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Takamatsu T, Fukushima R, Sato K, Umezawa M, Yokota H, Soga K, Hernandez-Guedes A, Callico GM, Takemura H. Development of a visible to 1600 nm hyperspectral imaging rigid-scope system using supercontinuum light and an acousto-optic tunable filter. OPTICS EXPRESS 2024; 32:16090-16102. [PMID: 38859246 DOI: 10.1364/oe.515747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/22/2024] [Indexed: 06/12/2024]
Abstract
In this study, we developed a rigid-scope system that can perform hyperspectral imaging (HSI) between visible and 1600 nm wavelengths using a supercontinuum light source and an acousto-optic tunable filter to emit specific wavelengths. The system optical performance was verified, and the classification ability was investigated. Consequently, it was demonstrated that HSI (490-1600 nm) could be performed. In addition, seven different targets could be classified by the neural network with an accuracy of 99.6%, recall of 93.7%, and specificity of 99.1% when the wavelength range of over 1000 nm (OTN) was extracted from HSI data as train data.
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Mori A, Umezawa M, Okubo K, Kamiya T, Kamimura M, Ohtani N, Soga K. Visualization of hydrocarbon chain length and degree of saturation of fatty acids in mouse livers by combining near-infrared hyperspectral imaging and machine learning. Sci Rep 2023; 13:20555. [PMID: 37996472 PMCID: PMC10667523 DOI: 10.1038/s41598-023-47565-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023] Open
Abstract
Fatty acids play various physiological roles owing to their diverse structural characteristics, such as hydrocarbon chain length (HCL) and degree of saturation (DS). Although the distribution of fatty acids in biological tissues is associated with lipid metabolism, in situ imaging tools are still lacking for HCL and DS. Here, we introduce a framework of near-infrared (1000-1400 nm) hyperspectral label-free imaging with machine learning analysis of the fatty acid HCL and DS distribution in the liver at each pixel, in addition to the previously reported total lipid content. The training data of 16 typical fatty acids were obtained by gas chromatography from liver samples of mice fed with various diets. A two-dimensional mapping of these two parameters was successfully performed. Furthermore, the HCL/DS plot exhibited characteristic clustering among the different diet groups. Visualization of fatty acid distribution would provide insights for revealing the pathophysiological conditions of liver diseases and metabolism.
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Affiliation(s)
- Akino Mori
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Masakazu Umezawa
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan.
| | - Kyohei Okubo
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Tomonori Kamiya
- Department of Pathophysiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Masao Kamimura
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Naoko Ohtani
- Department of Pathophysiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Kohei Soga
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan.
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Mitsui T, Mori A, Takamatsu T, Kadota T, Sato K, Fukushima R, Okubo K, Umezawa M, Takemura H, Yokota H, Kuwata T, Kinoshita T, Ikematsu H, Yano T, Maeda S, Soga K. Evaluating the identification of the extent of gastric cancer by over-1000 nm near-infrared hyperspectral imaging using surgical specimens. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:086001. [PMID: 37614567 PMCID: PMC10442660 DOI: 10.1117/1.jbo.28.8.086001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/22/2023] [Accepted: 08/02/2023] [Indexed: 08/25/2023]
Abstract
Significance Determining the extent of gastric cancer (GC) is necessary for evaluating the gastrectomy margin for GC. Additionally, determining the extent of the GC that is not exposed to the mucosal surface remains difficult. However, near-infrared (NIR) can penetrate mucosal tissues highly efficiently. Aim We investigated the ability of near-infrared hyperspectral imaging (NIR-HSI) to identify GC areas, including exposed and unexposed using surgical specimens, and explored the identifiable characteristics of the GC. Approach Our study examined 10 patients with diagnosed GC who underwent surgery between 2020 and 2021. Specimen images were captured using NIR-HSI. For the specimens, the exposed area was defined as an area wherein the cancer was exposed on the surface, the unexposed area as an area wherein the cancer was present although the surface was covered by normal tissue, and the normal area as an area wherein the cancer was absent. We estimated the GC (including the exposed and unexposed areas) and normal areas using a support vector machine, which is a machine-learning method for classification. The prediction accuracy of the GC region in every area and normal region was evaluated. Additionally, the tumor thicknesses of the GC were pathologically measured, and their differences in identifiable and unidentifiable areas were compared using NIR-HSI. Results The average prediction accuracy of the GC regions combined with both areas was 77.2%; with exposed and unexposed areas was 79.7% and 68.5%, respectively; and with normal regions was 79.7%. Additionally, the areas identified as cancerous had a tumor thickness of > 2 mm . Conclusions NIR-HSI identified the GC regions with high rates. As a feature, the exposed and unexposed areas with tumor thicknesses of > 2 mm were identified using NIR-HSI.
