing, China
| | - Hongyou Zhao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Wei Jin
- Department of Pathology, Chinese PLA General Hospital, Beijing, China
| | - Haixia Qiu
- Department of Laser Medicine, First Medical Center of PLA General Hospital, Beijing, China
| | - Ying Gu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
- Department of Laser Medicine, First Medical Center of PLA General Hospital, Beijing, China
- Precision Laser Medical Diagnosis and Treatment Innovation Unit, Chinese Academy of Medical Sciences, Beijing, China
| | - Defu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
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9
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Cao C, Yin H, Yang B, Yue Q, Wu G, Gu M, Zhang Y, Fan Y, Dong X, Wang T, Wang C, Zhu X, Mao Y, Zhang X, Lei Z, Li C. Intra-Operative Definition of Glioma Infiltrative Margins by Visualizing Immunosuppressive Tumor-Associated Macrophages. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304020. [PMID: 37544917 PMCID: PMC10558635 DOI: 10.1002/advs.202304020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Indexed: 08/08/2023]
Abstract
Accurate delineation of glioma infiltrative margins remains a challenge due to the low density of cancer cells in these regions. Here, a hierarchical imaging strategy to define glioma margins by locating the immunosuppressive tumor-associated macrophages (TAMs) is proposed. A pH ratiometric fluorescent probe CP2-M that targets immunosuppressive TAMs by binding to mannose receptor (CD206) is developed, and it subsequently senses the acidic phagosomal lumen, resulting in a remarkable fluorescence enhancement. With assistance of CP2-M, glioma xenografts in mouse models with a tumor-to-background ratio exceeding 3.0 for up to 6 h are successfully visualized. Furthermore, by intra-operatively mapping the pH distribution of exposed tissue after craniotomy, the glioma allograft in rat models is precisely excised. The overall survival of rat models significantly surpasses that achieved using clinically employed fluorescent probes. This work presents a novel strategy for locating glioma margins, thereby improving surgical outcomes for tumors with infiltrative characteristics.
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Affiliation(s)
- Chong Cao
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Hang Yin
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Biao Yang
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Qi Yue
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Guoqing Wu
- School of Information Science and TechnologyFudan UniversityShanghai200438China
| | - Meng Gu
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Yuwen Zhang
- Institute of Science and Technology for Brain‐Inspired IntelligenceMOE Key Laboratory of Computational Neuroscience and Brain‐Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan University220 Handan RoadShanghai200433China
| | - Yang Fan
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Xiaoyan Dong
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Ting Wang
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Cong Wang
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Xiao Zhu
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Ying Mao
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Xiao‐Yong Zhang
- Institute of Science and Technology for Brain‐Inspired IntelligenceMOE Key Laboratory of Computational Neuroscience and Brain‐Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan University220 Handan RoadShanghai200433China
| | - Zuhai Lei
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
| | - Cong Li
- Key Laboratory of Smart Drug Delivery Ministry of EducationInnovative Center for New Drug Development of Immune Inflammatory Diseases, Ministry of EducationSchool of PharmacyDepartment of Neurosurgery, Huashan HospitalFudan UniversityShanghai201203China
- State Key Laboratory of Medical NeurobiologyZhongshan HospitalFudan UniversityShanghai200032China
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10
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Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [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: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
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Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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11
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Kumari S, Gupta R, Ambasta RK, Kumar P. Multiple therapeutic approaches of glioblastoma multiforme: From terminal to therapy. Biochim Biophys Acta Rev Cancer 2023; 1878:188913. [PMID: 37182666 DOI: 10.1016/j.bbcan.2023.188913] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/24/2023] [Accepted: 05/10/2023] [Indexed: 05/16/2023]
Abstract
Glioblastoma multiforme (GBM) is an aggressive brain cancer showing poor prognosis. Currently, treatment methods of GBM are limited with adverse outcomes and low survival rate. Thus, advancements in the treatment of GBM are of utmost importance, which can be achieved in recent decades. However, despite aggressive initial treatment, most patients develop recurrent diseases, and the overall survival rate of patients is impossible to achieve. Currently, researchers across the globe target signaling events along with tumor microenvironment (TME) through different drug molecules to inhibit the progression of GBM, but clinically they failed to demonstrate much success. Herein, we discuss the therapeutic targets and signaling cascades along with the role of the organoids model in GBM research. Moreover, we systematically review the traditional and emerging therapeutic strategies in GBM. In addition, we discuss the implications of nanotechnologies, AI, and combinatorial approach to enhance GBM therapeutics.
