Objective: In the initial phases of parenchymal diseases, the only finding would be an increase in organ size. We aimed to provide percentile charts of solid intraabdominal organ sizes by age for Turkish children on contrast-enhanced computed tomography images and reveal relative size ratios. Materials and Methods: A total of 800 abdominal computed tomography examinations of otherwise healthy children (468 males, 332 females) were enrolled. The transverse diameter and lengths of both liver lobes, the thickness of the pancreas, width and length of the spleen, and anteroposterior diameters and lengths of both kidneys were measured. Differences in mean diameters among ages were compared with the analysis of variance test. Pearson’s correlation was assessed to depict the association of size with age and waist circumference. Results: Percentile charts for all measured size parameters that presented statistically significant positive correlations with age and waist circumference were provided. There were constant ratios of the right liver lobe to the left liver lobe (1.9 ± 0.37), right liver lobe to the right kidney (1.56 ± 0.26), left to the right kidney (1.03 ± 0.09), and spleen to the left kidney (1 ± 0.2) lengths in every age groups. Age (years)-dependant regression equation for waist circumference (mm) was depicted as “22 × age + 408.” Conclusion: Age-based percentile charts of solid intraabdominal organ diameters along with relative organ size ratios were provided. Cite this article as: Bayramoğlu Z, Ayyıldız H, Ersoy B. Reference ranges of age-based liver, spleen, pancreas, and kidney size in conjunction with waist circumference in children. Turk Arch Pediatr. 2022;57(2):175-185.

Background: Transperineal magnetic resonance (MRI)/ultrasound (US) fusion-guided prostate biopsy has emerged as a promising alternative to the transrectal approach by improving lesion targeting and reducing infectious complications. However, real-world data addressing factors that influence the detection of clinically significant prostate cancer (csPCa), including imaging characteristics and procedural experience, remain limited. Objective: To evaluate the diagnostic performance, safety profile, and independent predictors of csPCa detection in patients who underwent transperineal MR/US fusion-guided prostate biopsy, with particular emphasis on PIRADS category, prostate-specific antigen (PSA) level, and procedural learning curve. Methods: In this study, patient data were prospectively recorded in a routinely maintained institutional database, while the present analysis was conducted retrospectively. A total of 136 patients with clinical suspicion of prostate cancer—defined as elevated prostate-specific antigen (PSA), abnormal digital rectal examination, or PIRADS ≥3 on multiparametric MRI—underwent transperineal MR/US fusion-guided biopsy between January 2023 and October 2024. Results: Prostate cancer was detected in 45.5% of patients, whereas csPCa was identified in 32.3%. The PIRADS category emerged as the strongest independent predictor of csPCa detection, with PIRADS-5 lesions showing a significantly greater likelihood of csPCa than PIRADS-3 lesions (OR 6.70, p = 0.006). The PSA level was also independently associated with csPCa detection (OR 1.06 per ng/mL increase, p = 0.033). Although csPCa detection rates increased across learning curve groups, procedural experience was not an independent predictor after adjustment. The procedure demonstrated a favorable safety profile, with a low rate of infectious and noninfectious complications despite minimal use of antibiotic prophylaxis. The multivariable model showed moderate explanatory power and acceptable overall classification accuracy. Conclusions: Transperineal MR/US fusion-guided prostate biopsy provides reliable detection of clinically significant prostate cancer with a low complication rate and consistent performance across different stages of institutional experience. The PIRADS category and PSA level remain key determinants of csPCa detection, supporting the integration of MRI-based risk stratification into contemporary prostate cancer diagnostic methods.

Several data sets have been collected and various artificial intelligence models have been developed for COVID‐19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground‐glass opacities by conventional CXR has limited the use of this modality in the diagnostic work‐up of COVID‐19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well‐established deep CXR models for this new data set and develop a machine learning‐based non‐maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID‐19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID‐19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID‐19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID‐19 on chest CTX examination. COVID‐Net, a specially designed convolutional neural network, was used to detect cases of COVID‐19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID‐Net variants (COVIDNet‐CXR3‐A, ‐B, ‐C/COVIDNet‐CXR4‐A, ‐B, ‐C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID‐Net's six variants, the area under the curve (AUC) using the ensemble COVID‐Net CXR 4A‐4B‐3C was .78, sensitivity 67%, specificity 95%; COVID‐Net CXR 4a‐3b‐3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID‐Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature.