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.

Background/Aims: The aim of this study was to investigate the effect of a surgical treatment algorithm recently proposed by the American Association for the Study of Liver Diseases (AASLD) on survival outcomes in patients with early-stage hepatocellular carcinoma (HCC) and identify effective alternative treatment modalities when liver transplantation (LT) is not available. Methods: We studied the clinical data of 1,442 patients who were diagnosed with early-stage HCC (a single lesion measuring 2–5 cm in size or 2 to 3 lesions measuring ≤3 cm in size) be-tween 2013 and 2018 and classified as Child-Turcotte-Pugh (CTP) A or B. Analyses were separately performed for individuals recommended for resection (single lesion, CTP A and no clinically significant portal hypertension) and those recommended for LT (single lesion with impaired liver function such as CTP B or clinically significant portal hypertension or multiple lesions). Results: Of 791 patients recommended for surgical resection, 85.8% underwent resection. The 5-year survival rate was higher for patients who underwent surgical resection than for those who received other treatments (89.4% vs 72.3%). Among 651 patients recommended for LT, only 3.4% underwent the procedure. The most common alternative treatment modalities were transarterial therapy (39.3%) followed by resection (28.9%) and ablation (27.8%). The overall survival rate associated with transarterial therapy was lower than that for resection and ablation, whereas that of the latter two treatments were comparable. Conclusions The survival outcomes of treatment strategies that most closely aligned with the algorithm proposed by the AASLD were superior to those of alternative treatment approaches.However, LT in patients with early-stage HCC can be challenging. When LT is not feasible, resection and ablation can be considered first-line alternative options.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

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.