Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID-19.

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.

Immune checkpoint inhibitors: Assessment of the performance and the agreement of iRECIST, irRC, and irRECIST.

INTRODUCTION: Immunotherapy has become more widely accepted and used by medical oncologists. Radiologists face challenges in assessing tumor response and becoming more involved in the management of treatment. We aimed to assess the agreement between immune-related response criteria (irRC), immune-related RECIST (irRECIST), and immune RECIST (iRECIST) to correlate the response measured by them with overall survival (OS), and to determine the confirmation rate of progressive disease (PD). METHODS: A total of 43 patients (28 men, 15 women; average age = 54.6 ± 15.7 years) treated with immunotherapy were included in this study. Pairwise agreements between iRECIST, irRC, and irRECIST were calculated using Cohen's kappa statistics. The correlation of the criteria-based response and OS was evaluated using the Kaplan-Meier method and log-rank test. A confirmation rate with 95% confidence intervals (CI) was calculated in patients with PD. RESULTS: The kappa values between iRECIST and irRC, iRECIST and irRECIST, and irRC and irRECIST were 0.961 (almost perfect; P < 0.001), 0.961 (almost perfect; P < 0.001), and 0.922 (almost perfect; P < 0.001), respectively. The Kaplan-Meier method and log-rank test showed for each criterion a statistically significant correlation with OS (P < 0.05). The confirmation rates of PD for irRC, irRECIST, and iRECIST were 95% (19/20; 95% CI = 76.4-99.1%), 90% (18/20; 95% CI = 69.9-97.2%), and 90.5% (19/21; 95% CI = 71.1-97.4%), respectively. CONCLUSION: There was an almost perfect and statistically significant agreement between iRECIST, irRC, and irRECIST. The measurements performed with them significantly correlated with the OS; their confirmation rates were similar. iRECIST and irRECIST might be favored over irRC because of their relative ease of use.

State-of-the-art Prostate Imaging.

Prostate cancer is one of the most common cancers in men. In addition to methods such as prostate-specific antigen test, digital rectal examination, and transrectal ultrasonography, magnetic resonance imaging has an important role for accurate and reproducible diagnosis. However, guidance in targeted biopsies and recent use in determining localization for treatment increase its importance. Due to technical difficulties, patient tolerance, and differences in interpretation, the prostate imaging reporting and data system recommends preparations for the patient and magnetic resonance imaging techniques. However, techniques continue to be developed to improve the diagnosis rate and image quality. In our article, patient preparation before imaging and techniques were tried to be discussed in detail. In addition, current approaches in biparametric magnetic resonance imaging and radiomics and new techniques such as T1 and T2 mapping will be mentioned.

The Significance of Preoperative Computed Tomography Features in the Prediction of Overall Survival in Gastric Cancer: A Retrospective Analysis.

Objectives: Computed tomography (CT) is a frequently used modality for staging in the preoperative evaluation of gastric cancer (GC). Our aim was to interpret the importance of preoperative CT features in predicting overall survival (OS) in patients operated for GC. Methods: One hundred and one patients with GC (33 women, 68 men; range of age: 29-82 years, median age: 61 years) who had abdominal CT prior to surgical resection were included in the study retrospectively. Two radiologists evaluated CT scans to record the longest dimension of the tumor, the localization of the lesion, the attenuation values of the tumor in the arterial and venous phases (Hounsfield units), invasion depth of the lesion (T stage), and the number of pathological lymph nodes (LNs) (N stage). Postoperative pathological results including resection (R0, R1), T stage, N stage, grade, and histopathological subtype were documented. All CT-provided results and clinicopathological features associated with OS were analyzed by univariate, multivariate, and receiver operator characteristic analysis. Results: Multivariate analysis revealed that none of the CT features were associated with the OS. After resection, the survival ratio was poor for the R1 and high-grade groups than for the R0 and low-grade groups (p=0.001 and p=0.005, respectively). N stage and the longest dimension of the tumor on CT imaging truly estimated R1 resection status (AUC, 0.697; sensitivity, 63%; and specificity, 88%, and AUC, 0.734; sensitivity, 18%; and specificity, 76%, respectively). Conclusion: R1 resection status is associated with poor OS in GC. CT features, including the tumor's longest dimension and the number of pathological LNs, can predict R1 resection status.

