A case of Crimean-Congo haemorrhagic fever complicated with portal vein thrombosis and hemophagocytosis.

OBJECTIVES: Crimean-Congo haemorrhagic fever (CCHF) is a zoonotic viral infection which is an important public health problem in Turkey. CCHF causes fever and bleeding and can lead to severe health outcomes. The study aims to report a case of a male patient with severe CCHF, hemophagocytic lymphohistiocytosis (HLH) treated with steroids and portal vein thrombosis. CASE REPORT: A 37-year-old man was admitted to the emergency department with complaints of high fever, headache, myalgia and diarrhoea. The patient travelled to the endemic region of Turkey. In laboratory findings, thrombocytopenia, abnormal liver function tests and elevated coagulation parameters were observed. Real-time polymerase chain reaction assay was used for diagnosis of CCHF. Hypofibrinogenemia, hypertriglyceridemia, elevated ferritin and d-dimer levels were observed in the clinical follow-up. Prednisolone treatment was performed due to considered the diagnosis of HLH. Portal vein thrombosis was detected on abdominal computed tomography scan. He was successfully treated with ribavirin, corticosteroids, anticoagulant and supportive therapy. CONCLUSION: The clinical presentation of CCHF can range from self-limiting flu-like to severe symptoms possibly fatal. Acute portal vein embolism is a rare complication that has not been reported before to our knowledge. Corticosteroids may be a life-saving treatment for CCHF patients presenting with HLH.

AdaRes: A deep learning-based model for ultrasound image denoising: Results of image quality metrics, radiomics, artificial intelligence, and clinical studies.

PURPOSE: The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising. METHODS: A novel DL-based filter using adaptive residual (AdaRes) learning was proposed. Five image quality metrics (IQMs) and 27 radiomics features were used to evaluate denoising results. The effect of our proposed filter, AdaRes, on four pre-trained convolutional neural network (CNN) classification models and three radiologists was assessed. RESULTS: AdaRes filter was tested on both natural and ultrasound image databases. IQMs results indicate that AdaRes could remove noises in three different noise levels with the highest performances. In addition, a radiomics study proved that AdaRes did not distort tissue textures and it could preserve most radiomics features. AdaRes could also improve the performance classification using CNNs in different settings. Finally, AdaRes also improved the mean overall performance (AUC) of three radiologists from 0.494 to 0.702 in the classification of benign and malignant lesions. CONCLUSIONS: AdaRes filtered out noises on ultrasound images more effectively and can be used as an auxiliary preprocessing step in computer-aided diagnosis systems. Radiologists may use it to remove unwanted noises and improve the ultrasound image quality before the interpretation.

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

Risk of Conversion from Laparoscopic Appendectomy to Open Surgery: The Role of Clinical and Radiological Factors in Prediction.

Introduction: Laparoscopic appendectomy is the most preferred surgical method in the treatment of acute appendicitis. In our study, we aim to determine the clinical and radiological factors affecting conversion from laparoscopic appendectomy to open surgery. Materials and Methods: All patients older than 18 years, who were operated on with the diagnosis of acute appendicitis in the General Surgery clinic of Prof. Dr. Ilhan Varank Training and Research hospital between January 2020 and January 2022, were included in the study. The data consisting of clinical, laboratory, and radiological (computed tomography) findings of the patients were evaluated retrospectively. The patients were divided into two groups as those whose surgery was completed laparoscopically (Group 1) and those converted from laparoscopic appendectomy to open surgery (Group 2). The risk of conversion to open surgery was analyzed by binary logistic regression analysis as univariate and multivariate models. Results: Appendectomy was performed in 831 patients within the specified period. The surgery of 31 (3.73%) patients started laparoscopically; however, they were completed by converting to open surgery. Multivariable analysis showed that the risk of conversion to open surgery increased with leukocyte count, Alvarado score and with the presence of periappendiceal fluid and lymphadenopathy on CT. Conclusion: Our study shows that patients with high risk of returning to open surgery can be identified preoperatively with the risk analysis method in which clinical, laboratory, and radiological findings are evaluated together. We conclude that, starting the operation of these patients with the open technique from the beginning will prevent unnecessary expenditures and reduce morbidities.

Accuracy and reliability of magnetic resonance imaging in the diagnosis of idiopathic intracranial hypertension.

