Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 6 Articles
Background: The rising incidence of vertebral body fractures, vertebral infections and metastatic disease increases the need for diagnostic modalities with high specificity. Biopsy remains essential, yet comparative data on CT-guided versus intraoperative percutaneous fluoroscopy-guided biopsy are limited. Methods: This retrospective study compared two cohorts biopsied for spinal lesions between April 2015 and April 2024: CT-guided biopsy (n = 62), and intraoperative percutaneous biopsy (n = 154). Groups were analyzed for demographic and clinical characteristics, and diagnostic yield was defined by the conclusiveness of the primary biopsy; statistical comparisons were performed using Fisher’s exact test. Results: CT-guided biopsy yielded conclusive results in 46 of 62 cases (74.2%), whereas intraoperative, fluoroscopy-guided biopsy was conclusive in 41 of 154 cases (26.6%), representing a statistically significant difference (p < 0.001). In analogy, propensity score matching (1:1) resulted in similar significant (p < 0.001) results (CT-guided group vs. intraoperative, fluoroscopy-guided group: 86.7% vs. 35.6%) Conclusions: CT-guided biopsy demonstrated a substantially higher rate of conclusive results compared with intraoperative biopsy in this cohort. Further studies with larger and more balanced cohorts are needed to strengthen clinical recommendations....
Background: Challenges in conventional radiology services, such as long queues and manual administrative procedures, hinder the efficiency of radiology services. With the introduction of a webbased administrative and archiving information system, it is hoped that patient registration, examination recording, and the delivery of results can be carried out effectively and efficiently. Research Objective: To design and evaluate the impact of a web-based radiology administration information system on a suitable radiology department so that it can replace the existing manual administration system in the radiology department. Method: Using the Research and Development (R&D) method and Rapid Application Development (RAD), the system was functionally tested via Blackbox Testing (Equivalence Partitioning technique) and its satisfaction level was measured using the End User Computing Satisfaction (EUCS) method. Results: Expert validation results showed that out of 89 test cases, 85 were deemed valid, achieving a validity rate of 95.5%. Furthermore, user satisfaction testing indicated that respondents were satisfied with the system’s content, accuracy, format, ease of use, and timeliness. Conclusion: this information system is capable of addressing service issues and reducing the potential for data input errors as it is fully integrated, covering medical records, administration, examinations, and the retrieval of patient results....
Background/Objectives: Large language models (LLMs) are increasingly applied to medical image interpretation; however, their diagnostic accuracy and reliability in musculoskeletal radiology remain uncertain. This study evaluates the diagnostic performance and confidence calibration of LLMs in detecting and classifying bone tumors on radiographs. Methods: This retrospective observational study analyzed a dataset of 257 radiographs with confirmed diagnoses obtained from Radiopaedia, including normal studies and a spectrum of benign and malignant bone tumors. Cases were selected to ensure representation across multiple tumor types. Three LLMs (ChatGPT 5.3, X-ray Interpreter GPT-4.1, and X-ray Interpreter Gemini) evaluated each image using a standardized prompt assessing abnormality detection, tumor detection, classification, and confidence. Outcomes included diagnostic accuracy, false positive abnormality rates, false negative rates, tumor hallucination rates, and confidence calibration. Results: Abnormality detection was high across models, with Gemini demonstrating the highest sensitivity (up to 100%). Tumor detection was strongest in lesions with characteristic features, including osteosarcoma and osteochondroma. False negative rates varied substantially, with GPT-4.1 demonstrating the highest rate (29.9%), followed by ChatGPT (24.8%) and Gemini (6.6%). Primary diagnostic accuracy was highest for osteosarcoma in GPT-4.1 (80%), while ChatGPT 5.3 performed best in benign lesions, including osteochondroma (84.6%) and non-ossifying fibroma (76.9%). Tumor subtype classification remained limited across all models and was poorest for Ewing sarcoma (0% in ChatGPT and GPT-4.1; 10.3% in Gemini). False positive abnormality rates were highest in GPT-4.1 (40.7%), followed by Gemini (25.9%) and ChatGPT (13.5%). Tumor hallucination occurred only in Gemini (12.3%). All models demonstrated confidence miscalibration, with higher confidence observed in incorrect predictions and in tumor-negative cases. Conclusions: LLMs demonstrate strong performance in detecting radiographic abnormalities but remain limited in tumor subtype classification, particularly for diagnostically challenging lesions such as Ewing sarcoma. Elevated false positive and false negative rates, along with systematic overconfidence—especially in GPT-4.1—highlight important limitations for clinical use. These findings support the role of LLMs as adjunctive tools rather than independent diagnostic systems....