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Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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12
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Bhargav AG, Domino JS, Alvarado AM, Tuchek CA, Akhavan D, Camarata PJ. Advances in computational and translational approaches for malignant glioma. Front Physiol 2023; 14:1219291. [PMID: 37405133 PMCID: PMC10315500 DOI: 10.3389/fphys.2023.1219291] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/05/2023] [Indexed: 07/06/2023] Open
Abstract
Gliomas are the most common primary brain tumors in adults and carry a dismal prognosis for patients. Current standard-of-care for gliomas is comprised of maximal safe surgical resection following by a combination of chemotherapy and radiation therapy depending on the grade and type of tumor. Despite decades of research efforts directed towards identifying effective therapies, curative treatments have been largely elusive in the majority of cases. The development and refinement of novel methodologies over recent years that integrate computational techniques with translational paradigms have begun to shed light on features of glioma, previously difficult to study. These methodologies have enabled a number of point-of-care approaches that can provide real-time, patient-specific and tumor-specific diagnostics that may guide the selection and development of therapies including decision-making surrounding surgical resection. Novel methodologies have also demonstrated utility in characterizing glioma-brain network dynamics and in turn early investigations into glioma plasticity and influence on surgical planning at a systems level. Similarly, application of such techniques in the laboratory setting have enhanced the ability to accurately model glioma disease processes and interrogate mechanisms of resistance to therapy. In this review, we highlight representative trends in the integration of computational methodologies including artificial intelligence and modeling with translational approaches in the study and treatment of malignant gliomas both at the point-of-care and outside the operative theater in silico as well as in the laboratory setting.
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Affiliation(s)
- Adip G. Bhargav
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Joseph S. Domino
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Anthony M. Alvarado
- Department of Neurological Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Chad A. Tuchek
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - David Akhavan
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS, United States
- Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS, United States
- Bioengineering Program, University of Kansas Medical Center, Kansas City, KS, United States
| | - Paul J. Camarata
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
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13
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Luo J, Pan M, Mo K, Mao Y, Zou D. Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma. Semin Cancer Biol 2023; 91:110-123. [PMID: 36907387 DOI: 10.1016/j.semcancer.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023]
Abstract
Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.
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Affiliation(s)
- Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Ke Mo
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Yingwei Mao
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
| | - Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China; Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
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14
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Achkasova KA, Moiseev AA, Yashin KS, Kiseleva EB, Bederina EL, Loginova MM, Medyanik IA, Gelikonov GV, Zagaynova EV, Gladkova ND. Nondestructive label-free detection of peritumoral white matter damage using cross-polarization optical coherence tomography. Front Oncol 2023; 13:1133074. [PMID: 36937429 PMCID: PMC10017731 DOI: 10.3389/fonc.2023.1133074] [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: 12/28/2022] [Accepted: 01/27/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction To improve the quality of brain tumor resections, it is important to differentiate zones with myelinated fibers destruction from tumor tissue and normal white matter. Optical coherence tomography (OCT) is a promising tool for brain tissue visualization and in the present study, we demonstrate the ability of cross-polarization (CP) OCT to detect damaged white matter and differentiate it from normal and tumor tissues. Materials and methods The study was performed on 215 samples of brain tissue obtained from 57 patients with brain tumors. The analysis of the obtained OCT data included three stages: 1) visual analysis of structural OCT images; 2) quantitative assessment based on attenuation coefficients estimation in co- and cross-polarizations; 3) building of color-coded maps with subsequent visual analysis. The defining characteristics of structural CP OCT images and color-coded maps were determined for each studied tissue type, and then two classification tests were passed by 8 blinded respondents after a training. Results Visual assessment of structural CP OCT images allows detecting white matter areas with damaged myelinated fibers and differentiate them from normal white matter and tumor tissue. Attenuation coefficients also allow distinguishing all studied brain tissue types, while it was found that damage to myelinated fibers leads to a statistically significant decrease in the values of attenuation coefficients compared to normal white matter. Nevertheless, the use of color-coded optical maps looks more promising as it combines the objectivity of optical coefficient and clarity of the visual assessment, which leads to the increase of the diagnostic accuracy of the method compared to visual analysis of structural OCT images. Conclusions Alteration of myelinated fibers causes changes in the scattering properties of the white matter, which gets reflected in the nature of the received CP OCT signal. Visual assessment of structural CP OCT images and color-coded maps allows differentiating studied tissue types from each other, while usage of color-coded maps demonstrates higher diagnostic accuracy values in comparison with structural images (F-score = 0.85-0.86 and 0.81, respectively). Thus, the results of the study confirm the potential of using OCT as a neuronavigation tool during resections of brain tumors.