PURPOSE: To determine the diagnostic utility of brain magnetic resonance imaging (MRI) findings in patients with idiopathic intracranial hypertension (IIH) and to investigate the significance of evaluating radiological findings together with neurological and ophthalmological data in the diagnosis of IIH. MATERIALS AND METHODS: All consecutive patients diagnosed with IIH in our tertiary neuro-ophthalmology center between January 1, 2018 and March 15, 2020, were included in the study. The clinical, radiological, and ophthalmological findings of IIH patients were compared with the control group with similar demographic characteristics. RESULTS: A total of 98 patients, 49 cases and 49 controls, were included in the study. Lateral ventricular index had the highest area under the curve (AUC) value (0.945) for prediction of disease group followed by sella height category (AUC = 0.915) and optic nerve tortuosity (AUC = 0.855) According to the multivariate model we developed, caudate index (OR = 0.572, 95% CI 0.329-0.996), lateral ventricle index (OR = 3.969, 95% CI 1.851-8.509) and bilateral optic nerve tortuosity (OR = 22,784, 95% CI 2.432-213.450) were significant predictors for disease group. CONCLUSION: Tortuosity in the optic nerve, lateral ventricular index and caudate index can be used as MRI parameters supporting the diagnosis of IIH in clinically suspicious cases. A holistic approach to the clinical and radiological findings of the cases in the diagnosis of IIH can prevent overdiagnosis and enable early correct diagnosis.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Analysis of intra-observer and inter-observer variability of pathologists for non-benign thyroid fine needle aspiration cytology according to Bethesda system categories.

AIM: To examine the intra- and inter-observer variability for non-benign thyroid cytological subcategories according to the Bethesda classification system after the second review. METHODS: Between November 2018 and May 2019, thyroid fine needle aspiration biopsies of 381 nodules were retrospectively evaluated. Among them, 74 non-benign (category III-VI) thyroid biopsies, analyzed according to the Bethesda system (pathologist 1:40 vs pathologist 2:34) by two independent pathologists, were reassessed by the same pathologists and by a cytopathologist. In this observer-blinded study, weighted Cohen's kappa was used to assess the intra-observer agreement, and Krippendorff's alpha was used to assess the inter-observer agreement. RESULTS: At the first and second evaluations of pathologists 1 and 2, the percentage agreement was 62.5% for pathologist 1 and 58.8% for pathologist 2. The intra-observer agreement was substantial (κ = 0.705) for pathologist 1, and moderate (κ = 0.447) for pathologist 2. In the second evaluation of pathologist 1 and 2, which was compared with the cytopathologist, the agreement percentage of pathologist 1 with the cytopathologist was 50.0%, and that of pathologist 2 was 56.8%. The inter-observer agreement was below the lowest acceptable limit for an overall agreement (α = 0.634) among the three raters. The inter-observer agreement was only acceptable between the cytopathologist and the second pathologist, while it was low between the other raters. In the evaluation of the non-benign nodules, the mean category score of the cytopathologist was 3.22 and lower than both pathologists (3.73 and 3.58, respectively). CONCLUSIONS: The intra-observer agreement of pathologists was moderate-to-substantial in the evaluation of non-benign thyroid biopsies according to the Bethesda reporting system. However, the inter-observer agreement was below the lowest acceptable limit when the cytopathologist was taken as a reference.

Clinical and radiologic characteristics of symptomatic pregnant women with COVID-19 pneumonia

Objective: To describe the radiological features, diagnostic accuracy and features of imaging studies and their relation with clinical course of Coronavirus disease-2019 (COVID-19) pneumonia in pregnant women. Material and Methods: The clinical, laboratory and radiological features of symptomatic pregnant women suspected of COVID-19 were retrospectively reviewed. Chest radiography (CXR) and chest computed tomography (CT) findings of COVID-19 in pregnant women were identified. Results: Fifty-five of eighty-one pregnant women were included in the final analysis. The most common admission symptoms were dry cough (45.4%), fever (29.1%) and dyspnea (34.5%). Radiological imaging studies were performed in 34 (61.8%) patients. Fourteen (66.7%) of the laboratory-confirmed COVID-19 patients had parenchymal abnormalities on CXR, and most common abnormalities were airspace opacities (61.9%) and prominent bronchovascular shadows (28.6%). Seventeen (85.0%) of the patients had parenchymal abnormalities consistent with COVID-19 on their chest CT. Chest CT most commonly showed bilateral (88.2%), multilobe (100%) involvement; peripheral and central distribution (70.6%); patchy-shape (94.1%) and ground-glass opacity (94.1%). The sensitivity of CXR and chest CT was calculated as 66.7% and 83.3%, respectively. Preterm birth rate was 41.2% (n=7/17). Five (9.1%) of the 55 pregnant women admitted to the intensive care unit, three of those developed acute respiratory distress syndrome and one died. Conclusion: This study describes the main radiological features of symptomatic pregnant women infected with COVID-19. The refusal rate among pregnant women for the imaging modalities involving ionizing radiation was high but these had high sensitivity for COVID-19 diagnosis. The preterm birth and cesarean section rates were observed as remarkably increased.

