{"id":2783,"date":"2025-02-17T13:54:22","date_gmt":"2025-02-17T04:54:22","guid":{"rendered":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/?page_id=2783"},"modified":"2025-12-09T12:13:06","modified_gmt":"2025-12-09T03:13:06","slug":"achievement-achievement-2025","status":"publish","type":"page","link":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/en\/achievement-achievement-2025\/","title":{"rendered":"Achievement-2025"},"content":{"rendered":"\n<h2 class=\"wp-block-heading blue-line\">Original Papers<\/h2>\n\n\n\n<p>Yoshiro Ieko,&nbsp;Noriyuki Kadoya,&nbsp;Shohei Tanaka,&nbsp;Koyo Kikuchi,&nbsp;Takaya Yamamoto,&nbsp;Hisanori Ariga&nbsp;&amp;&nbsp;Keiichi Jingu, \u201cRadiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors\u201d, Radiol Phys Technol. 2025.1<\/p>\n\n\n\n<p>Ryohei Kato, Noriyuki Kadoya, Takahiro Kato, Ryota Tozuka, Shuta Ogawa, Masao Murakami, Keiichi Jingu, &#8220;Improvement of deep learning-based dose conversion accuracy to a Monte Carlo algorithm in proton beam therapy for head and neck cancers&#8221;,&nbsp;<em>Journal of Radiation Research<\/em>, 2025.4<\/p>\n\n\n\n<p>Yushan Xiao, Shohei Tanaka, Noriyuki Kadoya, Kiyokazu Sato, Yuto Kimura, Rei Umezawa, Yoshiyuki Katsuta, Kazuhiro Arai, Haruna Takahashi, Taichi Hoshino, Keiichi Jingu, &#8220;Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.&#8221;&nbsp;<em><em>Scientific Reports<\/em><\/em>,&nbsp;2025.4<\/p>\n\n\n\n<p>Takayama, Y., Kadoya, N., Yamamoto, T.&nbsp;Miyasaka, Y.,Kusano, Y,. Kajikawa, T,. Tomori, S,. Kastuta, Y,. Tanaka, S,. Arai, K,. Takeda, K,. Jingu, K,.&nbsp;Automatic segmentation of cone beam CT images using treatment planning CT images in patients with prostate cancer.&nbsp;<em>Radiol Phys Technol<\/em>&nbsp;, 2025.7<\/p>\n\n\n\n<p>Ryota Tozuka, Noriyuki Kadoya, Arata Yasunaga, Masahide Saito, Takafumi Komiyama, Hikaru Nemoto, Hidetoshi Ando, Hiroshi Onishi, Keiichi Jingu, \u201cFractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework\u201d, Japanese Journal of Radiology, 2025.9<\/p>\n\n\n\n<p>Shohei Tanaka, Noriyuki Kadoya, Wingyi Lee, Hisamichi Takagi, Yoshiyuki Katsuta, Kazuhiro Arai, Yushan Xiao, Taichi Hoshino, Noriyoshi Takahashi, Keiichi Jingu, \u201dDevelopment and evaluation of deep learning models for estimating the organ at-risk dose constraint from two-dimensional cine magnetic resonance imaging scans during irradiation\u201c, <em>The Journal of Applied Clinical Medical Physics <\/em>, 2025.11<\/p>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading blue-line\">Domestic Conference<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u9ad8\u6a4b\u79c0\u4f91\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u7530\u4e2d\u7965\u5e73\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u661f\u91ce\u5927\u5730\u3001\u5b89\u4e95\u5553\u7950\u3001\u6797\u76f4\u6a39\u3001\u795e\u5bae\u5553\u4e00.\u201d\u653e\u5c04\u7dda\u6cbb\u7642\u652f\u63f4\u306e\u305f\u3081\u653e\u5c04\u7dda\u6cbb\u7642\u7528\u8a00\u8a9e\u751f\u6210AI\u306e\u958b\u767a\u201d.\u7b2c38\u56de\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a\u5b66\u8853\u5927\u4f1a. 2025. 5 \u5317\u6d77\u9053<\/li>\n\n\n\n<li>\u7530\u4e2d\u771f\u4e00, \u89d2\u8c37\u502b\u4e4b, \u795e\u5bae\u5553\u4e00. &#8220;\u78c1\u6c17\u5171\u9cf4\u753b\u50cf\u306e\u6570\u5024\u89e3\u6790\u306b\u3088\u308b\u30b0\u30ea\u30bd\u30f3\u30d1\u30bf\u30fc\u30f3\u306e\u63a8\u5b9a&#8221;. \u7b2c114\u56de\u65e5\u672c\u75c5\u7406\u5b66\u4f1a\u7dcf\u4f1a. 2025.4 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u4e2d\u5cf6\u6b66\u7409\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6238\u585a\u51cc\u592a\u3001\u8fd1\u85e4\u6b63\u8f1d\u3001\u7530\u4e2d\u7965\u5e73\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u795e\u5bae\u5553\u4e00. \u201c\u5c40\u6240\u9032\u884c\u6027\u80ba\u764c\u60a3\u8005\u306b\u5bfe\u3059\u308b\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u81ea\u52d5\u6cbb\u7642\u8a08\u753b\u306e\u81e8\u5e8a\u7684\u6709\u7528\u6027\u8a55\u4fa1\uff1a\u5546\u7528\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3068\u306e\u6bd4\u8f03\u691c\u8a0e\u201d. \u7b2c53\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2025.10 \u672d\u5e4c<\/li>\n<\/ul>\n<\/div><\/div>\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>Wynn Wingyi Lee, Yoshiyuki Katsuta, Noriyuki Kadoya, Keiichi Jingu. \u201cEvaluation of conventional and PCA-based feature selection method of multi-omics prediction model for radiation pneumonitis in NSCLC Stage III patient\u201d. The 4th ICRPT. 