Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Primary cholangiocarcinoma is a rare bile duct epithelial neoplasm that can present with atypical clinical manifestations, complicating its diagnosis. A 62-year-old male showed symptoms suggestive of a complicated hepatic cyst that was later identified as intrahepatic cholangiocarcinoma. The patient presented with abdominal discomfort without fever. Imaging revealed a large cystic lesion in the liver. Despite the initial treatment for a presumed abscess, a biopsy confirmed cholangiocarcinoma. This case highlights the diagnostic challenge of distinguishing between benign complicated hepatic cysts and malignancies, particularly when typical markers of infection are absent. Early biopsy and vigilant assessments are crucial in such presentations to avoid a delayed diagnosis and initiate appropriate treatment.

Hepatic tuberculosis, typically associated with miliary tuberculosis, can occasionally present as localized liver lesions. This case report describes a 77-year-old male presenting with persistent abdominal pain and fever, following an endoscopic retrograde cholangiopancreatography for bile duct sludge removal. Subsequent computed tomography revealed focal liver lesions. Despite initial treatment with antibiotics for a suspected inflammatory liver abscess, his condition did not improve. A liver biopsy was performed, revealing caseous granulomas, and the tuberculosis polymerase chain reaction result was positive. The patient was diagnosed with primary hepatic tuberculosis, which later disseminated. Oral anti-tuberculosis therapy was initiated and is currently being closely monitored. This case emphasizes the importance of considering hepatic tuberculosis in the differential diagnosis of liver lesions, particularly in cases involving cholestatic liver function tests, and persistent symptoms unresponsive to conventional antibiotics.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Primary cholangiocarcinoma is a rare bile duct epithelial neoplasm that can present with atypical clinical manifestations, complicating its diagnosis. A 62-year-old male showed symptoms suggestive of a complicated hepatic cyst that was later identified as intrahepatic cholangiocarcinoma. The patient presented with abdominal discomfort without fever. Imaging revealed a large cystic lesion in the liver. Despite the initial treatment for a presumed abscess, a biopsy confirmed cholangiocarcinoma. This case highlights the diagnostic challenge of distinguishing between benign complicated hepatic cysts and malignancies, particularly when typical markers of infection are absent. Early biopsy and vigilant assessments are crucial in such presentations to avoid a delayed diagnosis and initiate appropriate treatment.

Hepatic tuberculosis, typically associated with miliary tuberculosis, can occasionally present as localized liver lesions. This case report describes a 77-year-old male presenting with persistent abdominal pain and fever, following an endoscopic retrograde cholangiopancreatography for bile duct sludge removal. Subsequent computed tomography revealed focal liver lesions. Despite initial treatment with antibiotics for a suspected inflammatory liver abscess, his condition did not improve. A liver biopsy was performed, revealing caseous granulomas, and the tuberculosis polymerase chain reaction result was positive. The patient was diagnosed with primary hepatic tuberculosis, which later disseminated. Oral anti-tuberculosis therapy was initiated and is currently being closely monitored. This case emphasizes the importance of considering hepatic tuberculosis in the differential diagnosis of liver lesions, particularly in cases involving cholestatic liver function tests, and persistent symptoms unresponsive to conventional antibiotics.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Primary cholangiocarcinoma is a rare bile duct epithelial neoplasm that can present with atypical clinical manifestations, complicating its diagnosis. A 62-year-old male showed symptoms suggestive of a complicated hepatic cyst that was later identified as intrahepatic cholangiocarcinoma. The patient presented with abdominal discomfort without fever. Imaging revealed a large cystic lesion in the liver. Despite the initial treatment for a presumed abscess, a biopsy confirmed cholangiocarcinoma. This case highlights the diagnostic challenge of distinguishing between benign complicated hepatic cysts and malignancies, particularly when typical markers of infection are absent. Early biopsy and vigilant assessments are crucial in such presentations to avoid a delayed diagnosis and initiate appropriate treatment.

Hepatic tuberculosis, typically associated with miliary tuberculosis, can occasionally present as localized liver lesions. This case report describes a 77-year-old male presenting with persistent abdominal pain and fever, following an endoscopic retrograde cholangiopancreatography for bile duct sludge removal. Subsequent computed tomography revealed focal liver lesions. Despite initial treatment with antibiotics for a suspected inflammatory liver abscess, his condition did not improve. A liver biopsy was performed, revealing caseous granulomas, and the tuberculosis polymerase chain reaction result was positive. The patient was diagnosed with primary hepatic tuberculosis, which later disseminated. Oral anti-tuberculosis therapy was initiated and is currently being closely monitored. This case emphasizes the importance of considering hepatic tuberculosis in the differential diagnosis of liver lesions, particularly in cases involving cholestatic liver function tests, and persistent symptoms unresponsive to conventional antibiotics.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.