Mesenteric injuries from blunt trauma are rare, but can result in life-threatening complications, including massive hemorrhage from mesenteric arterial disruption, bowel ischemia, necrosis, and perforation. Prompt diagnosis and treatment are critical, and surgical intervention is traditionally considered the gold standard intervention. Although transcatheter arterial embolization (TAE) is an established treatment for solid organ injuries following blunt abdominal trauma, its application to mesenteric injuries is less common. A 47-year-old male was admitted to our trauma center, which employes a hybrid emergency room system, after falling from a height of 3 meters. Abdominal computed tomography revealed active contrast extravasation from the middle colic artery which had a large hemoperitoneum, but no evidence of bowel injury. After initial resuscitation and stabilization, TAE was performed to control the hemorrhage. The patient was discharged 42 days after admission without significant complications. As a possible treatment option for mesenteric injuries without intestinal damage, we suggest TAE.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Purpose: This study aimed to present a novel method for estimating the effective atomic number(Zeff ) using dual-energy computed tomography (DECT) designed to improve accuracy andstreamline clinical workflows by reducing computational complexity. Methods: The proposed model leverages the DECT-derived mass attenuation coefficients without detailed compositional analysis. By incorporating additional parameters into the conventional Rutherford model, such as exponential and trigonometric functions, the model effectively capturescomplex variations in attenuation, enabling precise Zeff estimation. Model fitting was performedusing dual-energy data and evaluated using the percentage difference in error rates. Results: Compared with the Rutherford model, which recorded a maximum error rate of 0.55%, the proposed model demonstrated a significantly lower maximum error rate of 0.15%, highlightingits precision. Zeff estimates for various materials closely matched the reference values, confirmingthe improved accuracy of the model. Conclusions The proposed DECT-based model provides a practical and efficient approach to Zeff estimation, with potential applications in radiation oncology, particularly for accurate stopping power ratio calculations in proton and heavy ion therapies.

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.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Purpose: This study aimed to present a novel method for estimating the effective atomic number(Zeff ) using dual-energy computed tomography (DECT) designed to improve accuracy andstreamline clinical workflows by reducing computational complexity. Methods: The proposed model leverages the DECT-derived mass attenuation coefficients without detailed compositional analysis. By incorporating additional parameters into the conventional Rutherford model, such as exponential and trigonometric functions, the model effectively capturescomplex variations in attenuation, enabling precise Zeff estimation. Model fitting was performedusing dual-energy data and evaluated using the percentage difference in error rates. Results: Compared with the Rutherford model, which recorded a maximum error rate of 0.55%, the proposed model demonstrated a significantly lower maximum error rate of 0.15%, highlightingits precision. Zeff estimates for various materials closely matched the reference values, confirmingthe improved accuracy of the model. Conclusions The proposed DECT-based model provides a practical and efficient approach to Zeff estimation, with potential applications in radiation oncology, particularly for accurate stopping power ratio calculations in proton and heavy ion therapies.

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.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.