No abstract available.
Induction Chemotherapy* ; Uterine Cervical Neoplasms*

A case of multiple skeletal tuberculosis with spina ventosa proved by radiological and pathologicaI methods in 3 year old Korean male child is reported. It was treated with antituberculous chemotherapy (triple method of PAS, INAH, streptomycin). At follow up check within 2 years, we obtained good healing process without other complication.
Child ; Drug Therapy ; Follow-Up Studies ; Humans ; Male ; Methods ; Tuberculosis

No abstract available.
Cervical Intraepithelial Neoplasia* ; Cervix Uteri* ; Diagnosis* ; Female

OBJECTIVE: Excessive external rotation of the hemiplegic foot is a common problem of hemiplegic gait. There has been a few report on etiological investigation and corrective measurement of an excessive external rotation of hemiplegic foot. Thus we present a newly designed Torque heel to correct the external rotation of hemiplegic foot. METHOD: Ten hemiparetic patients with an excessive external rotation of affected foot participated in this study. All of the participants were able to walk at least 10 meters with metal a ankle foot orthosis (AFO) using a single cane. Each of these patients was placed on four tries of walk: (1) on a bare foot; (2) with an AFO; (3) with an AFO and a quarter inch of lateral wedge; and (4) with an AFO and the Torque heel . Gait patterns were analysed by the Vicon 370, three dimensional motion analyser. RESULTS: The speed and stride length increased in all tries except for the bare foot walk. Those who walked with the assistive devices showed no difference in the speed and stride length. All the participants showed an increased in external rotation of pelvis and ankle. Those who walked with an AFO and Torque heel presented a decrease in the external rotation of foot and pelvis. The hip and ankle motions of the hemiplegic limbs were not affected with the AFO and Torque heel . A significant degree of correction in pelvic rotation with an AFO and Torque heel was noted. CONCLUSIONS: This study indicates that an AFO with Torque heel is beneficial to the correction of external rotation of a hemiplegic foot. And the excessive external rotation of hemiplegic foot may be due possibly to the external rotation of pelvis.
Ankle ; Canes ; Extremities ; Foot Orthoses ; Foot* ; Gait Disorders, Neurologic ; Gait* ; Heel* ; Hip ; Humans ; Pelvis ; Self-Help Devices ; Torque*

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

BACKGROUND: Noninvasive measurements that relate to the extent and severity of coronary atherosclerosis have long been sought for clinical screening of patients with chest pain syndromes and for use in clinical trials. Intima-media thickeness (IMT) of the carotid artery has been suggested to be associated with coronary artery atherosclerosis. In this study, we tried to assess the relation of carotid artery atherosclerosis by B-mode ultrasonography with presence and severity of coronary artery disease. METHOD: We studied 57 patients (36 men, 21 women) with ischemic heart disease, mean age 65+/-8 yrs (48 to 83 yrs), who underwent both coronary angiography and carotid ultrasonography with 10 MHz transducer. The patients who had received revascularization procedure were excluded. We classified the patients into two groups, the control group without significant coronary stenosis (18 patients) and the coronary artery disease (CAD) group (39 patients) with significant luminal stenosis (> or =50%). The CAD group was divided into single vessel disease group (SVD, 19 patients) and multivessel disease group (MVD, 20 patients). IMT was measured in far wall of common carotid artery (CCA) at 10 mm proximal to carotid bulb and abnormal IMT was defined if the measurement was greater than mean IMT+2SD of control group. Serum total cholesterol (TC), low density lipoprotein (LDL), high density lipoprotein (HDL), triglyceride (TG), Lipoproteinp (a)(Lp(a)) were measured and history of hypertension, diabetes mellitus, and smoking were investigated. RESULTS: A significant difference in IMT of the CCA was found between control and CAD group (0.76+/-0.09 mm vs. 0.97+/-0.20 mm; p<0.0001). Also a significant difference in the number of atherosclerotic plaque was found between the two groups (control; 0.67+/-1.14 vs. CAD; 1.87+/-1.75; p<0.005). In the CAD group, both mean IMT and numbers of athero-sclerotic plaque tended to increase in MVD group compared with SVD group (1.03 mm vs. 0.91 mm; p=NS, 2.65 vs. 1.05; p<0.05). The sensitivity of IMT for prediction of significant CAD was 66.7%, the specificity 83.3%, the positive predictive value 89.7%, and the negative predictive value 53.6%. The sensitivity of plaque presence on the carotid artery for prediction of CAD was 71.8%, the specificity 61.3%, the positive predictive value 80.3% and the negative predictive value 50.5%. Among risk factor, diabetes mellitus and Lp (a) were correlated well with IMT of CCA, Hypertension was correlated with atherosclerotic plaque. History of smoking was correlated with coronary artery disease. CONCLUSION: Increases in IMT and plaque of the carotid artery, as measured noninvasively by ultrasonography, can be used as a predictor of significant coronary artery stenosis.
Atherosclerosis ; Carotid Arteries* ; Carotid Artery, Common ; Chest Pain ; Cholesterol ; Constriction, Pathologic ; Coronary Angiography ; Coronary Artery Disease ; Coronary Stenosis* ; Coronary Vessels* ; Diabetes Mellitus ; Humans ; Hypertension ; Lipoproteins ; Male ; Mass Screening ; Myocardial Ischemia ; Phenobarbital ; Plaque, Atherosclerotic* ; Risk Factors ; Sensitivity and Specificity ; Smoke ; Smoking ; Transducers ; Triglycerides ; Ultrasonography