Differences between radiological findings of COVID-19 and non-COVID-19 infections in pediatric patients.

BACKGROUND: This study aimed to reveal the differences between coronavirus disease 2019 (COVID-19) infections and non-COVID-19 respiratory tract infections in pediatric patients. METHODS: Sixty pediatric patients admitted to the hospital between March 11, 2020 and April 15, 2020 with respiratory tract infections were evaluated retrospectively. Among them, 20 patients with reverse transcription-polymerase chain reaction (RT-PCR) tests and chest computed tomography (CT) examinations were included in the study. According to the RT-PCR test results, the patients were divided into the COVID-19 and non-COVID-19 groups. The clinical observations, laboratory results, and radiological features from the two groups were then compared. RESULTS: According to the RT-PCR test results, 12 patients were assigned to the COVID-19 group and 8 to the non-COVID-19 group. There were no significant differences between the two groups in terms of clinical or laboratory features. In terms of radiological features, the presence of bronchiectasis and peribronchial thickening was statistically significantly higher in the non-COVID-19 group (P = 0.010 and P = 0.010, respectively). CONCLUSIONS: In pediatric cases, diagnosing COVID-19 using radiological imaging methods plays an important role in determining the correct treatment approach by eliminating the possibility of other infections.

A comparison of clinical, laboratory and chest CT findings of laboratory-confirmed and clinically diagnosed COVID-19 patients at first admission.

PURPOSE: This study aims to identify chest computed tomography (CT) characteristics of coronavirus disease 2019 (COVID-19), investigate the association between CT findings and laboratory or demographic findings, and compare the accuracy of chest CT with reverse transcription-polymerase chain reaction (RT-PCR). METHODS: Overall, 120 of 159 consecutive cases isolated due to suspected COVID-19 at our hospital between 17 and 25 March 2020 were included in this retrospective study. All patients underwent both chest CT and RT-PCR at first admission. The patients were divided into two groups: laboratory-confirmed COVID-19 and clinically diagnosed COVID-19. Clinical findings, laboratory findings, radiologic features and CT severity index (CT-SI) of the patients were noted. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of chest CT were calculated for the diagnosis of COVID-19, using RT-PCR as reference. RESULTS: The laboratory-confirmed and clinically diagnosed COVID-19 groups consisted of 69 (M/F 43/26, mean age 50.9±14.0 years) and 51 patients (M/F 24/27, mean age 50.9±18.8 years), respectively. Dry cough (62.3% vs. 52.9%), fever (30.4% vs. 25.5%) and dyspnea (23.2% vs. 27.5%) were the most common admission symptoms in the laboratory-confirmed and clinically diagnosed COVID-19 groups, respectively. Bilateral multilobe involvement (83.1% vs. 57.5%), peripheral distribution (96.9% vs. 97.5%), patchy shape (75.4% vs. 70.0%), ground-glass opacities (GGO) (96.9% vs. 100.0%), vascular enlargement (56.9% vs. 50.0%), intralobular reticular density (40.0% vs. 40.0%) and bronchial wall thickening (27.7% vs. 45.0%) were the most common CT findings in the laboratory-confirmed and clinically diagnosed COVID-19 subgroups, respectively. Except for the bilateral involvement and white blood cell (WBC) count, no difference was found between the clinical, laboratory, and parenchymal findings of the two groups. Positive correlation was found between CT-SI and, lactate dehydrogenase (LDH) and C-reactive protein (CRP) values in the laboratory-confirmed COVID-19 subgroup. Chest CT and RT-PCR positivity rates among patients with suspected COVID-19 were 87.5% (105/120) and 57.5% (69/120), respectively. The sensitivity, specificity, PPV, NPV and accuracy rates of chest CT were determined as 94.2% (95% confidence interval [CI], 85.8-98.4), 21.57% (95% CI, 11.3-35.3), 61.90% (95% CI, 58.2-65.5), 73.3% (95% CI, 48.2-89.1) and 63.3% (95% CI, 54.1-71.9), respectively. CONCLUSION: Chest CT has high sensitivity and low specificity in the diagnosis of COVID-19. The clinical, laboratory, and CT findings of laboratory-confirmed and clinically diagnosed COVID-19 patients are similar.

Lung ultrasonography in pregnant women during the COVID-19 pandemic: an interobserver agreement study among obstetricians.