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Affiliation(s)
- Ksenia A. Achkasova
- Research institute of experimental oncology and biomedical technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
- *Correspondence: Ksenia A. Achkasova,
| | - Alexander A. Moiseev
- Laboratory of Highly Sensitive Optical Measurements, Institute of Applied Physics of Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Konstantin S. Yashin
- Department of oncology and neurosurgery, University clinic, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Elena B. Kiseleva
- Research institute of experimental oncology and biomedical technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Evgenia L. Bederina
- Department of pathology, University clinic, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Maria M. Loginova
- Research institute of experimental oncology and biomedical technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Igor A. Medyanik
- Department of oncology and neurosurgery, University clinic, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Grigory V. Gelikonov
- Laboratory of Highly Sensitive Optical Measurements, Institute of Applied Physics of Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Elena V. Zagaynova
- Research institute of experimental oncology and biomedical technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
- Lobachevsky State University, Nizhny Novgorod, Russia
| | - Natalia D. Gladkova
- Research institute of experimental oncology and biomedical technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
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15
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Kuppler P, Strenge P, Lange B, Spahr-Hess S, Draxinger W, Hagel C, Theisen-Kunde D, Brinkmann R, Huber R, Tronnier V, Bonsanto MM. The neurosurgical benefit of contactless in vivo optical coherence tomography regarding residual tumor detection: A clinical study. Front Oncol 2023; 13:1151149. [PMID: 37139150 PMCID: PMC10150702 DOI: 10.3389/fonc.2023.1151149] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/13/2023] [Indexed: 05/05/2023] Open
Abstract
Purpose In brain tumor surgery, it is crucial to achieve complete tumor resection while conserving adjacent noncancerous brain tissue. Several groups have demonstrated that optical coherence tomography (OCT) has the potential of identifying tumorous brain tissue. However, there is little evidence on human in vivo application of this technology, especially regarding applicability and accuracy of residual tumor detection (RTD). In this study, we execute a systematic analysis of a microscope integrated OCT-system for this purpose. Experimental design Multiple 3-dimensional in vivo OCT-scans were taken at protocol-defined sites at the resection edge in 21 brain tumor patients. The system was evaluated for its intraoperative applicability. Tissue biopsies were obtained at these locations, labeled by a neuropathologist and used as ground truth for further analysis. OCT-scans were visually assessed with a qualitative classifier, optical OCT-properties were obtained and two artificial intelligence (AI)-assisted methods were used for automated scan classification. All approaches were investigated for accuracy of RTD and compared to common techniques. Results Visual OCT-scan classification correlated well with histopathological findings. Classification with measured OCT image-properties achieved a balanced accuracy of 85%. A neuronal network approach for scan feature recognition achieved 82% and an auto-encoder approach 85% balanced accuracy. Overall applicability showed need for improvement. Conclusion Contactless in vivo OCT scanning has shown to achieve high values of accuracy for RTD, supporting what has well been described for ex vivo OCT brain tumor scanning, complementing current intraoperative techniques and even exceeding them in accuracy, while not yet in applicability.
