Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction

• Item type: SIG Technical Reports(1)
• 公開日: 2024-11-27
• タイトル: Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction
• タイトル / 言語: en
• タイトル: Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: University of Tsukuba
• 著者所属: University of Tsukuba Hospital
• 著者所属: Aichi Cancer Center Hospital
• 著者所属: University of Tsukuba
• 著者所属(英): en
• 著者所属(英): University of Tsukuba
• 著者所属(英): en
• 著者所属(英): University of Tsukuba Hospital
• 著者所属(英): en
• 著者所属(英): Aichi Cancer Center Hospital
• 著者所属(英): en
• 著者所属(英): University of Tsukuba
• 著者名: Shota, Nakagawa; Satoshi, Nitta; Takahiro, Kojima; Hideki, Kakeya
• 著者名(英): Shota, Nakagawa; Satoshi, Nitta; Takahiro, Kojima; Hideki, Kakeya
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.
• 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: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2024-BIO-80, 号 15, p. 1-6, 発行日 2024-11-27
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8590
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会