This study aimed to evaluate the diagnostic value of lung ultrasound (LUS) in detecting lung necrosis in children with community-acquired pneumonia (CAP), and to determine the sensitivity of the modified Lung Ultrasound Score (m-LUScore) in diagnosing necrotizing pneumonia (NP). METHODS: Children aged 2 months to 18 years hospitalized with CAP were prospectively enrolled. All participants underwent detailed clinical assessment, laboratory investigations (CBC, CRP), lung ultrasonography, serial chest radiographs, and chest CT when lung necrosis was suspected based on LUS or radiographic findings. The m-LUScore was recorded on the first and fifth days of admission. RESULTS: Among 161 children fulfilling the inclusion criteria for CAP, 42 had necrotizing pneumonia and 119 had non-necrotizing pneumonia. On day 1, the mean m-LUScore was significantly higher in NP than in non-NP cases (15.05 vs. 11.27, p < 0.001). A similar difference was observed on day 5 (15.64 vs. 8.65, p < 0.001). Logistic regression analysis identified the m-LUScore on both days as an independent detector of necrotizing pneumonia. CONCLUSIONS: Lung ultrasound is a sensitive and practical diagnostic tool for identifying necrotizing pneumonia in children. The m-LUScore provides a simple, noninvasive, and cost-effective method for detecting lung necrosis, especially valuable in settings with limited access to CT imaging....
Background: Differentiating adrenal adenomas from non-adenomatous lesions remains a critical challenge in the management of adrenal incidentalomas. Conventional unenhanced CT relies on attenuation thresholds of 10 HU and 20 HU, which present trade-offs between sensitivity and specificity. Objectives: To evaluate the diagnostic performance of unenhanced Spectral CT using the attenuation difference between 40 keV and 140 keV virtual monoenergetic images for differentiating adrenal adenomas from non-adenomatous lesions. Methods: In this retrospective single-center study, 60 patients with adrenal lesions who underwent unenhanced dual-energy CT were included. Mean attenuation values were measured on conventional images and on virtual monoenergetic images at 40 keV and 140 keV. The spectral attenuation difference (Δ40–140 keV) was calculated. ROC analysis was performed to determine the optimal threshold and diagnostic performance. Additional analyses included DeLong comparison of correlated ROC curves and bootstrap resampling to estimate 95% confidence intervals for the area under the curve. Results: Forty-nine lesions were adenomas and eleven were non-adenomatous. The optimal threshold for Δ40–140 keV was −17 HU. When evaluated as a continuous variable, Δ40–140 keV yielded an area under the curve of 0.940 (95% confidence interval: 0.851–1.000), compared with 0.939 (95% confidence interval: 0.870–0.992) for conventional unenhanced attenuation. DeLong comparison showed no statistically significant difference between the two curves (p = 0.980). Diagnostic performance was as follows: HU ≤ 10 (AUC 0.816, diagnostic accuracy 0.70), HU ≤ 20 (AUC 0.883, diagnostic accuracy 0.87), and Δ40–140 keV≤ −17 HU (AUC 0.940, diagnostic accuracy 0.90). The spectral attenuation difference demonstrated the highest overall diagnostic accuracy. Conclusions: Unenhanced Spectral CT using Δ40–140 keV improves discrimination between adrenal adenomas and non-adenomatous lesions compared with conventional attenuation thresholds. This technique may reduce indeterminate findings and limit the need for additional imaging....
Objectives: Secondary progressive multiple sclerosis (SPMS) is a natural transition from relapsing-remitting multiple sclerosis (RRMS) in many cases. However, whether and how these phenotypes differ on an individual basis is not fully understood, limiting timely diagnosis and management for SPMS. This study aimed to investigate how deep learning using 3-dimensional (3D) frameworks including VGG19, ResNet152, and DenseNet- 121 helped differentiate SPMS from RRMS based on routine clinical datasets, and what brain areas mostly contributed to this differentiation using model explanation techniques. Methods: We examined 140 participants (70 each for RRMS and SPMS) as part of an ongoing study comprising prospectively collected clinical and imaging data from routine healthcare. The data was curated to improve consistency and completeness using different strategies and were then randomly split by subject into training (n = 120) and held-out testing (n = 20). The former was used for model development through five-fold cross validation. Deep learning used T1-weighted, T2-weighted, and FLAIR brain MRI, with optional clinical variables (n = 6). A 3D gradient-weighted class activation mapping (Grad- CAM) technique was applied to identify brain areas of significance followed by ablation studies for additional insight. Results: Among the 3D frameworks validated, VGG19 was deemed the best. Based on MRI and the best 3D VGG19 model, different data curation strategies showed largely similar results. Additionally, the models combining clinical variables with MRI achieved equivalent or slightly greater performance than MRI-only models, with an average testing area under the receiver operating characteristic curve of 0.84 when datasets were fused at the flatten layer, best at 0.92, versus 0.82 and 0.89. Model explanation indicated brain regions of significance in distinguishing SPMS from RRMS individuals, including bilateral frontal lobes, left occipital and temporal lobes, and cerebellum. Conclusions: Overall findings suggest the potential of 3D deep learning models such as VGG19 for distinguishing SPMS from RRMS using routine brain MRI and clinical data, which, along with 3D Grad-CAM, could facilitate discovery of new biomarkers underlying disease worsening....
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