Neuro Group

Suzuki A, Kashiwagi N, Doi H, Ishii K, Doi K, Kitano M, Kozuka T, Hyodo T, Tsurusaki M, Yagyu Y, Nakanishi K. Patterns of bone metastases from head and neck squamous cell carcinoma. Auris Nasus Larynx. 2020 Apr;47(2):262-7.

Breast Group

Tokuda Y, Yanagawa M, Minamitani K, Naoi Y, Noguchi S, Tomiyama N. Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study. Medicine. 2020 Apr; 99(16):e19664.

Chest Group

Hata A, Yanagawa M, Yoshida Y, Miyata T, Tsubamoto M, Honda O, Tomiyama N. Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation. AJR Am J Roentgenol. 2020 Dec;215(6):1321-1328.

Hida T, Nishino M, Hino T, Lu J, Putman RK, Gudmundsson EF, Araki T, Valtchinov VI, Honda O, Yanagawa M, Yamada Y, Hata A, Jinzaki M, Tomiyama N, Honda H, Estepar RSJ, Washko GR, Johkoh T, Christiani DC, Lynch DA, Gudnason V, Gudmundsson G, Hunninghake GM, Hatabu H. Traction Bronchiectasis/Bronchiolectasis is Associated with Interstitial Lung Abnormality Mortality. Eur J Radiol. 2020 Aug;129:109073.

Kikuchi N, Yanagawa M, Enchi Y, Nakayama A, Yoshida Y, Miyata T, Hata A, Tsubamoto M, Honda O, Tomiyama N. The effect of the reconstruction algorithm for the pulmonary nodule detection under the metal artifact caused by a pacemaker. Medicine (Baltimore). 2020 Jun 12;99(24):e20579.

Kume M, Nakagawa Y, Kiyohara E, Arase N, Wataya-K M, Yaga M, Yanagawa M, Fujimoto M. A case of zonisamide-induced toxic epidermal necrolysis with acute respiratory failure. Allergol Int. 2020 Oct;69(4):642-644.

Kuriyama K, Yanagawa M. CT Diagnosis of Lung Adenocarcinoma: Radiologic-Pathologic Correlation and Growth Rate. Radiology. 2020 Oct;297(1):199-200.

Miyata T, Yanagawa M, Hata A, Honda O, Yoshida Y, Kikuchi N, Tsubamoto M, Tsukagoshi S, Uranishi A, Tomiyama N. Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT. Eur Radiol. 2020 Jun;30(6):3324-3333.

Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, Schluger NW, Volpi A, Yim JJ, Martin IBK, Anderson DJ, Kong C, Altes T, Bush A, Desai SR, Goldin J, Goo JM, Humbert M, Inoue Y, Kauczor HU, Luo F, Mazzone PJ, Prokop M, Remy-Jardin M, Richeldi L, Schaefer-Prokop CM, Tomiyama N, Wells AU, Leung AN. The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society. Chest. 2020 Jul;158(1):106-116.

Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, Schluger NW, Volpi A, Yim JJ, Martin IBK, Anderson DJ, Kong C, Altes T, Bush A, Desai SR, Goldin O, Goo JM, Humbert M, Inoue Y, Kauczor HU, Luo F, Mazzone PJ, Prokop M, Remy-Jardin M, Richeldi L, Schaefer-Prokop CM, Tomiyama N, Wells AU, Leung AN. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology. 2020 Jul;296(1):172-180.

Tokuda Y, Yanagawa M, Minamitani K, Naoi Y, Noguchi S, Tomiyama N. Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study. Medicine (Baltimore). 2020 Apr;99(16):e19664.

Tomiyama N, Yamada K, Watanabe Y, Imai Y. The Fourth Asian Radiology Summit: Current Projects of the Radiological Societies in Asia. Jpn J Radiol; 38(5):387-390. 2020

Tomiyama N.　Approach to the prevascular mass. Mediastinum 2019; 1-4 (第24号同門会誌 未掲載分).

Tsubamoto M, Hata A, Yanagawa M, Honda O, Miyata T, Yoshida Y, Nakayama A, Kikuchi N, Uranishi A, Tsukagoshi S, Watanabe Y, Tomiyama N. Ultra high-resolution computed tomography with 1024-matrix: Comparison with 512-matrix for the evaluation of pulmonary nodules. Eur J Radiol. 2020 Jul;128:109033.

Yanagawa M, Tsubamoto M, Satoh Y, Hata A, Miyata T, Yoshida Y, Kikuchi N, Kurakami H, Tomiyama N. Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness. Radiology. 2020 Nov;297(2):462-471.

Abdominal Imaging & Interventional Radiology Group

de Gouw DJJM, Maas MC, Slagt C, Mühling J, Nakamoto A, Klarenbeek BR, Rosman C, Hermans JJ, Scheenen TWJ. Controlled mechanical ventilation to detect regional lymph node metastases in esophageal cancer using USPIO-enhanced MRI; comparison of image quality. Magn Reson Imaging. 2020;74:258-265.

