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Ninomiya K, Arimura H, Yoshitake T, Hirose TA, Shioyama Y. Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis. PLoS One 2022; 17:e0263292. [PMID: 35100322 PMCID: PMC8803154 DOI: 10.1371/journal.pone.0263292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 01/18/2022] [Indexed: 12/25/2022] Open
Abstract
Objectives We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer. Methods A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP–(grade 1)) and 30 cases (8 RP+ and 22 RP–) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models. Results The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively. Conclusion This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.
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Arimura H, Kodama T, Urakami A, Kamezawa H, Hirose TA, Ninomiya K. [6. Imaging Biopsy for Assisting Cancer Precision Therapy -Information Extracted from Radiomics]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:219-224. [PMID: 35185102 DOI: 10.6009/jjrt.780213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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Okumura T, Azuma T, Bennett DA, Caradonna P, Chiu I, Doriese WB, Durkin MS, Fowler JW, Gard JD, Hashimoto T, Hayakawa R, Hilton GC, Ichinohe Y, Indelicato P, Isobe T, Kanda S, Kato D, Katsuragawa M, Kawamura N, Kino Y, Kubo MK, Mine K, Miyake Y, Morgan KM, Ninomiya K, Noda H, O'Neil GC, Okada S, Okutsu K, Osawa T, Paul N, Reintsema CD, Schmidt DR, Shimomura K, Strasser P, Suda H, Swetz DS, Takahashi T, Takeda S, Takeshita S, Tampo M, Tatsuno H, Tong XM, Ueno Y, Ullom JN, Watanabe S, Yamada S. Deexcitation Dynamics of Muonic Atoms Revealed by High-Precision Spectroscopy of Electronic K X Rays. PHYSICAL REVIEW LETTERS 2021; 127:053001. [PMID: 34397250 DOI: 10.1103/physrevlett.127.053001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
We observed electronic K x rays emitted from muonic iron atoms using superconducting transition-edge sensor microcalorimeters. The energy resolution of 5.2 eV in FWHM allowed us to observe the asymmetric broad profile of the electronic characteristic Kα and Kβ x rays together with the hypersatellite K^{h}α x rays around 6 keV. This signature reflects the time-dependent screening of the nuclear charge by the negative muon and the L-shell electrons, accompanied by electron side feeding. Assisted by a simulation, these data clearly reveal the electronic K- and L-shell hole production and their temporal evolution on the 10-20 fs scale during the muon cascade process.
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Ninomiya K, Arimura H, Chan WY, Tanaka K, Mizuno S, Muhammad Gowdh NF, Yaakup NA, Liam CK, Chai CS, Ng KH. Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers. PLoS One 2021; 16:e0244354. [PMID: 33428651 PMCID: PMC7799813 DOI: 10.1371/journal.pone.0244354] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 12/09/2020] [Indexed: 12/24/2022] Open
Abstract
Objectives To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). Materials and methods Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers’ scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. Results The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). Conclusion The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
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Le QC, Arimura H, Ninomiya K, Kabata Y. Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients. Sci Rep 2020; 10:21301. [PMID: 33277570 PMCID: PMC7718925 DOI: 10.1038/s41598-020-78338-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/23/2020] [Indexed: 12/23/2022] Open
Abstract
This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10-2, 1.85 [Formula: see text] 10-2, 3.17 [Formula: see text] 10-2, and 1.87 [Formula: see text] 10-3, respectively. When the features were integrated with clinical variables, the p values of conventional, index, and combined features were 3.53 [Formula: see text] 10-3, 1.28 [Formula: see text] 10-3, and 1.45 [Formula: see text] 10-3, respectively. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients.
