Sacral pressure sores have been treated by a variety of surgical methods. complete treatment needs wide excision and coverage with healthy tissue which has constant and sufficient blood supply. Use of gluteus maximus muscle flap with or without overlying skin is a revolutionary method because of the reliability of blood supply. However, it is technically a little bit complicated, and future reconstruction for recurrent decubitus is especially limited in paraplegic patients. The development of gluteal perforator-based flap with para-sacral perforator introduce a new treatment modality for the sacral pressure sores. Total 10 cases of sacral pressure sores were treated with gluteal perforator-based flap. There were minimal postoperative complications except wound dehiscence in one case. This flap has a many advantage of no transection or sacrifice of the gluteus maximus muscle, elevation time for the flap is short, reliable blood flow of the perforator, large rotation arc and no post-operative hindrance to walking in patients who are not paraplegic. The disadvantages of this perforator-based flaps are the anatomical variation in the location of perforators and the need for technically careful dissection.
Humans ; Postoperative Complications ; Pressure Ulcer* ; Skin ; Walking ; Wounds and Injuries

A 43 years old woman had suffered from a lower back pain for 2 months. She experienced pain aggravation after spinal manipulative therapy that was practiced by non-licentiate. Physical examination showed tenderness on L1 and L2 spinous processes. Radionuclide bone scan with 99mTc-MDP showed increased radioactivity of L1, L2 vertebral bodies. The MRI finding showed low signal intensity of L1 and L2 vertebral bodies in T1-weighted image and high signal intensity in T2-weighted image. Needle biopsy finding showed fibrosis and inflammatory cell invasion of bone marrow. We concluded that she had tuberculous spondylitis and non-detection or negligent treatment of a preexisting disease contributed to aggravation of her symptoms. We report one case of tuberculous spondylitis aggravated by spinal manipulative therapy with review of literatures.
Adult ; Biopsy, Needle ; Bone Marrow ; Female ; Fibrosis ; Humans ; Low Back Pain ; Magnetic Resonance Imaging ; Musculoskeletal Manipulations* ; Physical Examination ; Preexisting Condition Coverage ; Radioactivity ; Spondylitis* ; Technetium Tc 99m Medronate

Twenty seven patients with known rectal cancer were evaluated with CT and CT findings were correlated with surgical and pathologic results on the aspect of perirectal fat infiltration and lymph node involvement. The accuracy in assessment of perirectal fat infiltration was 77.8% (21 of 27); sensitivity, 73.3% (11 of 13); specificity, 83.3% (10 of 12). In the detection of lymph node involvement, lymph nodes were divided into five groups according to the arterial teritories. Overall accuracy in the evaluation of lymph node involvement was 86.7%. Accuracy of peritumoral lymph node involvement was 51.9% (14 of 27); sensitivity, 42.9%(9 of 21); specificity 83.3% (5 of 6). Accuracy of internal iliac lymph node involvement was 88.9% (24 of 27); sensitivity, 85.7% (6 of 7); specificity, 90.0% (18 of 20). Of the common and external iliac lymph node, accuracy was 100% (27 of 27); sensitivity, 100% (2 of 2); specificity, 100% (25 of 25). Of the aortic bifurcation and mid sacral lymph node, accuracy was 92.6% (25 of 27); sensitivity, 50% (2 of 4); specificity, 100% (23 of 23). In regard to the inferior mesenteric lymph node, no lymphadenopathy was found on CT and pathologic results. In conclusion, CT has limited value in evaluating rectal cancer but with the satisfactory outcome in assessment of perirectal fat infiltration and lymph node, involvement except peritumoral node preoperative CT is useful in the evaluation of rectal cancer.
Humans ; Lymph Nodes* ; Lymphatic Diseases ; Rectal Neoplasms* ; Sensitivity and Specificity

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.

Checkrein deformity is characterized by the dynamic status of the hallux, in which flexion deformity is aggravated by ankle dorsiflexion and relieved by ankle plantarflexion. In most cases, a checkrein deformity occurs secondary to trauma or following surgery.It has been suggested that the flexor hallucis longus tendon tethers or entraps scar tissue or fracture sites. Improvement with conservative treatment is difficult once the deformity has already become entrenched, and surgical management is usually required in severe cases. Various surgical options are available for the correction of checkrein deformities. It includes a simple release of adhesion at the fracture site; lengthening of the flexor hallucis longus by Z-plasty at the fracture site combined with the release of adhesion; lengthening of the flexor hallucis longus by Z-plasty at the midfoot, retromalleolar, or tarsal tunnel area; and flexor hallucis longus tenotomy with interphalangeal arthrodesis for recurrent cases. This review aimed to summarize the overall etiology, relevant anatomy, diagnosis, and treatment of checkrein deformities described in the literature.

PURPOSE: This study was performed in order to compare unenhanced CT with diffusion-weighted, T2-weight-ed,and fast FLAIR MR imaging in the detection of hyperacute cerebral ischema induced in a dog and to deter-mine whichmodality first detected cerebral ischemia. MATERIALS AND METHODS: Experimental cerebral infarction was induced bythe occlusion of intracerebral arter-ies using embolic materials (polyvinyl-alcohol, 300 -6 00 micro) introducedthrough a microcatheter into the internal carotid artery of five dogs weighing 12 -20 kg. Serial CT and MR imageswere obtained at one hour intervals from one to five hours after occlusion, and were analyzed independently by tworadiologists. We assessed changes in attenuation, as seen on unenhanced CT and the signal intensity of the lesionon each MR image, and measured the contrast-to-noise ratio (CNR) of the lesions. RESULTS: Ischemic lesions weredetected on unenhanced CT 1 -3 hours after occlusion of cerebral arteries. In all dogs, the lesions were detectedearliest on diffusion-weighted images obtained at 1 hour. They were detect-ed on T2-weighted images at 3 -5 hoursand on fast FLAIR images of 2 -5 hours. The CNR of ischemic lesions increased gradually during the 5-hour period.It was highest on diffusion-weighted images, while on unen-hanced CT, T2-weighted, and fast FLAIR images it wassimilar. CONCLUSION: Hyperacute ischemic lesions were detected earliest on diffusion-weighted images, and earlieron unenhanced CT than on fast FLAIR or T2-weighted MR image.
Animals ; Brain Ischemia ; Carotid Artery, Internal ; Cerebral Arteries ; Cerebral Infarction* ; Dogs* ; Magnetic Resonance Imaging*