PURPOSE: This study investigated interobserver agreement in lung ultrasonography (LUS) in pregnant women performed by obstetricians with different levels of expertise, with confirmation by an expert radiologist. METHODS: This prospective study was conducted at a tertiary "Coronavirus Pandemic Hospital" in April 2020. Pregnant women suspected to have coronavirus disease 2019 (COVID-19) were included. Two blinded experienced obstetricians performed LUS on pregnant women separately and noted their scores for 14 lung zones. Following a theoretical and hands-on practical course, one experienced obstetrician, two novice obstetric residents, and an experienced radiologist blindly evaluated anonymized and randomized still images and videoclips retrospectively. Weighted Cohen's kappa and Krippendorff's alpha tests were used to assess the interobserver agreement. RESULTS: Fifty-two pregnant women were included, with confirmed COVID-19 diagnosis rate of 82.7%. In total, 336 eligible still images and 115 videoclips were included in the final analysis. The overall weighted Cohen's kappa values ranged from 0.706 to 0.912 for the 14 lung zones. There were only seven instances of major disagreement (>1 point) in the evaluation of 14 lung zones of 52 patients (n=728). The overall agreement between the radiologist and obstetricians for the still images (Krippendorff's α=0.856, 95% confidence interval [CI], 0.797 to 0.915) and videoclips (Krippendorff's α=0.785; 95% CI, 0.709 to 0.861) was good. CONCLUSION: The interobserver agreement between obstetricians with different levels of experience on still images and videoclips of LUS was good. Following a brief theoretical course, obstetricians' performance of LUS in pregnant women and interpretation of pre-acquired LUS images can be considered consistent.

Comparison of Number of Passes and Cytopathological Specimen Adequacy for Thyroid Fine-Needle Aspiration Biopsy in the Absence of an On-Site Pathologist.

AIM: Ultrasound-guided fine-needle aspiration biopsy (FNAB) is a reliable, minimally invasive diagnostic method with high sensitivity and specificity in the evaluation of thyroid nodules. Our aim in this retrospective study was to determine if there was a difference in the adequacy ratio based on the number of needle passes in the thyroid FNABs in the absence of rapid on-site evaluation (ROSE) by the pathologist and to determine the optimal needle pass number for FNAB. METHODS: Between November 2018 and February 2019, thyroid FNABs of 121 (99 female, 22 male) patients were evaluated retrospectively. Samples for each biopsy were numbered according to the order of retrieval, and 4 pairs of slides were prepared by the radiologist without on-site microscopic evaluation. Cytological results were determined according to the Bethesda classification. RESULTS: The rate of adequacy in the first, second, third, and fourth passes were 76.0, 82.6, 77.7, and 71.2%, respectively. No statistically significant difference was found between these four groups in terms of adequacy (p = 0.21). The adequacy rates of the 1st, 1st+2nd, 1st+2nd+3rd, and cumulative evaluation of all four biopsies were 76.0, 87.6, 90.1, and 91.7%, respectively (p = 0.001). A statistically significant difference was found in the comparison of the 1st biopsy and the cumulative 1st+2nd biopsy in terms of adequacy rates (p = 0.019). However, there was no statistically significant difference between the cumulative 1st+2nd biopsy and the cumulative 1st+2nd+3rd biopsy in terms of adequacy rates (p = 0.54). CONCLUSIONS: In cases where ROSE cannot be performed, we recommend a minimum of 2 and a maximum of 3 needle entries for FNAB adequacy with the right technique and preparation.

Spigelian Hernia Including the Urinary Bladder: A Rare Potential Cause of Surgical Complication.

Spigelian hernia is a rare type of anterior abdominal wall hernia. While it is itself very rare, seeing urinary bladder in this hernia is even rarer. Here, in this case, we specifically illustrate a rare case of Spigelian hernia including the urinary bladder, diagnosed with computerized tomography.

Correlation of clot distribution with morphometric measurements and pleuroparenchymal findings in acute pulmonary embolism: experience with 692 cases.

PURPOSE: The aim of the study is to determine if clot distribution in acute pulmonary embolism (PE) correlates with morphometric measurements of right heart function, reflux in inferior vena cava (IVC), and pleuroparenchymal findings. MATERIALS AND METHODS: A total of 692 computed tomography pulmonary angiographies with PE were enrolled, and patients were grouped according to clot localization. Parenchymal findings, morphometric measurements of right heart function, and contrast reflux in IVC were noted. RESULTS: Differences were found between groups for most measurements, and central PE was associated with significantly higher right ventricle (RV) and pulmonary artery diameters, ratio of RV diameter to left ventricle (LV) diameter (RV/LV) ≥ 1, and IVC reflux. CONCLUSIONS: Significant association was present among clot distribution in PE, morphometrics, IVC reflux, and pleuroparenchymal findings.