{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241343","sets":["1164:5352:11553:11808"]},"path":["11808"],"owner":"44499","recid":"241343","title":["Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-27"},"_buckets":{"deposit":"67feecbe-fea9-463a-9f7e-050bd3bd07d2"},"_deposit":{"id":"241343","pid":{"type":"depid","value":"241343","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction","author_link":["664185","664186","664191","664190","664188","664189","664192","664187"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction"},{"subitem_title":"Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-11-27","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"University of Tsukuba"},{"subitem_text_value":"University of Tsukuba Hospital"},{"subitem_text_value":"Aichi Cancer Center Hospital"},{"subitem_text_value":"University of Tsukuba"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"University of Tsukuba Hospital","subitem_text_language":"en"},{"subitem_text_value":"Aichi Cancer Center Hospital","subitem_text_language":"en"},{"subitem_text_value":"University of Tsukuba","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/241343/files/IPSJ-BIO24080015.pdf","label":"IPSJ-BIO24080015.pdf"},"date":[{"dateType":"Available","dateValue":"2026-11-27"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO24080015.pdf","filesize":[{"value":"1.4 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":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f9cbffd0-cdeb-4182-9f02-10f5a3dbd8ce","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shota, Nakagawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Satoshi, Nitta"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takahiro, Kojima"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hideki, Kakeya"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shota, Nakagawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Satoshi, Nitta","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takahiro, Kojima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hideki, Kakeya","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","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":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Testicular cancer that metastasizes to retroperitoneal lymph nodes is typically treated with chemotherapy, followed by post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND). A significant concern is that approximately 50% of patients undergoing PC-RPLND have necrotic tissue in the resected specimens, indicating potential overtreatment. In this study, we propose a U-net-based classification model to distinguish between necrosis and residual teratoma prior to surgery, aiming to reduce unnecessary procedures. The U-net-based classifier achieves an area under the curve (AUC) of 0.856 and demonstrates superior performance compared to a ResNet50 classifier when results are shown in scatterplots with the results given by Logistic Regression using clinical variables. These plots highlight that the U-net-based model more accurately identifies benign tissues, supporting clinical decision-making and potentially minimizing unnecessary surgeries in testicular cancer patients.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Testicular cancer that metastasizes to retroperitoneal lymph nodes is typically treated with chemotherapy, followed by post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND). A significant concern is that approximately 50% of patients undergoing PC-RPLND have necrotic tissue in the resected specimens, indicating potential overtreatment. In this study, we propose a U-net-based classification model to distinguish between necrosis and residual teratoma prior to surgery, aiming to reduce unnecessary procedures. The U-net-based classifier achieves an area under the curve (AUC) of 0.856 and demonstrates superior performance compared to a ResNet50 classifier when results are shown in scatterplots with the results given by Logistic Regression using clinical variables. These plots highlight that the U-net-based model more accurately identifies benign tissues, supporting clinical decision-making and potentially minimizing unnecessary surgeries in testicular cancer patients.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学(BIO)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"15","bibliographicVolumeNumber":"2024-BIO-80"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":241343,"updated":"2025-01-19T07:41:19.750218+00:00","links":{},"created":"2025-01-19T01:45:56.784600+00:00"}