Technical Feasibility and Outcome of Cryoablation of Aneurysmal Bone Cysts in Pediatric Patients.

PURPOSE: To evaluate the clinical and imaging outcome of aneurysmal bone cysts (ABCs) in children using percutaneous cryoablation as the sole treatment. MATERIALS AND METHODS: This retrospective study included 7 children with a mean age of 8.7 years (range, 3.0-11.9 years) who underwent at least 1 cryoablation for ABC. Cryoablation needles were placed and guided by computed tomography (CT). Imaging follow-up and clinical examination were performed by radiography 1 and 6 months after the procedure and magnetic resonance (MR) imaging was performed 3 and 12 months after the procedure. Additional cryoablation was performed in case of MR imaging-detected recurrence. Data were analyzed by anatomical location, measurement of lesion volume, numbers of intralesional cysts, grade of mineralization (5-point Likert scale), pain (0 [none] to 4 [severe]), and grade of fluid-fluid levels (FFLs; 4-point Likert scale). RESULTS: Fourteen cryoablations were performed. Patients showed volume reduction, with 1 showing a complete response and 6 showing partial response. Following treatment, there was a substantial reduction in lesion volume compared with baseline, leading to a mean volume decrease of 81.9% (range, 66.8%-100%). The grade of mineralization (3.2 [SD ± 1.2] after therapy vs 1.1 [SD ± 0.3] at baseline), grade of FFL (1.6 [SD ± 0.5] after therapy vs 3.4 [SD ± 1.1] at baseline), and pain (0.29 [SD ± 0.4] after therapy vs 1.86 [SD ± 0.7] at baseline) significantly improved after therapy (all P < .05). One severe adverse event occurred. CONCLUSIONS: Cryoablation is an effective treatment option for ABC in children. Further research is needed to compare it with other techniques.

Artificial intelligence-based detection of paediatric appendicular skeletal fractures: performance and limitations for common fracture types and locations.

BACKGROUND: Research into artificial intelligence (AI)-based fracture detection in children is scarce and has disregarded the detection of indirect fracture signs and dislocations. OBJECTIVE: To assess the diagnostic accuracy of an existing AI-tool for the detection of fractures, indirect fracture signs, and dislocations. MATERIALS AND METHODS: An AI software, BoneView (Gleamer, Paris, France), was assessed for diagnostic accuracy of fracture detection using paediatric radiology consensus diagnoses as reference. Radiographs from a single emergency department were enrolled retrospectively going back from December 2021, limited to 1,000 radiographs per body part. Enrolment criteria were as follows: suspected fractures of the forearm, lower leg, or elbow; age 0-18 years; and radiographs in at least two projections. RESULTS: Lower leg radiographs showed 607 fractures. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were high (87.5%, 87.5%, 98.3%, 98.3%, respectively). Detection rate was low for toddler's fractures, trampoline fractures, and proximal tibial Salter-Harris-II fractures. Forearm radiographs showed 1,137 fractures. Sensitivity, specificity, PPV, and NPV were high (92.9%, 98.1%, 98.4%, 91.7%, respectively). Radial and ulnar bowing fractures were not reliably detected (one out of 11 radial bowing fractures and zero out of seven ulnar bowing fractures were correctly detected). Detection rate was low for styloid process avulsions, proximal radial buckle, and complete olecranon fractures. Elbow radiographs showed 517 fractures. Sensitivity and NPV were moderate (80.5%, 84.7%, respectively). Specificity and PPV were high (94.9%, 93.3%, respectively). For joint effusion, sensitivity, specificity, PPV, and NPV were moderate (85.1%, 85.7%, 89.5%, 80%, respectively). For elbow dislocations, sensitivity and PPV were low (65.8%, 50%, respectively). Specificity and NPV were high (97.7%, 98.8%, respectively). CONCLUSIONS: The diagnostic performance of BoneView is promising for forearm and lower leg fractures. However, improvement is mandatory before clinicians can rely solely on AI-based paediatric fracture detection using this software.

Ultra-low-dose lung multidetector computed tomography in children - Approaching 0.2 millisievert.

PURPOSE: To compare objective and subjective parameters in image quality and radiation dose of two MDCTs (helical 64 detector CT vs. axial 256 detector CT) in paediatric lung CT. METHODS: Radiation dose and image quality were compared between non-enhanced lung CT from a helical 64-slice multidetector CT (MDCT 1) and a 256-slice scanner (MDCT 2) with axial wide-cone acquisition and using deep learning image reconstruction. In 23 size-matched paediatric studies (age 2-18 years) from each scanner, the radiation exposure, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image sharpness and delineation of small airways were assessed. Subjective image quality was rated by 6 paediatric radiologists. RESULTS: While MDCT 2 provided higher SNR and CNR, subjective image quality was not significantly different between studies from both scanners. Radiation exposure was lower in studies from MDCT 2 (CTDIvol 0.26 ± 0.14 mGy, effective dose 0.23 ± 0.11 mSv) than from MDCT 1 (CTDIvol 0.96 ± 0.52 mGy, effective dose 1.13 ± 0.58 mSv), p < 0.001. Despite lower radiation dose for the scout images, the relative scout-scan-ratio increased from 2.64 ± 1.42 % in MDCT 1 to 6.60 ± 5.03 % in MDCT 2 (p = 0.001). CONCLUSIONS: By using latest scanner technology effective radiation dose can be reduced to 0.1-0.3 mSv for lung CT in children without compromising image quality. Scout image dose increasingly accounts for substantial portions of the total scan dose and needs to be optimized. In children CT should be performed on state-of-the-art MDCT scanners with size-adapted exposure protocols and iterative reconstruction.

Analysis of pneumatization and neurovascular structures of the sphenoid sinus using cone-beam tomography (CBT).

BACKGROUND: The sphenoid sinus is a frequent target of paranasal sinus surgery. Because of the high risk of injuring the surrounding structures (e.g. internal carotid artery, optical nerve) a preoperative imaging is absolutely necessary. PURPOSE: To analyze the possibilities of cone-beam computed tomography (CBCT), which is especially quite a new technique in ENT, in the evaluation of the sphenoid sinus, its surrounding structures, and the corresponding anatomical variations. MATERIAL AND METHODS: This was a retrospective, single-centre study of 580 patients (1160 sides = cases). The Accu-I-Tomo-F17 was used. Pneumatization of sphenoid sinus, course of internal artery, course of optical nerve, and dehiscence of the bony canals were evaluated. RESULTS: In the case of pneumatization a type I (completely missing or minimal sphenoid sinus) was found in two patients (0.3%), type II (posterior wall of sphenoid sinus is in front of the anterior wall of the sella) in 38 patients (6.6%), type III (posterior wall is between anterior and posterior wall of sella) in 332 patients (57,2%), type IVa (posterior wall is behind the posterior wall of sella without air dorsal the sella) in 104 patients (17.9%), and type IVb (similar to type IVa but with air dorsal the sella) in 104 patients (17.9%). In 1025 cases (89.5%) a smooth course of the internal carotid artery was found whereas a free course could be detected in 120 cases (10.5%). Defects of the bony canal of the optical nerve were found in 16.7% and of the internal carotid artery in 2.7% of the cases. The optical nerve showed a free course through the sphenoid in 151 cases (13.7%) and a smooth course in 1007 cases (87.0%). CONCLUSION: CBCT could evaluate all relevant anatomic structures and answer the questions of different anatomical variants. A modified classification of the pneumatization of the sphenoid sinus could be described. Frequencies of anatomical variations are in accordance with the current literature of CT research.