{"id":591,"date":"2023-06-29T13:48:46","date_gmt":"2023-06-29T04:48:46","guid":{"rendered":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/?page_id=591"},"modified":"2023-07-13T14:40:34","modified_gmt":"2023-07-13T05:40:34","slug":"achievement-2020","status":"publish","type":"page","link":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/en\/achievement-2020\/","title":{"rendered":"Achievement-2020"},"content":{"rendered":"\n<h2 class=\"wp-block-heading blue-line\">Original Papers<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Tomori S, Kadoya N, Kajikawa T, Kimura Y, Narazaki K, Ochi T, Jingu K. \u201cSystematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy\u201d, Med Phys. 2020 Dec 26.<\/li>\n\n\n\n<li>Umezawa R, Kadoya N, Ota H, Nakajima Y, Saito M, Takagi H, Takanami K, Takahashi N, Ishikawa Y, Yamamoto T, Matsushita H, Takeda K, Takase K, Jingu K. \u201cDose-Dependent Radiation-Induced Myocardial Damage in Esophageal Cancer Treated With Chemoradiotherapy: A Prospective Cardiac Magnetic Resonance Imaging Study\u201d, Adv Radiat Oncol. 2020 Aug 5;5(6):1170-1178.<\/li>\n\n\n\n<li>Miyasaka Y, Kadoya N, Umezawa R, Takayama Y, Ito K, Yamamoto T, Tanaka S, Dobashi S, Takeda K, Nemoto K, Iwai T, Jingu K. \u201cComparison of predictive performance for toxicity by accumulative dose of DVH parameter addition and DIR addition for cervical cancer patients\u201d, J Radiat Res. 2021 Jan 1;62(1):155-162.<\/li>\n\n\n\n<li>Kajikawa T, Kadoya N, Tanaka S, Nemoto H, Takahashi N, Chiba T, Ito K, Katsuta Y, Dobashi S, Takeda K, Yamada K, Jingu K. \u201cDose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy\u201d, Phys Med. 2020 Dec;80:186-192.<\/li>\n\n\n\n<li>Sugawara Y, Kadoya N, Kotabe K, Nakajima Y, Ikeda R, Tanabe S, Ohashi H, Jingu K. \u201cDevelopment of a dynamic deformable thorax phantom for the quality management of deformable image registration\u201d, Phys Med. 2020 Sep;77:100-107.<\/li>\n\n\n\n<li>Kadoya N, Nemoto H, Kajikawa T, Nakajima Y, Kanai T, Ieko Y, Ikeda R, Sato K, Dobashi S, Takeda K, Jingu K. \u201cEvaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom\u201d, Phys Med. 2020 Sep;77:75-83.<\/li>\n\n\n\n<li>Ieko Y, Kadoya N, Kanai T, Nakajima Y, Arai K, Kato T, Ito K, Miyasaka Y, Takeda K, Iwai T, Nemoto K, Jingu K. \u201cThe impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer\u201d, Radiol Phys Technol. 2020 Sep;13(3):230-237.<\/li>\n\n\n\n<li>Miyasaka Y, Kadoya N, Ito K, Umezawa R, Kubozono M, Yamamoto T, Nakajima Y, Saito M, Takayama Y, Nemoto K, Iwai T, Jingu K, \u201cQuantitative Analysis of Intra-Fractional Variation in CT-based Image Guided Brachytherapy for Cervical Cancer Patients\u201d, Phys Med. 2020 Apr 30;73:164-172<\/li>\n\n\n\n<li>Kimura Y, Kadoya N, Tomori S, Oku Y, Jingu K, \u201cError Detection Using a Convolutional Neural Network With Dose Difference Maps in Patient-Specific Quality Assurance for Volumetric Modulated Arc Therapy\u201d, Phys Med. 2020 Apr 21;73:57-64<\/li>\n\n\n\n<li>Takagi H, Kadoya N, Kajikawa T, Tanaka S, Takayama Y, Chiba T, Ito K, Dobashi S, Takeda K, Jingu K, \u201cMulti-atlas-based Auto-Segmentation for Prostatic Urethra Using Novel Prediction of Deformable Image Registration Accuracy\u201d, Med Phys. 2020 Mar 22<\/li>\n\n\n\n<li>Parishan M, Faghihi R, Kadoya N, Jingu K, \u201cThe Effects of a Transverse Magnetic Field on the Dose Enhancement of Nanoparticles in a Proton Beam: A Monte Carlo Simulation\u201d, Phys Med Biol. 2020 Apr 17;65(8):085002<\/li>\n\n\n\n<li>Nakajima Y, Kadoya N, Kimura T, Hioki K, Jingu K, Yamamoto T, \u201cVariations between dose-ventilation and dose-perfusion metrics in radiotherapy planning for lung cancer\u201d, Adv Radiat Oncol. 2020 Mar 20<\/li>\n\n\n\n<li>Kadoya N, Tanaka S, Kajikawa T, Tanabe S, Abe K, Nakajima Y, Yamamoto T, Takahashi N, Takeda K, Dobashi S, Takeda K, Nakane K, Jingu K, \u201cHomology-based Radiomic Features for Prediction of the Prognosis of Lung Cancer Based on CT-based Radiomics\u201d, Med Phys. 2020 Feb 25<\/li>\n\n\n\n<li>Nakajima Y, Kadoya N, Kanai T, Saito M, Kito S, Hashimoto S, Karasawa K, Jingu K, \u201cEvaluation of the Effect of User-Guided Deformable Image Registration of Thoracic Images on Registration Accuracy Among Users\u201d, Med Dosim. 