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.

SUMMARY OBJECTIVE: This study aimed to determine the computed tomography findings associated with very recent catastrophic 2023 Turkey-Syria earthquake-related injuries and their anatomotopographic distribution in the adult population. METHODS: The incorporated computed tomography scans of 768 adult cases who had been admitted to the hospital and had undergone computed tomography imaging after these tragic disasters had been examined on the Teleradiology Reporting System of the Turkish Ministry of Health. To this end, the injuries were classified into six categories: head, thoracic, spinal, pelvic, extremity, and abdominal injury, with three age groups (18–34, 35–64, and ³65 years) and four different imaging intervals (<24, 24–48, 49–72, and >72 h). RESULTS: This study incorporated 316 (41.1%) cases on the first day, 57 (7.5%) on the second day, 219 (28.5%) on the third day, and 176 (22.9%) on the fourth day after the earthquake or later. Of the 768 cases, 109 (14.2%) had a head injury, 100 (13.0%) had a thoracic injury, 99 (12.9%) had a spinal injury, 51 (6.6%) had a pelvic injury, 41 (5.4%) had an extremity injury, and 11 (1.4%) had an abdominal injury. CONCLUSION: In these regrettable earthquake disasters, we determined a high ratio of head injuries, which was closely followed by thoracic and spinal injuries, in our preliminary outcomes for the pediatric population, Part I. The frequency of abdominal injuries was low among individuals who experienced the earthquake. Last but not least, we have noticed a higher likelihood of spinal injury in individuals older than 65 years in the studied population.

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.