Honda T, Kuriyama K, Kiso K, Kishimoto K, Tsuboyama T, Inoue A, Higashi M. Incidence rate of severe adverse drug reactions to nonionic contrast media at the National Hospital Organization Osaka National Hospital. Allergo Journal International. 2020 Sep; 29:240–244

Hongyo H, Higashihara H, Osuga K, Kashiwagi E, Kosai S, Nagai K, Tanaka K, Ono Y, Ujike T, Uemura M, Imamura R, Nonomura N, Tomiyama N. Efficacy of prophylactic selective arterial embolization for renal angiomyolipomas: identifying predictors of 50% volume reduction. CVIR Endovasc. 2020 Nov 21;3(1):84.

Kimura Y, Osuga K, Nagai K, Hongyo H, Tanaka K, Ono Y, Higashihara H, Matsuzaki S, Endo M, Kimura T, Tomiyama N. The efficacy of uterine artery embolization with gelatin sponge for retained products of conception with bleeding and future pregnancy outcomes. CVIR Endovasc. 2020 Feb 12;3(1):13

Kitajima K, Kihara T, Kawanaka Y, Kido A, Yoshida K, Mizumoto Y, Tomiyama A, Okuda S, Jinzaki M, Kato F, Takahama J, Takahata A, Fukukura Y, Nakamoto A, Tsujikawa T, Munechika J, Ohgiya Y, Kawai N, Goshima S, Ohya A, Fujinaga Y, Fukunaga T, Fujii S, Tanabe M, Ito K, Tsuboyama T, Kanie Y, Umeoka S, Ichikawa S, Motosugi U, Daido S, Kido A, Tamada T, Matsuki M, Yamashiro T, Yamakado K. Neuroendocrine carcinoma of uterine cervix findings shown by MRI for staging and survival analysis – Japan multicenter study. Oncotarget. 2020;11(40):3675-3686.

Nakamoto A, Hori M, Onishi H, Ota T, Fukui H, Ogawa K, Yano K, Tatsumi M, Tomiyama N. Ultra-high-resolution CT urography: Importance of matrix size and reconstruction technique on image quality. Eur J Radiol. 2020;130:109148

Tomimaru Y, Tanaka K, Noguchi K, Noura S, Imamura H, Iwazawa T, Dono K. Significance of fistulography findings to the healing time of postoperative pancreatic fistula after pancreaticoduodenectomy. Surg Today. 2020 ;50:577-584

Tsuboyama T, Takei O, Okada A, Honda T, Kuriyama K. Comparison of HASTE with multiple signal averaging versus conventional turbo spin echo sequence: a new option for T2-weighted MRI of the female pelvis. Eur Radiol 2020;30(6):3245-3253.

Tsuboyama T, Jost G, Pietsch H, Tomiyama N. Effect of Gadoxetic Acid Injection Duration on Tumor Enhancement in Arterial Phase Liver MRI. Acad Radiol 2020;27(8):e216-e223.

Yamamoto K, Inada Y, Nakamoto A, Nomi H, Kurisu Y, Yamamoto K, Osuga K. Catecholamine-secreting adrenal lipomatous ganglioneuroma: A case study. Radiol Case Rep. 2020;15(9):1709-1713.

Department of Artificial Intelligence Diagnostic Radiology

Mabu S, Atsumo A, Kido S, Kuremoto T, Hirano Y. Investigating the Effects of Transfer Learning on ROI-based Classification of Chest CT Images: A Case Study on Diffuse Lung Diseases. Journal of Signal Processing Systems. 2020(Mar);92(3):307-13.

Sakamoto M, Hiasa Y, Otake Y, Takao M, Suzuki Y, Sugano N, Sato Y. Bayesian Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT Using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction. Journal of Signal Processing Systems for Signal Image and Video Technology. 2020(Mar);92(3):335-44.

Kido S, Hirano Y, Mabu S. Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation. Deep Learning in Medical Image Analysis. In Advances in Experimental Medicine and Library. 2020;1213: 47-58.

Suzuki A, Sakanashi H, Kido S, Shouno H. Deep Learning in Textural Medical Image Analysis. Deep Learning in Healthcare. In Intelligent Systems Reference Library. 2020; 171:111-26.

Mabu S, Kido S, Hirano Y, Kuremoto T. Opacity Labeling of Diffuse Lung Diseases in CT Images Using Unsupervised and Semi-supervised Learning. Deep Learning in Healthcare. In Intelligent Systems Reference Library. 2020; 171:165-79.

Wataya T, Nakanishi K, Suzuki Y, Kido S, Tomiyama N. Introduction to deep learning: minimum essence required to launch a research. Jpn J Radiol. 2020;38(10):907-21.