@techreport{oai:ipsj.ixsq.nii.ac.jp:00241343,
 author = {Shota, Nakagawa and Satoshi, Nitta and Takahiro, Kojima and Hideki, Kakeya and Shota, Nakagawa and Satoshi, Nitta and Takahiro, Kojima and Hideki, Kakeya},
 issue = {15},
 month = {Nov},
 note = {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., 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.},
 title = {Enhancing Tumor Classification in Testicular Cancer: Segmentation-Based Pretraining and Multimodal Prediction},
 year = {2024}
}