Cutaneous angiosarcomas are uncommon malignancies which account about 1% of sarcomas. They are found most commonly in the head and neck regions, frequently on the scalp. Although preferred treatment has been combined surgery and postoperative radiation therapy, the extensiveness and multiplicity of the lesions set limits to such an approach and the patient is often referred for radiotherapy without surgery. As the entire scalp usually needs to be treated, radiation therapy is a challenging problem to radiation oncology staffs. We report a case of angiosarcoma of the scalp, which was treated successfully by radiation therapy with a simple and repeatable method using mixed photon and electron beam technique. Using a bolus to increase the surface dose of the scalp and to minimize dose to the normal tissues of the brain desirable but difficult technically to be well conformed o the three dimensional curved surface such as vertex of the head. A helmet made of thermoplastics filled with paraffin was elaborated and used for the treatment, resulting of the relatively uniform surface doses along the several points measured on the scalp, the difference among the points not exceeding 7% of the prescribed dose by TLD readings.
Brain ; Head ; Head Protective Devices ; Hemangiosarcoma* ; Humans ; Neck ; Paraffin ; Radiation Oncology ; Radiotherapy* ; Reading ; Sarcoma ; Scalp*

The purpose of this study was to examine the effects of dietary caffeine supplementation on bone mineral density and bone mineral content in ovariectomized rats. Twenty eight female Sprague-Dawley rats (body weight 210 +/- 5 g) were divided into two groups, ovariectomy (OVX) and Sham groups, which were each randomly divided into two subgroups that were fed control and control supplemented with caffeine diets (caffeine 0.03% diets). All rats were fed on experimental diet and deionized water ad libitum for 6 weeks. Bone mineral density (BMD) and bone mineral content (BMC) were measured using PIXImus (GE Lunar Co, Wisconsin) in spine and femur. Serum alkaline phosphatase activity (ALP) and osteocalcin and urinary DPD crosslinks value were measured as markers of bone formation and resorption. The results of this study indicate that body weight gain and food intake were higher in OVX groups than in Sham groups regardless of diets. There were no differences weight gain between the control and caffeine groups in both OVX and Sham groups. Within the OVX groups, serum Ca concentration was lower in rats fed caffeine than in rats fed the control diet. Serum ALP, osteocalcin, urinary Ca, and phosphate were not different in each group. Spine BMD, spine BMD/weight, and spine BMC/weight, femur BMD/weight and femur BMC/weight of ovariectomy groups were significantly lower than Sham groups. Within the OVX group, there were no differences in spine BMD and BMC and femur BMD and BMC. These results indicate that no significant differences in spine and femur BMD were found due to 0.03% caffeine intakes in diet in OVX rats for 6 weeks. No negative effect of caffeine in 0.03% diet on bone mineral density were found in the present study. Further investigation of the relation between caffeine and bone mineral density are warranted.
Alkaline Phosphatase ; Animals ; Body Weight ; Bone Density ; Caffeine ; Diet ; Eating ; Female ; Femur ; Humans ; Osteocalcin ; Osteogenesis ; Ovariectomy ; Rats ; Rats, Sprague-Dawley ; Salicylamides ; Spine ; Water ; Weight Gain

Purpose: This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95. Methods: We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance. Results: MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation. Conclusion MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

Purpose: This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95. Methods: We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance. Results: MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation. Conclusion MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

Purpose: This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95. Methods: We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance. Results: MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation. Conclusion MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

Purpose: This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95. Methods: We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance. Results: MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation. Conclusion MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

Purpose: This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95. Methods: We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance. Results: MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation. Conclusion MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

OBJECTIVES: Patients with schizophrenia often present the idea of reference in social situations ; however, the number of research studies examining the nature of the idea of reference and the visual searching pattern in social situations is limited. The aim of this study was to investigate behavioral and visual searching characteristics of patients with schizophrenia in social situations in which the idea of reference can be provoked. METHODS: Eighteen subjects with schizophrenia (eight males) and 18 healthy volunteers (seven males) performed the idea-of-reference-provoking task, which was composed of movie clips with scenes of two women sitting on a bench 1 m away. The participants' reactions were rated using questionnaires for self-reference, malevolent intentions, and anxiety. Visual scan path was monitored during performance of the task. RESULTS: There were significant group differences in the reactions on self-reference, malevolent intentions, and anxiety. The visual searching pattern in patients with schizophrenia was to avoid looking at the women's body area in every movie clip. However, there was no significant difference in the face area in both groups. CONCLUSION: A distinct visual strategy in schizophrenia may affect the self-referential bias and paranoid response. The absence of difference in attention to a core information region (face) may suggest the possibility of inferential errors as well as the cause of self-referential bias and paranoid responses.
Anxiety ; Bias (Epidemiology) ; Female ; Healthy Volunteers ; Humans ; Intention ; Surveys and Questionnaires ; Schizophrenia*