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Affiliation(s)
- Tomohiro Mitsui
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
- Yokohama City University Graduate School of Medicine, Department of Gastroenterology, Yokohama, Japan
| | - Akino Mori
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Toshihiro Takamatsu
- National Cancer Center, Exploratory Oncology Research and Clinical Trial Center, Chiba, Japan
- Tokyo University of Science, Research Institute for Biomedical Sciences, Chiba, Japan
| | - Tomohiro Kadota
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
| | - Konosuke Sato
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Ryodai Fukushima
- Tokyo University of Science, Department of Mechanical Engineering, Chiba, Japan
| | - Kyohei Okubo
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Masakazu Umezawa
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Hiroshi Takemura
- Tokyo University of Science, Research Institute for Biomedical Sciences, Chiba, Japan
- Tokyo University of Science, Department of Mechanical Engineering, Chiba, Japan
| | - Hideo Yokota
- RIKEN Center for Advanced Photonics, Saitama, Japan
| | - Takeshi Kuwata
- National Cancer Center Hospital East, Department of Pathology and Clinical Laboratories, Kashiwa, Japan
| | - Takahiro Kinoshita
- National Cancer Center Hospital East, Department of Gastric Surgery, Chiba, Japan
| | - Hiroaki Ikematsu
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
- National Cancer Center, Exploratory Oncology Research and Clinical Trial Center, Chiba, Japan
| | - Tomonori Yano
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
| | - Shin Maeda
- Yokohama City University Graduate School of Medicine, Department of Gastroenterology, Yokohama, Japan
| | - Kohei Soga
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
- Tokyo University of Science, Research Institute for Biomedical Sciences, Chiba, Japan
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Ding X, Min Y, Wang C, Zhang Q. Near-Infrared Emission Material with Superlong Afterglow Performance and Its Multifunctional Applications. Inorg Chem 2023; 62:1686-1696. [PMID: 36642952 DOI: 10.1021/acs.inorgchem.2c04112] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
A new near-infrared (NIR) emission material, BaSi1.5Ge2.5O9:Cr3+, with outstanding long afterglow properties is designed and synthesized successfully. Its structure, photoluminescence, and long afterglow emission features are studied in detail. Cr3+ occupies six-coordinated Ba2+ and Ge4+ sites in this system, which can emit characteristic broadband NIR light in the range of 700-1200 nm, and it has a long afterglow emission of more than 10 h. We calculated the band gap of the host at about 4.1 eV, the energy level distribution after Cr3+ doping, and the distribution of oxygen vacancies through density functional theory simulation, which theoretically proved the characteristic transition of Cr3+ and that the oxygen vacancies are crucial to form the defect energy levels. This is corroborated with the reflectance spectra, three-dimensional thermoluminescence spectra, and electron spin resonance (ESR) spectra. The oxygen-deficient environment favors the formation of oxygen vacancy defects and prevents the oxidation of Cr3+ to Cr6+, which are the key points for the luminescence and afterglow properties. A mechanistic model of defect formation and electron trapping and transport processes is successfully established. Finally, its multifunctional applications in information anticounterfeiting encryption, biological tissue penetration, and internal nondestructive testing and imaging are demonstrated.
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Affiliation(s)
- Xin Ding
- College of Materials, Science and Engineering, Qingdao University, Ningxia Road No. 308, Qingdao266071, China
| | - Yu Min
- College of Materials, Science and Engineering, Qingdao University, Ningxia Road No. 308, Qingdao266071, China
| | - Chang Wang
- College of Materials, Science and Engineering, Qingdao University, Ningxia Road No. 308, Qingdao266071, China
| | - Qiang Zhang
- Department of Materials Science, School of Materials & Energy, National & Local Joint Engineering Laboratory for Optical Conversion Materials and Technology, Lanzhou University, Tianshui South Road No. 222, Lanzhou, Gansu730000, PR China
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Martinez-Vega B, Tkachenko M, Matkabi M, Ortega S, Fabelo H, Balea-Fernandez F, La Salvia M, Torti E, Leporati F, Callico GM, Chalopin C. Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8917. [PMID: 36433516 PMCID: PMC9693077 DOI: 10.3390/s22228917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling.