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Affiliation(s)
- Patrick Kuppler
- Department of Neurosurgery, University Medical Center Schleswig-Holstein, Luebeck, Germany
- *Correspondence: Patrick Kuppler,
| | | | | | - Sonja Spahr-Hess
- Department of Neurosurgery, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | | | - Christian Hagel
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Ralf Brinkmann
- Medical Laser Center Luebeck, Luebeck, Germany
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | - Robert Huber
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | - Volker Tronnier
- Department of Neurosurgery, University Medical Center Schleswig-Holstein, Luebeck, Germany
| | - Matteo Mario Bonsanto
- Department of Neurosurgery, University Medical Center Schleswig-Holstein, Luebeck, Germany
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16
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Wang N, Lee CY, Park HC, Nauen DW, Chaichana KL, Quinones-Hinojosa A, Bettegowda C, Li X. Deep learning-based optical coherence tomography image analysis of human brain cancer. BIOMEDICAL OPTICS EXPRESS 2023; 14:81-88. [PMID: 36698668 PMCID: PMC9842008 DOI: 10.1364/boe.477311] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Real-time intraoperative delineation of cancer and non-cancer brain tissues, especially in the eloquent cortex, is critical for thorough cancer resection, lengthening survival, and improving quality of life. Prior studies have established that thresholding optical attenuation values reveals cancer regions with high sensitivity and specificity. However, threshold of a single value disregards local information important to making more robust predictions. Hence, we propose deep convolutional neural networks (CNNs) trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features. Specifically, we adapt a deep ensemble model trained on 5,831 examples in a training dataset of 7 patients. We obtain 93.31% sensitivity and 97.04% specificity on a holdout set of 4 patients without the need for beam profile normalization using a reference phantom. The segmentation maps produced by parsing the OCT volume and tiling the outputs of our model are in excellent agreement with attenuation mapping-based methods. Our new approach for this important application has considerable implications for clinical translation.
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Affiliation(s)
- Nathan Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Cheng-Yu Lee
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Hyeon-Cheol Park
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - David W. Nauen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | | | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
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17
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Tan Z, Zhang Z, Yu K, Yang H, Liang H, Lu T, Ji Y, Chen J, He W, Chen Z, Mei Y, Shen XL. Integrin subunit alpha V is a potent prognostic biomarker associated with immune infiltration in lower-grade glioma. Front Neurol 2022; 13:964590. [PMID: 36388191 PMCID: PMC9642104 DOI: 10.3389/fneur.2022.964590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/15/2022] [Indexed: 09/30/2023] Open
Abstract
As a member of integrin receptor family, ITGAV (integrin subunit α V) is involved in a variety of cell biological processes and overexpressed in various cancers, which may be a potential prognostic factor. However, its prognostic value and potential function in lower-grade glioma (LGG) are still unclear, and in terms of immune infiltration, it has not been fully elucidated. Here, the expression preference, prognostic value, and clinical traits of ITGAV were investigated using The Cancer Genome Atlas database (n = 528) and the Chinese Glioma Genome Atlas dataset (n = 458). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and gene set enrichment analysis (GSEA) were used to explore the biological function of ITGAV. Using R package "ssGSEA" analysis, it was found thatthe ITGAV mRNA expression level showed intense correlation with tumor immunity, such as tumor-infiltrating immune cells and multiple immune-related genes. In addition, ITGAV is associated with some immune checkpoints and immune checkpoint blockade (ICB) and response to chemotherapy. and the expression of ITGAV protein in LGG patients was verified via immunohistochemistry (IHC). ITGAV expression was higher in LGG tissues than in normal tissues (P < 0.001) and multifactor analysis showed that ITGAV mRNA expression was an independent prognostic factor for LGG overall survival (OS; hazard ratio = 2.113, 95% confidence interval = 1.393-3.204, P < 0.001). GSEA showed that ITGAV expression was correlated with Inflammatory response, complement response, KRAS signal, and interferon response. ssGSEA results showed a positive correlation between ITGAV expression and Th2 cell infiltration level. ITGAV mRNA was overexpressed in LGG, and high ITGAV mRNA levels were found to be associated with poor protein expression and poor OS. ITGAV is therefore a potential biomarker for the diagnosis and prognosis of LGG and may be a potential immunotherapy target.