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Fitri LA, Haryanto F, Arimura H, YunHao C, Ninomiya K, Nakano R, Haekal M, Warty Y, Fauzi U. Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network. Phys Med 2020; 78:201-208. [DOI: 10.1016/j.ejmp.2020.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/02/2020] [Accepted: 09/03/2020] [Indexed: 10/23/2022] Open
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Kai Y, Arimura H, Ninomiya K, Saito T, Shimohigashi Y, Kuraoka A, Maruyama M, Toya R, Oya N. Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy. JOURNAL OF RADIATION RESEARCH 2020; 61:285-297. [PMID: 31994702 PMCID: PMC7246080 DOI: 10.1093/jrr/rrz105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 10/26/2019] [Accepted: 01/10/2020] [Indexed: 06/10/2023]
Abstract
The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (CTV) shifts, with small errors. The pCT and daily CBCT images of 20 patients with prostate cancer were selected. The first 10 patients were employed for the development, and the second 10 patients for a validation test. The CTV position errors between the pCT and CBCT images were determined as reference CTV shifts (teacher data) after an automated bone-based registration. The anatomical features associated with rectum, bladder and prostate were calculated from the pCT and CBCT images. The features were fed as the input with the teacher data into five MLAs, i.e. three types of artificial neural networks, support vector regression (SVR) and random forests. Since the CTV shifts along the left-right direction were negligible, the MLAs were developed along the superior-inferior and anterior-posterior directions. The proposed framework was evaluated from the residual errors between the reference and predicted CTV shifts. In the validation test, the mean residual error with its standard deviation was 1.01 ± 1.09 mm in SVR using only one feature (one click), which was associated with positional difference of the upper rectal wall. The results suggested that MLAs with anatomical features could be useful in prediction of CTV shifts for prostate radiotherapy.
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Hossain A, Arimura H, Kinoshita F, Ninomiya K, Watanabe S, Imada K, Koyanagi R, Oda Y. Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis. Prostate 2020; 80:291-302. [PMID: 31868968 DOI: 10.1002/pros.23943] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 12/06/2019] [Indexed: 11/06/2022]
Abstract
BACKGROUND There is a low reproducibility of the Gleason scores that determine the grade group of prostate cancer given the intra- and interobserver variability among pathologists. This study aimed to develop an automated approach for estimating prostate cancer grade groups based on features obtained from histological image analysis. METHODS Fifty-nine patients who underwent radical prostatectomy were selected under the approval of the institutional review board of our university hospital. For estimation, we followed the grade group criteria provided by the International Society of Urological Pathology in 2014. One hundred eight specimen slides obtained from the patients were digitized to extract 110 regions of interest (ROI) from hematoxylin and eosin-stained histological images using a digital whole slide scanner at ×20 magnification with a pixel size of 0.4 μm. Each color pixel value in the ROI was decomposed into six intensities corresponding to the RGB (red, green, and blue) and HSV (hue, saturation, and value) color models. Image features were extracted by histological image analysis, obtaining 54 features from the ROI based on histogram and texture analyses in the six types of decomposed histological images. Then, 40 representative features were selected from the 324 histological image features based on statistically significant differences (P < .05) between the mean image feature values for high (≥3, Gleason score ≥4 + 3) and low (≤2, Gleason score ≤3 + 4) grade groups. The relationship between grade groups and the most representative image feature (ie, complexity) was approximated using regression to estimate real-number grade groups defined by continuous numerical grading. Finally, the grade groups were expressed as the conventional grade groups (ie, integers from 1 to 5) using a piecewise step function. RESULTS The grade groups were correctly estimated by the proposed approach without errors on training (70 ROIs) and validation (40 ROIs) data. CONCLUSIONS Our results suggest that the proposed approach may support pathologists during the evaluation of grade groups for prostate cancer, thus mitigating intra- and interobserver variability.
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Ninomiya K, Arimura H. Homological radiomics analysis for prognostic prediction in lung cancer patients. Phys Med 2019; 69:90-100. [PMID: 31855844 DOI: 10.1016/j.ejmp.2019.11.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 11/27/2019] [Accepted: 11/29/2019] [Indexed: 10/25/2022] Open
Abstract
PURPOSE This study explored a novel homological analysis method for prognostic prediction in lung cancer patients. MATERIALS AND METHODS The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan-Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database. RESULTS For the training dataset, the p-values between the two survival curves were 6.7 × 10-6 (HF), 5.9 × 10-3 (WF), 7.4 × 10-6 (HWF), and 1.1 × 10-3 (DL). The p-values for the validation dataset were 3.4 × 10-5 (HF), 6.7 × 10-1 (WF), 1.7 × 10-7 (HWF), and 1.2 × 10-1 (DL). CONCLUSION This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.
