{"links":{},"id":227891,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227891","sets":["1164:6390:11093:11330"]},"path":["11330"],"owner":"44499","recid":"227891","title":["肺がんにおけるEGFR遺伝子変異の有無を予測するCT画像診断支援システム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-09-18"},"_buckets":{"deposit":"9c2bf752-cd65-4103-ad98-4f6215f26e0e"},"_deposit":{"id":"227891","pid":{"type":"depid","value":"227891","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"肺がんにおけるEGFR遺伝子変異の有無を予測するCT画像診断支援システム","author_link":["607786","607787","607789","607791","607793","607788","607792","607790"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"肺がんにおけるEGFR遺伝子変異の有無を予測するCT画像診断支援システム"},{"subitem_title":"CT Image Diagnostic Support System for Predicting EGFR Mutations in Lung Cancer","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"データ活用と医療支援","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-09-18","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"新潟大学大学院自然科学研究科"},{"subitem_text_value":"新潟大学大学院自然科学研究科"},{"subitem_text_value":"新潟大学工学部工学科"},{"subitem_text_value":"新潟大学医歯学総合病院放射線診断科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Technology, Niigata University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Technology, Niigata University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Engineering, Niigata University","subitem_text_language":"en"},{"subitem_text_value":"Department of Diagnostic Radiology, Niigata University Medical & Dental Hospital","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/227891/files/IPSJ-CDS23038013.pdf","label":"IPSJ-CDS23038013.pdf"},"date":[{"dateType":"Available","dateValue":"2025-09-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CDS23038013.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"47"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"d0fbe996-1f19-4364-8299-784f4569d0c5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"タン, チェイイェン"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"赤川, 遼汰郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山﨑, 達也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山崎, 元彦"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Cher, Yen Tan","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryotaro, Akagawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tatsuya, Yamazaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Motohiko, Yamazaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628327","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8604","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"肺がんは最も頻度の高いがんであり，日本における肺がんの死亡数は部位別で最多となっている．近年，肺がんの診断に用いられる CT 検査の数は増加の一途をたどっているが，放射線科専門医の数は不足しているという問題がある．そのため，医師の診断を支援するツールが現場で求められている．また，日本人の肺がんでは，EGFR（Epidermal Growth Factor Receptor）遺伝子というがん細胞の増殖に関わる遺伝子に変異がよく認められる．EGFR 遺伝子変異の有無は治療薬決定に関わる重要な因子であるが，遺伝子検査には侵襲的な組織採取が必要であるため，非侵襲的な手法の開発が期待されている．そこで，CT 画像から腫瘍を検出し，同時にその画像情報から EGFR 遺伝子変異の有無を判定する非侵襲的な診断支援ツールが必要である．本研究では，深層学習による肺腫瘍の自動検出から機械学習を用いた EGFR 遺伝子変異の有無予測までを End-to-End で実現し，放射線科専門医の診断のサポートするシステムを構築する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Lung cancer is the most common type of cancer and is the leading cause of cancer-related deaths in Japan. However, with the increasing number of CT examinations for diagnosis, the healthcare system faces challenges due to a shortage of specialized radiologists. As a result, there is a high demand for diagnostic support tools that can assist physicians in the field. Among the Japanese population diagnosed with lung cancer, mutations in the EGFR (Epidermal Growth Factor Receptor) gene, a key factor in cancer cell proliferation, are frequently observed. The presence/absence of EGFR mutations is a critical determinant in therapeutic decision-making. However, current methods for identifying these mutations require invasive tissue sampling, thereby increasing the need for non-invasive diagnostic approaches. This study aims to fill this gap by developing a diagnostic support system that leverages deep learning algorithms for automated lung tumor detection in CT images and utilizes machine learning techniques for the concurrent prediction of the presence/absence of EGFR mutations. This end-to-end system is designed to aid radiology specialists in their diagnostic procedures.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンシューマ・デバイス＆システム（CDS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-09-18","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"13","bibliographicVolumeNumber":"2023-CDS-38"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:27:08.509130+00:00","updated":"2025-01-19T12:01:30.800446+00:00"}