ABSTRACT
SUMMARY OBJECTIVE: This study was carried out to evaluate the injuries in pediatric earthquake victims due to the 2023 Turkey-Syria earthquakes with computed tomography and determine the anatomotopographic distribution of injuries. METHODS: The material of this retrospective study consisted of the computed tomography findings of 257 pediatric cases injured in the 2023 Turkey-Syria earthquakes, and those were divided into subgroups based on their age group, i.e., 0-4, 5-9, 10-14, and 15-18 years, and the type of injury, i.e., head, maxillofacial, thoracic, abdominal, pelvic, and spinal injuries. RESULTS: Earthquake-related injuries had been detected in 102 (39.6%) patients. Of the 29 patients with multiple injuries, 17, 10, and 2 had injuries in two, three, and four topographic regions, respectively. The most common injury was a head injury, which was detected in 48 (18.7%) cases, followed by thoracic injury, spinal injury, pelvic fracture, abdominal injury, and maxillofacial fracture, which were detected in 40 (15.6%), 22 (8.5%), 19 (7.4%), 10 (3.9%), and 6 (2.3%) patients, respectively. The cranial bone fractures and intracranial injuries were significantly more frequent in the 0-4 years age group compared with other age groups (p=0.028 and p=0.024, respectively). The rib fractures with spinal and pleural injuries were significantly more common in the 15-18 years age group compared with others (p=0.016, p=0.004, and p=0.002, respectively). CONCLUSION: The head injury was the most common earthquake-related injury in pediatric cases. Herein, it was more common in younger children compared with other age groups, whereas rib, spine, and pleural injuries were more common in older children.
ABSTRACT
SUMMARY OBJECTIVE: This study aimed to evaluate the feasibility of texture analysis on T2-weighted axial images in differentiating affected and nonaffected ovaries in ovarian torsion. METHODS: We included 22 torsioned ovaries and 19 healthy ovaries. All patients were surgically proven ovarian torsion cases. On T2-weighted axial images, ovarian borders were delineated by the consensus of two radiologists for magnetic resonance imaging-based texture analysis. Statistical differences between texture features of affected and nonaffected ovaries were assessed. RESULTS: A total of 44 texture features were extracted from each ovary using LIFEx software. Of these, 17 features were significantly different between affected and nonaffected ovaries in ovarian torsion. NGLDM_Coarseness and NGLDM_Contrast, which are the neighborhood gray-level difference matrix parameters, had the largest area under the curve: 0.923. The best cutoff values for the NGLDM_Contrast and NGLDM_Coarseness were 0.45 and 0.01, respectively. With these cutoff levels, NGLDM_Contrast had the best accuracy (85.37%). CONCLUSION: Magnetic resonance imaging-based texture analysis on axial T2-weighted images may help differentiate affected and nonaffected ovaries in ovarian torsion.