2025.04 Yokohama<\/li>\n\n\n\n<li>Shinichi Tanaka, Noriyuki Kadoya, Keiichi Jingu. &#8220;Improvement of Normalisation of MRI to Estimate Pathological Grade of Prostate Cancer by Local Radiomics&#8221;. The 4th ICRPT. 2025.04 Yokohama<\/li>\n\n\n\n<li>Yoshiyuki Takahashi, Kazuhiro Arai, Noriyuki Kadoya, Shohei Tanaka, Yoshiyuki Katsuta, Taichi Hoshino, Keisuke Yasui<strong>,&nbsp;<\/strong>Naoki Hayashi, Keiichi Jingu. \u201cDevelopment of an AI chatbot for radiotherapy using Retrieval-Augmented Generation\u201d. The 4th ICRPT. 2025.04 Yokohama<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Award<\/h2>\n\n\n\n<p>\u5275\u751f\u5fdc\u7528\u533b\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc AI\u5fdc\u7528\u533b\u5b66\u90e8\u9580\u82e5\u624b\u75c7\u4f8b\u7814\u7a76\u300c\u30d5\u30e9\u30af\u30bf\u30eb\u69cb\u9020\u3092\u4e8b\u524d\u5b66\u7fd2\u306b\u7528\u3044\u305f\u653e\u5c04\u7dda\u6cbb\u7642\u9818\u57df\u306b\u304a\u3051\u308b\u65b0\u305f\u306a\u753b\u50cf\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u624b\u6cd5\u306e\u958b\u767a\u300d\uff08\u6238\u585a\u51cc\u592a\u300150\u4e07\u5186\uff09<\/p>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Invited talk &amp; Symposium<\/h2>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Grant<\/h2>\n\n\n\n<p>\u5275\u751f\u5fdc\u7528\u533b\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc AI\u5fdc\u7528\u533b\u5b66\u90e8\u9580\u82e5\u624b\u75c7\u4f8b\u7814\u7a76\u300c\u30d5\u30e9\u30af\u30bf\u30eb\u69cb\u9020\u3092\u4e8b\u524d\u5b66\u7fd2\u306b\u7528\u3044\u305f\u653e\u5c04\u7dda\u6cbb\u7642\u9818\u57df\u306b\u304a\u3051\u308b\u65b0\u305f\u306a\u753b\u50cf\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u624b\u6cd5\u306e\u958b\u767a\u300d\uff08\u6238\u585a\u51cc\u592a\u300150\u4e07\u5186\uff09<\/p>\n\n\n\n<p>\u79d1\u5b66\u7814\u7a76\u8cbb\u88dc\u52a9\u91d1\u3000\u57fa\u76e4C \u300c\u967d\u5b50\u7dda\u30d9\u30fc\u30b9\u30aa\u30f3\u30e9\u30a4\u30f3\u9069\u5fdc\u653e\u5c04\u7dda\u6cbb\u7642\u306b\u304a\u3051\u308b\u6cbb\u7642\u8a08\u753b\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u306e\u958b\u767a\u300d(2025\u5e744\u6708\uff5e2028\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005 \u52a0\u85e4\u4eae\u5e73 455\u4e07\u5186<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Original Papers Yoshiro Ieko,&nbsp;Noriyuki Kadoya,&nbsp;Shohei Tanaka,&nbsp;Koyo Kikuchi,&nbsp;Takaya Yamamoto,&nbsp;Hisanori Ariga&nbsp;&amp;&nbsp;Keiichi Jingu, \u201cRadiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors\u201d, Radiol Phys Technol. 2025.1 Ryohei Kato, Noriyuki Kadoya, Takahiro Kato, Ryota Tozuka, Shuta Ogawa, Masao Murakami, Keiichi Jingu, &#8220;Improvement of deep learning-based dose conversion accuracy to a [&hellip;]<\/p>\n","protected":false},"author":5,"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=2754","footnotes":"[]"},"class_list":["post-2783","page","type-page","status-publish","hentry","en-US"],"_links":{"self":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/2783","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\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/comments?post=2783"}],"version-history":[{"count":13,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/2783\/revisions"}],"predecessor-version":[{"id":3140,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/2783\/revisions\/3140"}],"wp:attachment":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/media?parent=2783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}