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Affiliation(s)
- Zilong Tan
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
- The Graduate Department, Jiangxi Medical College of Nanchang University Nanchang, Nanchang, China
| | - Zhe Zhang
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
- The Graduate Department, Jiangxi Medical College of Nanchang University Nanchang, Nanchang, China
| | - Kai Yu
- Department of Neurosurgery, People's Hospital of Wuhan University, Wuhan, China
| | - Huan Yang
- Department of Neurosurgery, Changde Hospital of Traditional Chinese Medicine, Changde, China
| | - Huaizhen Liang
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tianzhu Lu
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
| | - Yulong Ji
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
| | - Junjun Chen
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
| | - Wei He
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhen Chen
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuran Mei
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiao-Li Shen
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
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18
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Uribe-Cardenas R, Giantini-Larsen AM, Garton A, Juthani RG, Schwartz TH. Innovations in the Diagnosis and Surgical Management of Low-Grade Gliomas. World Neurosurg 2022; 166:321-327. [PMID: 36192864 DOI: 10.1016/j.wneu.2022.06.070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 12/15/2022]
Abstract
Low-grade gliomas are a broad category of tumors that can manifest at different stages of life. As a group, their prognosis has historically been considered to be favorable, and surgery is a mainstay of treatment. Advances in the molecular characterization of individual lesions has led to newer classification systems, a better understanding of the biological behavior of different neoplasms, and the identification of previously unrecognized entities. New prospective genetic and molecular data will help delineate better treatment paradigms and will continue to change the taxonomy of central nervous system tumors in the coming years. Advances in the field of radiomics will help predict the molecular profile of a particular tumor through noninvasive testing. Similarly, more precise methods of intraoperative tumor tissue analysis will aid surgical planning. Improved surgical outcomes propelled by novel surgical techniques and intraoperative adjuncts and emerging forms of medical treatment in the field of immunotherapy have enriched the management of these lesions. We review the contemporary management and innovations in the treatment of low-grade gliomas.
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Affiliation(s)
- Rafael Uribe-Cardenas
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Alexandra M Giantini-Larsen
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Andrew Garton
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Rupa Gopalan Juthani
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA.
| | - Theodore H Schwartz
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
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19
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Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images. Sci Rep 2022; 12:13995. [PMID: 35978040 PMCID: PMC9385745 DOI: 10.1038/s41598-022-18393-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/10/2022] [Indexed: 12/26/2022] Open
Abstract
The dominant consequence of irradiating biological systems is cellular damage, yet microvascular damage begins to assume an increasingly important role as the radiation dose levels increase. This is currently becoming more relevant in radiation medicine with its pivot towards higher-dose-per-fraction/fewer fractions treatment paradigm (e.g., stereotactic body radiotherapy (SBRT)). We have thus developed a 3D preclinical imaging platform based on speckle-variance optical coherence tomography (svOCT) for longitudinal monitoring of tumour microvascular radiation responses in vivo. Here we present an artificial intelligence (AI) approach to analyze the resultant microvascular data. In this initial study, we show that AI can successfully classify SBRT-relevant clinical radiation dose levels at multiple timepoints (t = 2–4 weeks) following irradiation (10 Gy and 30 Gy cohorts) based on induced changes in the detected microvascular networks. Practicality of the obtained results, challenges associated with modest number of animals, their successful mitigation via augmented data approaches, and advantages of using 3D deep learning methodologies, are discussed. Extension of this encouraging initial study to longitudinal AI-based time-series analysis for treatment outcome predictions at finer dose level gradations is envisioned.
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20
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Massalimova A, Timmermans M, Esfandiari H, Carrillo F, Laux CJ, Farshad M, Denis K, Fürnstahl P. Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review. Front Surg 2022; 9:952539. [PMID: 35990097 PMCID: PMC9381957 DOI: 10.3389/fsurg.2022.952539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.