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Affiliation(s)
- Beatriz Martinez-Vega
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Mariia Tkachenko
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), University of Leipzig, 04105 Leipzig, Germany
| | - Marianne Matkabi
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
- Department of Electrical Engineering, Mechanical Engineering and Industrial Engineering, Anhalt University of Applied Science Anhalt, 06366 Köthen, Germany
| | - Samuel Ortega
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Nofima, Norwegian Institute of Food Fisheries and Aquaculture Research, NO-9291 Tromsø, Norway
| | - Himar Fabelo
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Fundacion Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), 35019 Las Palmas de Gran Canaria, Spain
| | - Francisco Balea-Fernandez
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Department of Psychology, Sociology and Social Work, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Marco La Salvia
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy
| | - Emanuele Torti
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy
| | - Francesco Leporati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy
| | - Gustavo M. Callico
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Claire Chalopin
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
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Three Dimensional Lifetime-Multiplex Tomography Based on Time-Gated Capturing of Near-Infrared Fluorescence Images. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
We report a computed tomography (CT) technique for mapping near-infrared fluorescence (NIRF) lifetime as a multiplex three-dimensional (3D) imaging method, using a conventional NIR camera. This method is achieved by using a time-gated system composed of a pulsed laser and an NIR camera synchronized with a rotatable sample stage for NIRF-CT imaging. The fluorescence lifetimes in microsecond-order of lanthanides were mapped on reconstructed cross-sectional and 3D images, via back-projection of two-dimensional projected images acquired from multiple angles at each time point showing fluorescence decay. A method to select slopes (the observed decay rates in time-gated imaging) used for the lifetime calculation, termed as the slope comparison method, was developed for the accurate calculation of each pixel, resulting in reduction of image acquisition time. Time-gated NIRF-CT provides a novel choice for multiplex 3D observation of deep tissues in biology.
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Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. THE VISUAL COMPUTER 2021; 39:2207-2222. [PMID: 34744231 PMCID: PMC8557108 DOI: 10.1007/s00371-021-02322-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/30/2021] [Indexed: 05/07/2023]
Abstract
With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Jixiang Zhang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
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Goto O, Kaise M, Iwakiri K. Advancements in the Diagnosis of Gastric Subepithelial Tumors. Gut Liver 2021; 16:321-330. [PMID: 34456187 PMCID: PMC9099397 DOI: 10.5009/gnl210242] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/29/2021] [Accepted: 07/06/2021] [Indexed: 11/04/2022] Open
Abstract
A diagnosis of subepithelial tumors (SETs) is sometimes difficult due to the existence of overlying mucosa on the lesions, which hampers optical diagnosis by conventional endoscopy and tissue sampling with standard biopsy forceps. Imaging modalities, by using computed tomography and endoscopic ultrasonography (EUS) are mandatory to noninvasively collect the target's information and to opt candidates for further evaluation. Particularly, EUS is an indispensable diagnostic modality for assessing the lesions precisely and evaluating the possibility of malignancy. The diagnostic ability of EUS appears increased by the combined use of contrast-enhancement or elastography. Histology is the gold standard for obtaining the final diagnosis. Tissue sampling requires special techniques to break the mucosal barrier. Although EUS-guided fine-needle aspiration (EUS-FNA) is commonly applied, mucosal cutting biopsy and mucosal incision-assisted biopsy are comparable methods to definitively obtain tissues from the exposed surface of lesions and seem more useful than EUS-FNA for small SETs. Recent advancements in artificial intelligence (AI) have a potential to drastically change the diagnostic strategy for SETs. Development and establishment of noninvasive methods including AI-assisted diagnosis are expected to provide an alternative to invasive, histological diagnosis.
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Affiliation(s)
- Osamu Goto
- Department of Gastroenterology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Mitsuru Kaise
- Department of Gastroenterology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Katsuhiko Iwakiri
- Department of Gastroenterology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
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9
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Takamatsu T, Kitagawa Y, Akimoto K, Iwanami R, Endo Y, Takashima K, Okubo K, Umezawa M, Kuwata T, Sato D, Kadota T, Mitsui T, Ikematsu H, Yokota H, Soga K, Takemura H. Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging. SENSORS (BASEL, SWITZERLAND) 2021; 21:2649. [PMID: 33918935 PMCID: PMC8069262 DOI: 10.3390/s21082649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/01/2021] [Accepted: 04/06/2021] [Indexed: 01/17/2023]
Abstract
In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000-1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.
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Affiliation(s)
- Toshihiro Takamatsu
- Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba 277-8577, Japan;
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.S.); (H.T.)
| | - Yuichi Kitagawa
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Kohei Akimoto
- Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.A.); (Y.E.)
| | - Ren Iwanami
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Yuto Endo
- Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.A.); (Y.E.)
| | - Kenji Takashima
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Kyohei Okubo
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Masakazu Umezawa
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Takeshi Kuwata
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan;
| | - Daiki Sato
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Tomohiro Kadota
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Tomohiro Mitsui
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Hiroaki Ikematsu
- Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba 277-8577, Japan;
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Hideo Yokota
- RIKEN Center for Advanced Photonics, Wako, Saitama 351-0198, Japan;
| | - Kohei Soga
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.S.); (H.T.)
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Hiroshi Takemura
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.S.); (H.T.)
- Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.A.); (Y.E.)
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