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.

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.

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.

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.

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.

PURPOSE: This study assessed the axillary lymph node (ALN)-to-primary tumor maximum standard uptake value (SUVmax) ratio (ALN/T SUV ratio) in invasive ductal breast cancer (IDC) on preoperative 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to determine the effectiveness in predicting recurrence-free survival (RFS). METHODS: One hundred nineteen IDC patients (mean age, 50.5+/-10.5 years) with pathologically proven ALN involvement without distant metastasis and preoperative FDG PET/CT were enrolled in the study. SUVmax values of the ALN and primary tumor were obtained on FDG PET/CT, and ALN/T SUV ratio was calculated. Several factors were evaluated for their effectiveness in predicting RFS. These included several parameters on FDG PET/CT as well as several clinicopathological parameters: pathologic tumor/node stage; nuclear and histological grade; hormonal state; status with respect to human epidermal growth factor receptor 2, mindbomb E3 ubiquitin protein ligase 1 (MIB-1), and p53; primary tumor size; and ALN size. RESULTS: Among 119 patients with breast cancer, 17 patients (14.3%) experienced relapse during follow-up (mean follow-up, 28.4 months). The ALN/T SUV ratio of the group with disease recurrence was higher than that of the group without recurrence (0.97+/-1.60 and 0.45+/-0.40, respectively, p=0.005). Univariate analysis showed that the primary tumor SUVmax, ALN SUVmax, ALN/T SUV ratio, ALN status, nuclear and histological grade, estrogen receptor (ER) status, and MIB-1 status were predictors for RFS. Among these variables, ALN/T SUV ratio with hazard ratio of 4.20 (95% confidence interval [CI], 1.74-10.13) and ER status with hazard ratio of 4.33 (95% CI, 1.06-17.71) were predictors for RFS according to multivariate analysis (p=0.002 and p=0.042, respectively). CONCLUSION: Our study demonstrated that ALN/T SUV ratio together with ER status was an independent factor for predicting relapse in IDC with metastatic ALN. ALN/T SUV ratio on preoperative FDG PET/CT may be a useful marker for selecting IDC patients that need adjunct treatment to prevent recurrence.
Breast Neoplasms* ; Electrons ; Estrogens ; Fluorodeoxyglucose F18* ; Follow-Up Studies ; Humans ; Lymph Nodes ; Multivariate Analysis ; Neoplasm Metastasis ; Positron-Emission Tomography and Computed Tomography* ; Prognosis ; Receptor, Epidermal Growth Factor ; Recurrence ; Ubiquitin-Protein Ligases

The metastasis of uterine leiomyosarcoma to the neck node has not been reported previously and the radiotherapy has been rarely used for the metastatic lesion of the other sites. We report a case of neck metastasis from a uterine leiomyosarcoma, which developed 10 months after surgery and postoperative pelvic radiotherapy. It also involved the parapharyngeal space, adjacent spine, and spinal canal. The metastatic neck mass was inoperable, and was treated by neck radiotherapy (6,000 cGy) and chemotherapy including taxol and carboplatin. The mass has regressed progressively to a nearly impalpable state. She has never developed spinal cord compression syndrome, and has maintained good swallowing for eight months since the neck radiotherapy and chemotherapy. Since the extensive metastatic neck mass showed good local response to high dose radiotherapy and chemotherapy, both treatments may be considered for an unresectable metastatic leiomyosarcoma.
Carboplatin ; Deglutition ; Drug Therapy* ; Leiomyosarcoma* ; Lymphatic Diseases* ; Neck ; Neoplasm Metastasis ; Paclitaxel ; Radiotherapy* ; Spinal Canal ; Spinal Cord Compression ; Spine

A 24 year-old female presented for a (99m)Tc-methylene diphosphonatae (MDP) whole body bone scan due to chronic pain in the bilateral lower extremities that has aggravated since 2002. She was diagnosed with Camurati-Engelmann disease (CED) based on the clinical and radiological findings in 2002, and she re-visited our institute to evaluate disease status at this time. CED is a rare autosomal dominant type of bone dysplasia characterized by progressive cortical thickening of long bones, and narrowing of medullary cavity, and thus presents with typical clinical symptoms and signs such as chronic pain in the extremities, muscle weakness, and waddling gait. On the (99m)Tc-MDP bone scan performed to evaluate disease status, intense increased uptake was seen in the skull, facial bones, bilateral scapulae, bilateral long bones, and bilateral pelvic bones, which clearly demonstrated the extent of CED involvement.
Bone Diseases, Developmental ; Camurati-Engelmann Syndrome ; Chronic Pain ; Extremities ; Facial Bones ; Female ; Gait ; Humans ; Lower Extremity ; Muscle Weakness ; Pelvic Bones ; Scapula ; Skull

BACKGROUND: Vancomycin-resistant enterococci (VRE) infections have become a major healthcare-associated pathogen problem worldwide. Nosocomial VRE infections could be effectively controlled by screening patients at high risk of harboring VRE and thereby lowering the influx of VRE into healthcare centers. In this study, we evaluated factors associated with VRE colonization in patients transferred to emergency departments, to detect patients at risk for VRE carriage. METHODS: This study was conducted in the emergency department of a medical college-affiliated hospital in Korea. Every patient transferred to the emergency department and admitted to the hospital from January to December 2016 was screened for VRE using rectal cultures. In this cross-sectional study, the dependent variable was VRE colonization and the independent variables were demographic and clinical factors of the patients and factors related to the transferring hospital. Patients were divided into two groups, VRE and non-VRE, and previously collected patient data were analyzed. Then we performed logistic regression analyses of characteristics that differed significantly between groups. RESULTS: Out of 650 patients, 106 (16.3%) had positive VRE culture results. Significant variables in the logistic analysis were transfer from geriatric long-term care hospital (adjusted odds ration [aOR]: 8.017; 95% confidence interval [CI]: 1.378–46.651), hospital days (4–7 days; aOR: 7.246; 95% CI: 3.229–16.261), duration of antimicrobial exposure (1–3 days; aOR: 1.976; 95% CI: 1.137–3.436), and age (aOR: 1.025; 95% CI: 1.007–1.043). CONCLUSION: VRE colonization in patients transferred to the emergency department is associated primarily with factors related to the transferred hospitals rather than demographic and clinical characteristics.
Bacterial Infections ; Colon* ; Cross-Sectional Studies ; Delivery of Health Care ; Emergencies* ; Emergency Service, Hospital* ; Enterococcus* ; Humans ; Infection Control ; Korea* ; Logistic Models ; Long-Term Care ; Mass Screening ; Vancomycin Resistance ; Vancomycin-Resistant Enterococci

Glossectomy combined with radiotherapy causes different levels of tongue function disorders and leads to severe malocclusion, with poor periodontal status in cancer survivors. Although affected patients require regular access to orthodontic care, special considerations are crucial for treatment planning. This case report describes the satisfactory orthodontic management for the correction of severe dental crowding in a 43-year-old female 6  years after treatment for tongue cancer with total glossectomy combined with radiotherapy, to envision the possibility of orthodontic care for oral cancer survivors. Extraction was performed to correct dental crowding and establish proper occlusion following alignment, after considering the possibility of osteoradionecrosis. Orthodontic mini-implants were used to provide skeletal anchorage required for closure of the extraction space and intrusion of the anterior teeth. The dental crowding was corrected, and Class I occlusal relationship was established after 36 months of treatment. The treatment outcome was sustained after 15 months of retention, and long-term follow-up was recommended.