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Affiliation(s)
- Aidana Massalimova
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
- Correspondence: Aidana Massalimova
| | - Maikel Timmermans
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Hooman Esfandiari
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Fabio Carrillo
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Christoph J. Laux
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Kathleen Denis
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
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21
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Lautman Z, Winetraub Y, Blacher E, Yu C, Terem I, Chibukhchyan A, Marshel JH, de la Zerda A. Intravital 3D visualization and segmentation of murine neural networks at micron resolution. Sci Rep 2022; 12:13130. [PMID: 35907928 PMCID: PMC9338956 DOI: 10.1038/s41598-022-14450-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 06/06/2022] [Indexed: 12/03/2022] Open
Abstract
Optical coherence tomography (OCT) allows label-free, micron-scale 3D imaging of biological tissues' fine structures with significant depth and large field-of-view. Here we introduce a novel OCT-based neuroimaging setting, accompanied by a feature segmentation algorithm, which enables rapid, accurate, and high-resolution in vivo imaging of 700 μm depth across the mouse cortex. Using a commercial OCT device, we demonstrate 3D reconstruction of microarchitectural elements through a cortical column. Our system is sensitive to structural and cellular changes at micron-scale resolution in vivo, such as those from injury or disease. Therefore, it can serve as a tool to visualize and quantify spatiotemporal brain elasticity patterns. This highly transformative and versatile platform allows accurate investigation of brain cellular architectural changes by quantifying features such as brain cell bodies' density, volume, and average distance to the nearest cell. Hence, it may assist in longitudinal studies of microstructural tissue alteration in aging, injury, or disease in a living rodent brain.
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Affiliation(s)
- Ziv Lautman
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA
| | - Yonatan Winetraub
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA
- Biophysics Program at Stanford, Stanford, CA, 94305, USA
- The Bio-X Program, Stanford, CA, 94305, USA
| | - Eran Blacher
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, 94305, USA
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus Givat-Ram, 9190401, Jerusalem, Israel
| | - Caroline Yu
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA
| | - Itamar Terem
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | | | - James H Marshel
- CNC Department, Stanford University, Stanford, CA, 94305, USA
| | - Adam de la Zerda
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA.
- Biophysics Program at Stanford, Stanford, CA, 94305, USA.
- The Bio-X Program, Stanford, CA, 94305, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
- The Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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22
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Jiang C, Bhattacharya A, Linzey JR, Joshi RS, Cha SJ, Srinivasan S, Alber D, Kondepudi A, Urias E, Pandian B, Al-Holou WN, Sullivan SE, Thompson BG, Heth JA, Freudiger CW, Khalsa SSS, Pacione DR, Golfinos JG, Camelo-Piragua S, Orringer DA, Lee H, Hollon TC. Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence. Neurosurgery 2022; 90:758-767. [PMID: 35343469 PMCID: PMC9514725 DOI: 10.1227/neu.0000000000001929] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 12/16/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources. OBJECTIVE To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence. METHODS We used a fiber laser-based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set. RESULTS SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images. CONCLUSION SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.
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Affiliation(s)
- Cheng Jiang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Joseph R. Linzey
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Rushikesh S. Joshi
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Sung Jik Cha
- School of Medicine, Western Michigan University, Kalamazoo, Michigan, USA
| | | | - Daniel Alber
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Akhil Kondepudi
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, Michigan, USA
| | - Esteban Urias
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Balaji Pandian
- School of Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Wajd N. Al-Holou
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Stephen E. Sullivan
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - B. Gregory Thompson
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Jason A. Heth
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | - Donato R. Pacione
- Department of Neurosurgery, NYU Langone Health, New York, New York, USA
| | - John G. Golfinos
- Department of Neurosurgery, NYU Langone Health, New York, New York, USA
| | | | - Daniel A. Orringer
- Department of Neurosurgery, NYU Langone Health, New York, New York, USA
- Department of Pathology, NYU Langone Health, New York, New York, USA
| | - Honglak Lee
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Todd C. Hollon
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
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23
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OCT-Guided Surgery for Gliomas: Current Concept and Future Perspectives. Diagnostics (Basel) 2022; 12:diagnostics12020335. [PMID: 35204427 PMCID: PMC8871129 DOI: 10.3390/diagnostics12020335] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 02/01/2023] Open
Abstract
Optical coherence tomography (OCT) has been recently suggested as a promising method to obtain in vivo and real-time high-resolution images of tissue structure in brain tumor surgery. This review focuses on the basics of OCT imaging, types of OCT images and currently suggested OCT scanner devices and the results of their application in neurosurgery. OCT can assist in achieving intraoperative precision identification of tumor infiltration within surrounding brain parenchyma by using qualitative or quantitative OCT image analysis of scanned tissue. OCT is able to identify tumorous tissue and blood vessels detection during stereotactic biopsy procedures. The combination of OCT with traditional imaging such as MRI, ultrasound and 5-ALA fluorescence has the potential to increase the safety and accuracy of the resection. OCT can improve the extent of resection by offering the direct visualization of tumor with cellular resolution when using microscopic OCT contact probes. The theranostic implementation of OCT as a part of intelligent optical diagnosis and automated lesion localization and ablation could achieve high precision, automation and intelligence in brain tumor surgery. We present this review for the increase of knowledge and formation of critical opinion in the field of OCT implementation in brain tumor surgery.
