A rare entity in a pediatric patient: coexistence of emphysematous cystitis and emphysematous pyelonephritis.

BACKGROUND: Emphysematous cystitis (EC) and emphysematous pyelonephritis (EPN) are rare urinary tract infections. They have a wide spectrum of clinical manifestations; ranging from asymptomatic to septic shock at presentation. In children, EC and EPN are rare complications of urinary tract infections (UTIs). Their diagnosis is based on clinical manifestations, laboratory results and characteristic radiological findings of gas within the collecting system, renal parenchyma and/or perinephric tissue. Computed tomography is the best radiological option in the diagnosis of EC and EPN. Despite the availability of various treatment modalities including medical and/or surgical treatment alternatives, these life-threatening conditions have high mortality rates reaching up to 70 percent. CASE: Urinary tract infection was detected in the examinations of an 11-year-old female patient suffering from lower abdominal pain, vomiting and dysuria for two days. Air was detected in the bladder wall on X-ray. EC was detected in the abdominal ultrasonography. Air formations in the bladder lumen and calyces of both kidneys in abdominal computed tomography confirmed the presence of EPN. CONCLUSIONS: Individualized treatment should be instituted according to the severity of EC and EPN, and the overall health condition of the patient.

Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma.

BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis of chronic otitis media between January 2010 and January 2021 in our clinic were reviewed retrospectively. The patients were classified into the chronic otitis group without cholesteatoma (n=34) and the chronic otitis group with cholesteatoma (n=41) according to the presence of cholesteatoma at surgery. A dataset was created from the preoperative computed tomography images of the patients. In this dataset, the success rates of artificial intelligence in the diagnosis of cholesteatoma were determined by using the most frequently used artificial intelligence models in the literature. In addition, preoperative MRI were evaluated and the success rates were compared. RESULTS: Among the artificial intelligence architectures used in the paper, the lowest result was obtained in MobileNetV2 with an accuracy of 83.30%, while the highest result was obtained in DenseNet201 with an accuracy of 90.99%. In our paper, the specificity of preoperative magnetic resonance imaging in the diagnosis of cholesteatoma was 88.23% and the sensitivity was 87.80%. CONCLUSION: In this study, we showed that artificial intelligence can be used with similar reliability to magnetic resonance imaging in the diagnosis of cholesteatoma. This is the first study that, to our knowledge, compares magnetic resonance imaging with artificial intelligence models for the purpose of identifying preoperative cholesteatomas.

Application of different methods used to measure the apparent diffusion coefficient of renal cell carcinoma on the same lesion and its correlation with ISUP nuclear grading.

PURPOSE: To determine the most frequently used different apparent diffusion coefficient (ADC) measurement methods in renal cell carcinoma (RCC), and their correlation with the International Society of Urological Pathology (ISUP) histologic grading system. METHODS: A total of 99 patients who underwent diffusion-weighted imaging and whose pathologic diagnosis of RCC was confirmed were included in the study. As a result of a literature review, region of interest (ROI) selection and measurement methods were determined in five ways. These included a small ROI (ADC1) on the solid part of the lesion showing the most restriction; a large ROI (ADC2) on the solid part of the lesion showing restriction; ROI (ADC3) that covered the lesion in the cross-section with the largest diameter, which was obtained by placing ROIs (ADC4) covering the lesion on all sections of the lesion; three small ROIs (ADC5) on solid parts of the lesion showing the most restriction. Then, ADC measurements were made from the contralateral normal kidney parenchyma. Tumors were pathologically subdivided [71 clear cell RCCs (ccRCC), 17 chromophobe RCCs (chRCC), 11 papillary RCCs (pRCC)], and graded according to the ISUP nuclear grading system (42 high-grade, 57 low-grade). Data were analyzed statistically. RESULTS: In all measurement methods, ADC values of RCCs were statistically significantly lower than normal kidney ADC values. There were no differences between the ADC3 and ADC4 measurements of RCCs (p = 0.999). There was a statistical difference in other measurement methods (p < 0.001). There were differences between ccRCCs and pRCCs and chRCCs in all measurement methods. In all measurement methods, pRCC and chRCC ADC values ​​were lower than ccRCC ADC values. When ISUP nuclear grading and ADC values ​​were compared, there was a statistically inverse correlation between all ADC measurements. The strongest correlation was found in the ADC1 and ADC5 measurements. When the ADC values ​​of ISUP low and high-grade groups were compared, a significant difference was found in the ADC5 measurement method (p = 0.046). CONCLUSION: According to the findings of the study, ADC5 is the measurement method that shows the best correlation with the ISUP histologic grading system. Therefore, we think that ADC5 can be the primary measurement method for determining the ADC value of RCCs.

Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder.

BACKGROUND: Histogram analysis is a texture analysis method that can be used in medical images. Quantitative values of the intensity of images can be obtained with histogram analysis. It aimed to evaluate corpus callosum in magnetic resonance images (MRIs) using histogram analysis of pediatric patients with autism spectrum disorder (ASD) to compare them with healthy controls. METHODS: This study included 29 children with ASD and 29 healthy children with normal brain MRI. High-resolution three-dimensional turbo field echo images were obtained with a 1.5 T scanner device for brain magnetic resonance imaging. On the corpus callosum in the sagittal T1-weighted images obtained, mean gray level density (mean), the standard deviation, median, minimum, maximum, entropy, variance, skewness, kurtosis, uniformity, size % L, size % M, size % U, and percentile parameters were measured. RESULTS: In ASD patients, mean, standard deviation, maximum, median, variance, entropy, 25%, 75%, 90%, 97%, and 99% values were found to be lower than the control group, and size % U value was higher. In addition, the corpus callosum area was significantly lower in the ASD compared to the controls. CONCLUSION: According to our study, corpus callosum of patients with ASD showed differences compared to healthy controls by histogram analysis, even though they were seen as normal in brain MRI. We think that histogram analysis can be used to evaluate possibly affected areas of brain in ASD patients.

Is it useful to use computerized tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma?

OBJECTIVE: Cholesteatoma is an aggressive form of chronic otitis media (COM). For this reason, it is important to distinguish between COM with and without cholesteatoma. In this study, the role of artificial intelligence modelling in differentiating COM with and without cholesteatoma on computed tomography images was evaluated. METHODS: The files of 200 patients who underwent mastoidectomy and/or tympanoplasty for COM in our clinic between January 2016 and January 2021 were retrospectively reviewed. According to the presence of cholesteatoma, the patients were divided into two groups as chronic otitis with cholesteatoma (n = 100) and chronic otitis without cholesteatoma (n = 100). The control group (n = 100) consisted of patients who did not have any previous ear disease and did not have any active complaints about the ear. Temporal bone computed tomography (CT) images of all patients were analyzed. The distinction between cholesteatoma and COM was evaluated by using 80% of the CT images obtained for the training of artificial intelligence modelling and the remaining 20% for testing purposes. RESULTS: The accuracy rate obtained in the hybrid model we used in our study was 95.4%. The proposed model correctly predicted 2952 out of 3093 CT images, while it predicted 141 incorrectly. It correctly predicted 936 (93.78%) of 998 images in the COM group with cholesteatoma, 835 (92.77%) of 900 images in the COM group without cholesteatoma, and 1181 (98.82%) of 1195 images in the normal group. CONCLUSION: In our study, it has been shown that the differentiation of COM with and without cholesteatoma with artificial intelligence modelling can be made with highly accurate diagnosis rates by using CT images. With the deep learning modelling we proposed, the highest correct diagnosis rate in the literature was obtained. According to the results of our study, we think that with the use of artificial intelligence in practice, the diagnosis of cholesteatoma can be made earlier, it will help in the selection of the most appropriate treatment approach, and the complications can be reduced.

Acute demyelinating encephalomyelitis and transverse myelitis in a child with COVID-19.