BACKGROUND: Diagnosis of tuberculous pleurisy is sometimes difficult using conventional diagnostic methods. We have investigated the utility of pleural fluid cell IFN-gamma production assay in the diagnosis of tuberculous pleurisy. METHODS: We prospectively performed pleural fluid cell IFN-gamma production assay in 39 patients with tuberculous pleural effusions (TPE) and in 26 patients with nontuberculous pleural effusions (NTPE) (13 malignant pleural effusions and 13 parapneumonic effusions). Pleural fluid cells were cultured in DMEM media and stimulated with purified protein derivatives (PPD), and phytohemagglutinin (PHA) for 24 hr. The amount of IFN-gamma released in the culture supernatant was quantitated by IFN-gamma ELISA assay. We have also measured adenosine deaminase (ADA) activities and IFN-gamma concentrations in the pleural fluid. RESULTS: 1) The pleural fluid levels of ADA activity and IFN-gamma concentrations were significantly higher in TPE than NTPE (p<0.01). 2) IFN-gamma production in TPE cells stimulated by PPD (755,266+/-886,636 pg/ml) was significantly higher than NTPE cells (3,509+/-6,980 pg/ml) (p<0.01). By considering the fact that IFN-gamma concentrations over 10,000 pg/ml is a criteria for the diagnosis of TBE, sensitivity and specificity of the test were 97.4 and 92.3%, respectively. 3) The ratios of IFN-gamma production by the stimulation with PPD and PHA (PPD/PHA) were significantly higher in TPE cells (59+/-85) than NTPE cells (5+/-18)(p<0.01). Considering the criteria for the diagnosis of TBE as PPD/PHA ratio over 5, sensitivity and specificity of the test were 76.9 and 92.3%, respectively. CONCLUSION: Pleural fluid cell IFN-gamma production assay may be useful for the diagnosis of tuberculous pleurisy.
Adenosine Deaminase ; Diagnosis* ; Enzyme-Linked Immunosorbent Assay ; Humans ; Pleural Effusion ; Pleural Effusion, Malignant ; Pleurisy ; Prospective Studies ; Tuberculosis ; Tuberculosis, Pleural*

BACKGROUND: Although transfusion is a paramount life-saving therapy, there are multiple potential significant risks. Therefore, all adverse transfusion reaction (ATR) episodes require close monitoring. Using the computerized reporting system, we assessed the frequency and pattern of non-infectious ATRs. METHODS: We analyzed two-year transfusion data from electronic medical records retrospectively. From March 2013 to February 2015, 364,569 units of blood were transfused. Of them, 334,582 (91.8%) records were identified from electronic nursing records. For the confirmation of ATRs by blood bank physicians, patients' electronic medical records were further evaluated. RESULTS: According to the nursing records, the frequency of all possible transfusion-related events was 3.1%. After the blood bank physicians' review, the frequency was found to be 1.2%. The overall frequency of febrile non-hemolytic transfusion reactions (FNHTRs) to red blood cells (RBCs), platelet (PLT) components, and fresh frozen plasmas (FFPs) were 0.9%, 0.3%, and 0.2%, respectively, and allergic reactions represented 0.3% (RBCs), 0.9% (PLTs), and 0.9% (FFPs), respectively. The pre-storage leukocyte reduction significantly decreased the frequency of FNHTRs during the transfusion of RBCs (P<0.01) or PLTs (Pfalling dots0.01). CONCLUSIONS: The frequency of FNHTRs, allergic reactions, and "no reactions" were 22.0%, 17.0%, and 60.7%, respectively. Leukocyte-reduction was associated with a lower rate of FNHTRs, but not with that of allergic reactions. The development of an effective electronic reporting system of ATRs is important in quantifying transfusion-related adverse events. This type of reporting system can also accurately identify the underlying problems and risk factors to further the quality of transfusion care for patients.
Blood Transfusion/*adverse effects/statistics & numerical data ; Humans ; Republic of Korea/epidemiology ; Retrospective Studies ; Tertiary Care Centers ; Transfusion Reaction/*epidemiology/etiology