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24
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 143] [Impact Index Per Article: 71.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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25
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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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26
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Park HC, Li A, Guan H, Bettegowda C, Chaichana K, Quiñones-Hinojosa A, Li X. Minimizing OCT quantification error via a surface-tracking imaging probe. BIOMEDICAL OPTICS EXPRESS 2021; 12:3992-4002. [PMID: 34457394 PMCID: PMC8367274 DOI: 10.1364/boe.423233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/01/2021] [Accepted: 06/03/2021] [Indexed: 06/13/2023]
Abstract
OCT-based quantitative tissue optical properties imaging is a promising technique for intraoperative brain cancer assessment. The attenuation coefficient analysis relies on the depth-dependent OCT intensity profile, thus sensitive to tissue surface positions relative to the imaging beam focus. However, it is almost impossible to maintain a steady tissue surface during intraoperative imaging due to the patient's arterial pulsation and breathing, the operator's motion, and the complex tissue surface geometry of the surgical cavity. In this work, we developed an intraoperative OCT imaging probe with a surface-tracking function to minimize the quantification errors in optical attenuation due to the tissue surface position variations. A compact OCT imaging probe was designed and engineered to have a long working distance of ∼ 41 mm and a large field of view of 4 × 4 mm2 while keeping the probe diameter small (9 mm) to maximize clinical versatility. A piezo-based linear motor was integrated with the imaging probe and controlled based upon real-time feedback of tissue surface position inferred from OCT images. A GPU-assisted parallel processing algorithm was implemented, enabling detection and tracking of tissue surface in real-time and successfully suppressing more than 90% of the typical physiologically induced motion range. The surface-tracking intraoperative OCT imaging probe could maintain a steady beam focus inside the target tissue regardless of the surface geometry or physiological motions and enabled to obtain tissue optical attenuation reliably for assessing brain cancer margins in challenging intraoperative settings.
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Affiliation(s)
- Hyeon-Cheol Park
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21215, USA
| | - Ang Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21215, USA
| | - Honghua Guan
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Kaisorn Chaichana
- Department of Neurologic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21215, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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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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Plekhanov AA, Sirotkina MA, Sovetsky AA, Gubarkova EV, Kuznetsov SS, Matveyev AL, Matveev LA, Zagaynova EV, Gladkova ND, Zaitsev VY. Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography. Sci Rep 2020; 10:11781. [PMID: 32678175 PMCID: PMC7366713 DOI: 10.1038/s41598-020-68631-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 06/30/2020] [Indexed: 01/09/2023] Open
Abstract
We present a non-invasive (albeit contact) method based on Optical Coherence Elastography (OCE) enabling the in vivo segmentation of morphological tissue constituents, in particular, monitoring of morphological alterations during both tumor development and its response to therapies. The method uses compressional OCE to reconstruct tissue stiffness map as the first step. Then the OCE-image is divided into regions, for which the Young’s modulus (stiffness) falls in specific ranges corresponding to the morphological constituents to be discriminated. These stiffness ranges (characteristic "stiffness spectra") are initially determined by careful comparison of the "gold-standard" histological data and the OCE-based stiffness map for the corresponding tissue regions. After such pre-calibration, the results of morphological segmentation of OCE-images demonstrate a striking similarity with the histological results in terms of percentage of the segmented zones. To validate the sensitivity of the OCE-method and demonstrate its high correlation with conventional histological segmentation we present results obtained in vivo on a murine model of breast cancer in comparative experimental study of the efficacy of two antitumor chemotherapeutic drugs with different mechanisms of action. The new technique allowed in vivo monitoring and quantitative segmentation of (1) viable, (2) dystrophic, (3) necrotic tumor cells and (4) edema zones very similar to morphological segmentation of histological images. Numerous applications in other experimental/clinical areas requiring rapid, nearly real-time, quantitative assessment of tissue structure can be foreseen.