BACKGROUND: Corona virus disease 2019 (COVID-19) includes a wide range of diseases with varying pathophysiology in children and adults. Although the disease mainly affects the respiratory tract, neurological involvement is also reported in the literature. The most common neurological complaints due to COVID-19 are headache, dizziness and anosmia. Acute necrotizing myelitis, acute demyelinating encephalomyelitis (ADEM), acute axonal neuropathy, acute transverse myelitis, and Guillian-Barre syndrome have been reported as neurological dysfunctions associated with COVID-19. CASE: A ten-year-old male patient presented with complaints of fever, headache and generalized muscle pain. The patient developed inability to walk and significant muscle weakness during the disease course, and he was diagnosed with ADEM and transverse myelitis on magnetic resonance imaging (MRI). As the etiological agent, COVID-19 was detected in both the respiratory panel sample and the cerebrospinal fluid (CSF) sample by the polymerase chain reaction (PCR) technique. Pulse steroid, IVIG, and plasmapheresis treatment were administered. He started to stand with support during follow-up. CONCLUSION: We presented a case of COVID-19 related ADEM and transverse myelitis who responded to pulse steroid, IVIG, and plasmapheresis.

Comparative Evaluation of Apparent Diffusion Coefficient Values of White Matter Surrounding the Heterotopia in Children With Unilateral Subependymal Heterotopia.

INTRODUCTION: To compare the apparent diffusion coefficient (ADC) values of the white matter around heterotopia in children with unilateral subependymal heterotopia with those of the symmetrical normal cerebral hemisphere and control group. METHODS: Between January 2011 and September 2021, 15 pediatric patients with unilateral focal subependymal heterotopia among 47 patients with heterotopia detected in brain magnetic resonance imaging (MRI) in our hospital were included in the study. The control group consisted of 15 age- and sex-matched children with normal neurological examination and normal brain MRI. In brain MRIs, ADC value was measured from the white matter around the heterotopia area and from the opposite cerebral hemisphere matched to the location, and from the bilateral location-matched white matter of the control group. The area of heterotopia was measured on axial T1-weighted MRI. The data were evaluated statistically. RESULTS: There were eight girls and seven boys in the heterotopia group. The median age was 5.00 (min: 3, max: 14). There was no statistically significant difference between the ADC values of the heterotopia side and contralateral white matter of the heterotopia group. In addition, no statistically significant difference was found between the heterotopia side and opposite sides of the heterotopia and control groups ADC values. CONCLUSION: According to the findings of this study, no difference was found in the ADC values of the white matter around the lesion in children with subependymal heterotopia compared to the opposite cerebral hemisphere and control groups.

Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. METHODS: Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. RESULTS: In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. CONCLUSIONS: It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images.

Diffusion-Weighted MRI Findings of Caudate Nucleus and Putamen in Patients With Obsessive-Compulsive Disorder.

Objective The purpose of this study was to establish the diffusion-weighted magnetic resonance imaging (DW-MRI) findings of the caudate nucleus and putamen in patients with obsessive-compulsive disorder (OCD) and to obtain new information on the etiopathogenesis of OCD, which is still unclear. Methods The study comprised 20 patients with OCDs and 20 healthy volunteers. In these cases, DW-MRI and diffusion-weighted echo-planar images (DW-EPI) at b600 and b1000 gradient values were taken and the measurements were made using the apparent diffusion coefficient (ADC) maps of each group at b600 and b1000 values from the caudate nucleus and putamen. Results When the DW-MRI examination in patients with OCD was compared with the control group, the mean ADC values in the caudate nucleus and putamen were not found to have statistically significantly changed. In addition, there were no significant differences regarding the right and left caudate nuclei and putamen ADC values at the b600 and b1000 in the patients with OCD or the control group. Conclusion There are still many unknowns about the neurobiology of OCD. When the DW-MRI examination of the patients with OCD was compared with the control group in our study, no significant difference was found between the ADC values of the caudate nucleus and putamen. Further studies are required for this present study on DW-MRI in patients with OCD to be meaningful.