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Yokoyama T, Ninomiya K, Oze I, Hata T, Tanaka A, Bessho A, Hosokawa S, Kuyama S, Kudo K, Kozuki T, Harada D, Yasugi M, Murakami T, Nakanishi M, Takigawa N, Katsui K, Maeda Y, Hotta K, Kiura K. A randomized trial of sodium alginate prevention of radiation-induced esophagitis in patients with locally advanced NSCLC receiving concurrent chemoradiotherapy: OLCSG1401. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz265.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Arimura H, Soufi M, Kamezawa H, Ninomiya K, Yamada M. Radiomics with artificial intelligence for precision medicine in radiation therapy. JOURNAL OF RADIATION RESEARCH 2019; 60:150-157. [PMID: 30247662 PMCID: PMC6373667 DOI: 10.1093/jrr/rry077] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 07/21/2018] [Indexed: 05/27/2023]
Abstract
Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
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Arimura H, Soufi M, Ninomiya K, Kamezawa H, Yamada M. Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis. Radiol Phys Technol 2018; 11:365-374. [PMID: 30374837 DOI: 10.1007/s12194-018-0486-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 10/14/2018] [Accepted: 10/17/2018] [Indexed: 12/22/2022]
Abstract
Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer's "opinion" derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients' prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are "image manipulation". However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.
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Kubo T, Watanabe H, Ninomiya K, Kudo K, Minami D, Murakami E, Ochi N, Ninomiya T, Harada D, Yasugi M, Ichihara E, Ohashi K, Fujiwara K, Hotta K, Tabata M, Maeda Y, Kiura K. Immune checkpoint inhibitor efficacy and safety in elderly non-small cell lung cancer patients. Ann Oncol 2018. [DOI: 10.1093/annonc/mdy292.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Okuno T, Koseki K, Nakanishi T, Ninomiya K, Tanaka T, Sato Y, Osanai A, Sato K, Koike H, Yahagi K, Komiyama K, Aoki J, Yokozuka M, Miura S, Tanabe K. P1669Prognostic impact of computed tomography-derived abdominal fat area in patients undergoing transcatheter aortic valve implantation. Eur Heart J 2018. [DOI: 10.1093/eurheartj/ehy565.p1669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Okuno T, Koseki K, Nakanishi T, Ninomiya K, Tanaka T, Sato Y, Osanai A, Sato K, Koike H, Yahagi K, Komiyama K, Aoki J, Yokozuka M, Miura S, Tanabe K. P1673Impact of objective nutritional indexes on one-year clinical outcomes after transcatheter aortic valve implanation. Eur Heart J 2018. [DOI: 10.1093/eurheartj/ehy565.p1673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Bessho A, Ochi N, Kuyama S, Umeno T, Ikeda G, Harada D, Nogami N, Ninomiya K, Kishino D, Chikamori K, Fujimoto N, Hotta K, Takigawa N, Kiura K. A phase II trial of carboplatin plus S-1 for elderly patients with advanced non-small cell lung cancer with wild-type EGFR: The Okayama Lung Cancer Study Group Trial 1202 (OLCSG1202). Ann Oncol 2017. [DOI: 10.1093/annonc/mdx671.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Harada D, Kozuki T, Nogami N, Hotta K, Aoe K, Ohashi K, Ninomiya K, Hirata T, Hinotsu S, Toyooka S, Kiura K. MA 07.11 A Phase II Study of Trastuzumab Emtansine in HER2-positive Non-Small-Cell-Lung Cancer. J Thorac Oncol 2017. [DOI: 10.1016/j.jtho.2017.09.511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Okochi K, Murakami S, Ninomiya K, Kaneko M. Australia Antigen, Transfusion and Hepatitis. Vox Sang 2017. [DOI: 10.1159/000465919] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Ninomiya K, Inagaki M, Kubo MK, Nagatomo T, Higemoto W, Kawamura N, Strasser P, Shimomura K, Miyake Y, Sakamoto S, Shinohara A, Saito T. Negative muon induced elemental analysis by muonic X-ray and prompt gamma-ray measurements. J Radioanal Nucl Chem 2016. [DOI: 10.1007/s10967-016-4772-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Kikko T, Ishizaki D, Ninomiya K, Kai Y, Fujioka Y. Diel patterns of larval drift of honmoroko Gnathopogon caerulescens in an inlet of Ibanaiko Lagoon, Lake Biwa, Japan. JOURNAL OF FISH BIOLOGY 2015; 86:409-415. [PMID: 25430054 DOI: 10.1111/jfb.12570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Accepted: 09/28/2014] [Indexed: 06/04/2023]
Abstract
Diel drift patterns of larvae of the endangered cyprinid Gnathopogon caerelescens in an inlet of the Ibanaiko Lagoon, connected to Lake Biwa in Japan, were assessed in April 2012. Peak occurrence of yolk-sac larvae was within a few hours after dark. Drift of newly hatched larvae is considered to be an important biological mechanism that ensures larval dispersal and recruitment from the inlets (spawning grounds) to the lagoon which functions as a nursery ground.
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Yashima H, Sekimoto S, Ninomiya K, Kasamatsu Y, Shima T, Takahashi N, Shinohara A, Matsumura H, Satoh D, Iwamoto Y, Hagiwara M, Nishiizumi K, Caffee MW, Shibata S. Measurements of the neutron activation cross sections for Bi and Co at 386 MeV. RADIATION PROTECTION DOSIMETRY 2014; 161:139-143. [PMID: 24368868 DOI: 10.1093/rpd/nct334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Neutron activation cross sections for Bi and Co at 386 MeV were measured by activation method. A quasi-monoenergetic neutron beam was produced using the (7)Li(p,n) reaction. The energy spectrum of these neutrons has a high-energy peak (386 MeV) and a low-energy tail. Two neutron beams, 0° and 25° from the proton beam axis, were used for sample irradiation, enabling a correction for the contribution of the low-energy neutrons. The neutron-induced activation cross sections were estimated by subtracting the reaction rates of irradiated samples for 25° irradiation from those of 0° irradiation. The measured cross sections were compared with the findings of other studies, evaluated in relation to nuclear data files and the calculated data by Particle and Heavy Ion Transport code System code.
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Yoshida G, Ninomiya K, Ito TU, Higemoto W, Nagatomo T, Strasser P, Kawamura N, Shimomura K, Miyake Y, Miura T, Kubo KM, Shinohara A. Muon capture probability of carbon and oxygen for CO, CO2, and COS under low-pressure gas conditions. J Radioanal Nucl Chem 2014. [DOI: 10.1007/s10967-014-3602-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Murata J, Baba H, Behr J, Iguri T, Ikeda M, Kawamura H, Kishi R, Levy C, Nakaya Y, Narikawa R, Ninomiya K, Onishi J, Openshaw R, Pearson M, Seitaibashi E, Saiba S, Tanaka S, Tanuma R, Totsuka Y, Toyoda T. T-Violation experiment at TRIUMF-ISAC using polarized 8Li. EPJ WEB OF CONFERENCES 2014. [DOI: 10.1051/epjconf/20146605017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Inoue H, Katurahara M, Hamada Y, Ninomiya K, Tano S, Takayama R, Nojiri K, Tameda M, Horiki N, Takei Y. Hemosuccus pancreaticus caused by in situ carcinoma of the pancreas. Endoscopy 2013; 44 Suppl 2 UCTN:E336-7. [PMID: 23012009 DOI: 10.1055/s-0032-1309863] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
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Tano S, Tanaka K, Iguchi T, Nishikawa K, Takayama R, Ninomiya K, Inoue H, Katsurahara M, Horiki N, Takei Y. Large retention cyst with chondromatous metaplasia in the esophagus. Endoscopy 2011; 43 Suppl 2 UCTN:E262-3. [PMID: 21837606 DOI: 10.1055/s-0030-1256531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
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