CT Image Diagnostic Support System for Predicting EGFR Gene Mutations in Lung Cancer

• Item type: Trans(1)
• 公開日: 2024-05-24
• タイトル: CT Image Diagnostic Support System for Predicting EGFR Gene Mutations in Lung Cancer
• タイトル / 言語: en
• タイトル: CT Image Diagnostic Support System for Predicting EGFR Gene Mutations in Lung Cancer
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [コンシューマ・システム論文] lung CT images, tumor segmentation, EGFR gene mutation, deep learning, radiomic features
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Science and Technology, Niigata University
• 著者所属: Graduate School of Science and Technology, Niigata University
• 著者所属: Faculty of Engineering, Niigata University
• 著者所属: Department of Diagnostic Radiology, Niigata University Medical and Dental Hospital
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Niigata University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Niigata University
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, Niigata University
• 著者所属(英): en
• 著者所属(英): Department of Diagnostic Radiology, Niigata University Medical and Dental Hospital
• 著者名: Cher, Yen Tan; Ryotaro, Akagawa; Tatsuya, Yamazaki; Motohiko, Yamazaki
• 著者名(英): Cher, Yen Tan; Ryotaro, Akagawa; Tatsuya, Yamazaki; Motohiko, Yamazaki
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Lung cancer is the most common type of cancer and is the leading cause of cancer-related deaths in Japan. Regarding lung cancer diagnosis, a pivotal aspect in lung cancer treatment is tumor detection and selection of appropriate cancer treatment. Computer Tomography (CT) images are usually used to detect tumors. After tumor detection, identifying mutations in the Epidermal Growth Factor Receptor (EGFR) gene is essential for cancer treatment selection. The EGFR gene is a key factor associated with cancer cell proliferation its mutation appears both inside and around the tumors. This paper proposes a lung cancer diagnostic system designed to streamline the process from tumor detection to EGFR gene mutation identification. The proposed system consists of three modules: an input interface module, an automated lung tumor segmentation module, and an EGFR mutation prediction module. The system is characterized in that all modules are consistently based on image processing. Consequently, the proposed system enables users to acquire the diagnosis results of tumor detection as well as EGFR gene mutation prediction by just providing the input CT image data and the patients' clinical features. Our experimental results confirm that the system achieves performance levels comparable to existing research, both in terms of lung tumor segmentation accuracy and the precision of EGFR mutation predictions.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Lung cancer is the most common type of cancer and is the leading cause of cancer-related deaths in Japan. Regarding lung cancer diagnosis, a pivotal aspect in lung cancer treatment is tumor detection and selection of appropriate cancer treatment. Computer Tomography (CT) images are usually used to detect tumors. After tumor detection, identifying mutations in the Epidermal Growth Factor Receptor (EGFR) gene is essential for cancer treatment selection. The EGFR gene is a key factor associated with cancer cell proliferation its mutation appears both inside and around the tumors. This paper proposes a lung cancer diagnostic system designed to streamline the process from tumor detection to EGFR gene mutation identification. The proposed system consists of three modules: an input interface module, an automated lung tumor segmentation module, and an EGFR mutation prediction module. The system is characterized in that all modules are consistently based on image processing. Consequently, the proposed system enables users to acquire the diagnosis results of tumor detection as well as EGFR gene mutation prediction by just providing the input CT image data and the patients' clinical features. Our experimental results confirm that the system achieves performance levels comparable to existing research, both in terms of lung tumor segmentation accuracy and the precision of EGFR mutation predictions.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12628043
• 書誌情報: 情報処理学会論文誌コンシューマ・デバイス＆システム（CDS）; 巻 14, 号 2, 発行日 2024-05-24
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2186-5728
• 出版者 / 言語: ja
• 出版者: 情報処理学会