2020 Jan 31:S0958-3947(19)30130-X<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Domestic Conference<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Tanabe S, Kadoya N, Tanaka S, Kajikawa T, Abe K, Takeda K, Dobashi S, Jingu K, \u201cEffect of type of input image on prognostic prediction accuracy using deep learning radiomics in lung cancer patients\u201d, \u7b2c119\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2020.5 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li>Takeuchi T, Kadoya N, Tanabe S, Tanaka S, Matsuda S, Jingu K, \u201cDevelopment of GAN-based synthetic brain CT image: What impact does increasing the training data have on the accuracy?\u201d, \u7b2c119\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2020.5 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li>\u6728\u6751\u7950\u5229\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u5965\u6d0b\u5e73\u3001\u795e\u5bae \u5553\u4e00. \u201cVMAT\u60a3\u8005\u7dda\u91cf\u691c\u8a3c\u306b\u304a\u3051\u308bConvolutional neural network\u3092\u7528\u3044\u305fMLC\u30a8\u30e9\u30fc\u691c\u51fa\u30e2\u30c7\u30eb\u306e\u958b\u767a\u201d. \u7b2c33\u56de\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a\u5b66\u8853\u5927\u4f1a. 2020.5 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">International Conference<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ishida T, Kadoya K, Tanabe S, Ohashi H, Takeda K, Dobashi S, Jingu K, \u201cEvaluate of the accuracy of pelvic CT-MR deformable image registration using various cost functions\u201d, 20th Asia-Oceania Congress of Medical Physics (AOCMP), 2020. 12 Phuket, Thailand.<\/li>\n\n\n\n<li>Mouri S, Kadoya N, Katsuta Y, Kanai T, Nakajima Y, Tanabe S, Sugai Y, Umeda M, Dobashi S, Takeda K, Jingu K, \u201cEvaluation of machine learning-based prediction model for radiation pneumonitis in NSCLC patients\u201d, 20th Asia-Oceania Congress of Medical Physics (AOCMP), 2020. 12 Phuket, Thailand.<\/li>\n\n\n\n<li>Sugai Y, Kadoya N, Tanaka S, Tanabe S, Umeda M, Yamamoto T, Takeda K, Dobashi S, Ohashi H, Takeda K, Jingu K, \u201cPrognostic analysis of CT-based radiomics focusing on a subgroup of NSCLC patients\u201d, 20th Asia-Oceania Congress of Medical Physics (AOCMP), 2020. 12 Phuket, Thailand.<\/li>\n\n\n\n<li>Tanabe S, Kadoya N, Tanaka S, Takeda K, Dobashi S, Jingu K, \u201cImpact of image type and deep learning architecture in deep learning radiomics on the accuracy of lung cancer prediction\u201d, 20th Asia-Oceania Congress of Medical Physics (AOCMP), 2020. 12 Phuket, Thailand.<\/li>\n\n\n\n<li>Ieko Y, Kadoya N, Abe K, Tanaka S, Takagi H, Kanai T, Ichiji K, Yamamoto T, Ariga H, Jingu K, &#8220;Evaluation of CT-Based Radiomics Features for Predicting Parameters Measured Using a Pulmonary Function Test&#8221;, 2020 Joint AAPM | COMP Meeting 2020. 7. VIRTUAL<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Award<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cRadiological physics and technology\u201d \u8ad6\u6587\u8cde(\u571f\u4e95\u8cde). 2020.5<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Patent<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u89d2\u8c37\u502b\u4e4b\u3001\u68b6\u5ddd\u667a\u535a\u3001\u7530\u4e2d\u7965\u5e73\u3001\u571f\u6a4b\u5353\u3001\u795e\u5bae\u5553\u4e00. \u78c1\u5834\u5f71\u97ff\u3092\u8003\u616e\u3057\u305f\u7dda\u91cf\u5206\u5e03\u4f5c\u6210\u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u78c1\u5834\u5f71\u97ff\u3092\u8003\u616e\u3057\u305f\u7dda\u91cf\u5206\u5e03\u4f5c\u6210\u65b9\u6cd5\u3001\u304a\u3088\u3073\u7dda\u91cf\u5206\u5e03\u4f5c\u6210\u88c5\u7f6e, \u7279\u98582020-1851160<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Invited talk &amp; Symposium<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u52dd\u7530\u7fa9\u4e4b. \u201cTemplate\u306b\u3088\u308b\u52b9\u7387\u5316\uff08Monaco\uff09\u201d. \u4ee4\u548c2\u5e74\u5ea6\u6771\u5317\u5927\u5b66\u533b\u5b66\u7269\u7406\u30bb\u30df\u30ca\u30fc. 