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Affiliation(s)
- Anton A Plekhanov
- Privolzhsky Research Medical University, Minin Square 10/1, Nizhny Novgorod, 603950, Russia
| | - Marina A Sirotkina
- Privolzhsky Research Medical University, Minin Square 10/1, Nizhny Novgorod, 603950, Russia.
| | - Alexander A Sovetsky
- Institute of Applied Physics, Russian Academy of Sciences, Ulyanov Street 46, Nizhny Novgorod, 603950, Russia
| | - Ekaterina V Gubarkova
- Privolzhsky Research Medical University, Minin Square 10/1, Nizhny Novgorod, 603950, Russia
| | - Sergey S Kuznetsov
- N.A. Semashko Nizhny Novgorod Regional Clinical Hospital, Rodionov Street 190, Nizhny Novgorod, 603126, Russia
| | - Alexander L Matveyev
- Institute of Applied Physics, Russian Academy of Sciences, Ulyanov Street 46, Nizhny Novgorod, 603950, Russia
| | - Lev A Matveev
- Institute of Applied Physics, Russian Academy of Sciences, Ulyanov Street 46, Nizhny Novgorod, 603950, Russia
| | - Elena V Zagaynova
- Privolzhsky Research Medical University, Minin Square 10/1, Nizhny Novgorod, 603950, Russia
| | - Natalia D Gladkova
- Privolzhsky Research Medical University, Minin Square 10/1, Nizhny Novgorod, 603950, Russia
| | - Vladimir Y Zaitsev
- Institute of Applied Physics, Russian Academy of Sciences, Ulyanov Street 46, Nizhny Novgorod, 603950, Russia
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29
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Gesperger J, Lichtenegger A, Roetzer T, Salas M, Eugui P, Harper DJ, Merkle CW, Augustin M, Kiesel B, Mercea PA, Widhalm G, Baumann B, Woehrer A. Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning. Cancers (Basel) 2020; 12:E1806. [PMID: 32640583 PMCID: PMC7408054 DOI: 10.3390/cancers12071806] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/26/2020] [Accepted: 07/01/2020] [Indexed: 11/16/2022] Open
Abstract
Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM provides high-resolution information of the inherent tissue scattering and absorption properties of tissue. We here explore quantitative attenuation coefficients derived from volumetric OCM intensity data and quantitative high-resolution 5-ALA fluorescence as potential biomarkers for tissue malignancy including otherwise difficult-to-assess low-grade glioma. We validate our findings against the gold standard histology and use attenuation and fluorescence intensity measures to differentiate between tumor core, infiltrative zone and adjacent brain tissue. Using large field-of-view scans acquired by a near-infrared swept-source optical coherence tomography setup, we provide initial assessments of tumor heterogeneity. Finally, we use cross-sectional OCM images to train a convolutional neural network that discriminates tumor from non-tumor tissue with an accuracy of 97%. Collectively, the present hybrid approach offers potential to translate into an in vivo imaging setup for substantially improved intraoperative guidance of brain tumor surgeries.
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Affiliation(s)
- Johanna Gesperger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria; (T.R.); (A.W.)
| | - Antonia Lichtenegger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Thomas Roetzer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria; (T.R.); (A.W.)
| | - Matthias Salas
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Pablo Eugui
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Danielle J. Harper
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Conrad W. Merkle
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Marco Augustin
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria; (B.K.); (P.A.M.)
| | - Petra A. Mercea
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria; (B.K.); (P.A.M.)
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria; (B.K.); (P.A.M.)
| | - Bernhard Baumann
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (J.G.); (A.L.); (M.S.); (P.E.); (D.J.H.); (C.W.M.); (M.A.)
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria; (T.R.); (A.W.)
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