Pancreatic Damage and Radiological Changes in Patients With COVID-19.

Background and objective The novel coronavirus disease 2019 (COVID-19) primarily affects the lungs. However, others organs are also affected in varying degrees. We aimed to investigate the changes in pancreatic density on CT and its correlation with amylase/lipase values in patients diagnosed with COVID-19. Materials and methods Radiological changes using non-contrast CT and amylase/lipase values were evaluated retrospectively in patients admitted to the pandemic clinic. The patients were classified into two groups: [polymerase chain reaction (PCR)-positive and PCR-negative]. The correlation and difference between the data were evaluated statistically. Results There was no significant difference with respect to age and gender between the two groups (PCR-positive and PCR-negative). There was a significant difference in the head, neck, trunk, and tail of the pancreas and mean density values, but no statistically significant difference in amylase and lipase values between the two groups. No significant correlation was found using Spearman's correlation test. Conclusion Based on our findings, pancreatic involvement and severe necrotizing pancreatitis can be seen in COVID-19 patients. Pancreatic involvement is more common in patients with severe disease. Patients with gastrointestinal complaints should be evaluated for pancreatitis and their amylase/lipase values should be assessed. We believe that decreased pancreatic density on CT scans can be an early sign of pancreatitis.

Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR.

Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.

A Neglected Issue in Ulcerative Colitis: Mesenteric Lymph Nodes.

Data evaluating the presence and characteristics of mesenteric lymph nodes (LNs) in patients with ulcerative colitis (UC) are scarce. The aim of this study is to determine the presence and characteristics of LNs in UC. The LN characteristics in computed tomography (CT), including LN dimension and attenuation, were evaluated retrospectively in 100 patients with UC (61 active and 39 inactive cases). Clinical characteristics and laboratory parameters, including CBC, biochemical analysis, erythrocyte sedimentation rate (ESR), and C reactive protein (CRP) were also compared. Mesenteric LNs were evident in all patients with UC. The attenuation and dimension of mesenteric LNs did not differ between active and inactive patients with UC. No correlation was found among patients with UC in terms of LN dimension, attenuation, ESR, CRP, leucocyte, and albumin (all with p > 0.05). The current study suggested that inflammation results in the development of mesenteric LN in UC, similar to Crohn’s disease and other inflammatory disorders.

An Overlooked Potentially Treatable Disorder: Idiopathic Mesenteric Panniculitis.

OBJECTIVE: The aim of this study was to determine the prevalence of mesenteric panniculitis (MP) and to describe its clinical characteristics, therapy, and outcome. SUBJECTS AND METHODS: This retrospective study was carried out among patients with MP based on computed tomography (CT) scans from January 2012 to December 2015. The CT images were reanalyzed by study radiologists to confirm the previous MP diagnosis. Patients were divided into 2 groups, i.e., idiopathic and secondary, based on the presence or absence of associated predisposing factors such as trauma, malignancy, autoimmune disorders, ischemia, or previous abdominal surgery. The clinical characteristics of the 2 groups, as well as treatments, were assessed. RESULTS: Among the 19,869 CT scans, 36 patients (0.18%) with MP were identified (i.e., 19 [53%] females and 17 [47%] males). The median age was 54 years (range 26 - 76). Twenty-four patients (67%) were categorized into the idiopathic group. Malignancy was the predisposing factor in 8 (22%) of those patients. Furthermore, abdominal pain was the cardinal symptom observed in 22 patients (92%) in the idiopathic group. In the idiopathic group, 15 patients (63%) were treated with antibiotics and 16 (67%) were treated with nonsteroidal anti-inflammatory drugs (NSAID). One unresponsive patient was treated with colchicine. Symptomatic relief was achieved in all of the treated patients. CONCLUSION: In this study, a symptomatic idiopathic subgroup of patients with MP did not have any associated disorder. The response to treatment with antibiotics and NSAID was effective in most of the patients. Based on these findings, anti-inflammatory treatments beyond NSAID and surgery should be reserved for patients who are unresponsive to antibiotics and NSAID.