2020.12 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cMR-Linac\u201d. \u7b2c9\u56deJBMP\u653e\u5c04\u7dda\u6cbb\u7642\u54c1\u8cea\u7ba1\u7406\u30fb\u533b\u5b66\u7269\u7406\u8b1b\u7fd2\u4f1a. 2020.11 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cAdaptive radiotherapy\u89e3\u5256\u5b66\u7684\u5909\u5316\u306b\u5bfe\u3059\u308b\u653e\u5c04\u7dda\u6cbb\u7642\u201d, \u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u7b2c33\u56de\u5b66\u8853\u5927\u4f1a (\u6559\u80b2\u8b1b\u6f14)<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cOn-line adaptive radiotherapy with AI technology\u201d, \u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u7b2c33\u56de\u5b66\u8853\u5927\u4f1a (AI\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0)<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201c\u533b\u5b66\u7269\u7406\u58eb\u306b\u306a\u308b\u306b\u306f\u201c. \u533b\u5b66\u7269\u7406\u58eb\u306b\u306a\u308d\u3046\u30bb\u30df\u30ca\u30fc. 2020.10 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201c\u9ad8\u7cbe\u5ea6\u306a\u60a3\u8005\u500b\u5225\u653e\u5c04\u7dda\u6cbb\u7642\u3092\u5b9f\u73fe\u3059\u308b\u30ec\u30c7\u30a3\u30aa\u30df\u30af\u30b9\u6280\u8853\u201d. \u7b2c117\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a \u30b7\u30f3\u30dd\u30b8\u30a6\u30e0. 2019.5 \u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Grant<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u79d1\u5b66\u7814\u7a76\u8cbb\u88dc\u52a9\u91d1\u3000\u7814\u7a76\u6d3b\u52d5\u30b9\u30bf\u30fc\u30c8\u652f\u63f4 \u300c\u60a3\u8005\u500b\u5225\u5316\u533b\u7642\u306b\u5411\u3051\u305f\u6cbb\u7642\u524d\u306e\u533b\u7642\u753b\u50cf\u306e\u307f\u304b\u3089\u816b\u760d\u306e\u7e2e\u5c0f\u3092\u4e88\u6e2c\u3059\u308b\u624b\u6cd5\u306e\u958b\u767a\u300d(2020\u5e7410\u6708\uff5e2021\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005 \u7530\u4e2d\u7965\u5e73 273\u4e07\u5186\uff09<\/li>\n\n\n\n<li>\u79d1\u5b66\u7814\u7a76\u8cbb\u88dc\u52a9\u91d1\u3000\u82e5\u624b\u7814\u7a76 \u300c\u80ba\u306e\u6a5f\u80fd\u3068\u5f62\u614b\u306e\u7dda\u91cf\u8a55\u4fa1\u3092\u878d\u5408\u3057\u305f\u653e\u5c04\u7dda\u80ba\u81d3\u708e\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u300d(2020\u5e744\u6708\uff5e2023\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005 \u52dd\u7530\u7fa9\u4e4b 350\u4e07\u5186\uff09<\/li>\n\n\n\n<li>AMED\u533b\u5de5\u9023\u643a\u4e8b\u696d\u5316\u63a8\u9032\u4e8b\u696d \u6771\u5317\u5927\u5b66(\u4ee3\u8868:\u89d2\u8c37\u52a9\u6559)\u3001\u4eac\u90fd\u5927\u5b66(\u4ee3\u8868:\u6e9d\u8107\u6559\u6388)\u3001\u6771\u4eac\u5973\u5b50\u533b\u5927(\u4ee3\u8868:\u897f\u5c3e\u6559\u6388)\u306e3\u5927\u5b66\u304c\u53c2\u753b\u3059\u308b\u7814\u7a76\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\uff08\u56db\u6b21\u5143\u653e\u5c04\u7dda\u6cbb\u7642\u306b\u5bfe\u5fdc\u3057\u305f\u653e\u5c04\u7dda\u6cbb\u7642\u8a08\u753b\u88c5\u7f6e\u306e\u958b\u767a\uff09 (2020\u5e742\u6708~2022\u5e743\u6708:\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u5168\u4f53\u7dcf\u984d: \u7d041\u51048000\u4e07\u5186).<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Original Papers Domestic Conference International Conference Award Patent Invited talk &amp; Symposium Grant<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-achievement.php","meta":{"_locale":"en_US","_original_post":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/?page_id=435","footnotes":""},"class_list":["post-591","page","type-page","status-publish","hentry","en-US"],"_links":{"self":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/591","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/comments?post=591"}],"version-history":[{"count":4,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/591\/revisions"}],"predecessor-version":[{"id":1247,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/591\/revisions\/1247"}],"wp:attachment":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/media?parent=591"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}