Radiological management and challenges of the twin earthquakes of February 6th.

Two major earthquakes measuring 7.8 and 7.7 on the Richter scale struck Turkey and Northern Syria on February 6, claiming more than 50,000 lives. In such an unprecedented disaster, radiologists were confronted with very critical tasks of stepping out of the routine reporting process, performing radiological triage, managing acute adverse events, and optimizing imaging protocols. In our experience, radiologists can take three different positions in such disasters: (1) in the scene of the disaster, (2) serving in teleradiology, and (3) working in tertiary hospital for transported patients. With this article, we aimed to describe the challenges radiologists face on the three main fronts and how we manage these challenges.

Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models.

PURPOSE: This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODS: Pre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTS: Forty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSION: Machine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study.

Background: Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic. Aims: We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus. Methods: Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables. Results: RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (ß = 0.097, p < 0.05) and pleural effusion (ß = 10.631, p < 0.05) on COVID-19 pneumonia patients. Conclusion: The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

The effect of sarcopenia and serum myokines on prognosis and survival in cirrhotic patients: a multicenter cross-sectional study.

OBJECTIVE: Sarcopenia is one of the most significant contributors to morbidity in patients with chronic liver disease. Serum myokines are potential biomarkers for detecting early sarcopenia. We aimed to investigate the relationship between serum myokines and cirrhosis-related mortality in the early stages of the disease. METHODS: In total, 262 patients and 50 healthy controls were enrolled in this study, which was designed as a multicenter cross-sectional study. At the beginning of the study, sarcopenia was defined by computed tomography scans using the third lumbar vertebra skeletal muscle index. Serum myostatin, irisin, and follistatin levels, nutritional status of the patients, and muscle strength as measured by the handgrip test were recorded. Cirrhosis-related mortality and overall survival were evaluated in the fourth year of the study as the second checkpoint of cross-sectional analysis. RESULTS: A total of 145 (55.3%) patients were diagnosed with sarcopenia. Multivariate analysis revealed that low BMI, high levels of myostatin, and decreased irisin levels were independent predictors of sarcopenia. While serum irisin level was the most predictive parameter in terms of 4th-year cirrhosis-related mortality in the CHILD A group, serum myostatin levels were found more indicative in the CHILD BC group regardless of sarcopenia status ( P < 0.001). CONCLUSION: Serum myostatin levels predict sarcopenia in all stages of cirrhosis. Serum irisin levels can also be used as a potential biomarker to predict both treatable sarcopenia and cirrhosis-related mortality in CHILD A patients.

A Rare Presentation of Extramedullary Hematopoiesis: Perirenal Masses.

Extramedullary hematopoiesis (EMH) is a physiological compensatory mechanism that develops in response to ineffective or insufficient hematopoiesis. Although the liver and spleen are the most common sites, EMH may occur anywhere in the body. Peri-renal involvement in EMH is quite rare and mimics tumoral lesions. To our knowledge, 12 peri-renal EMH cases have been reported in the literature so far.  A 57-year woman was admitted to our hospital with non-specific abdominal pain. She had a history of chronic renal failure. On physical examination, diffuse abdominal tenderness and mild rigidity were detected. Blood tests revealed bicytopenia and elevated acute phase reactants. On non-contrast abdominal computed tomography (CT) scan, two peri-renal mass lesions of 4.5 cm and 4 cm were detected incidentally on the right side. Tumoral lesions were considered in the differential diagnosis, and magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT) were performed. Lesions showed mild hypermetabolic activity (SUVmax: 4,68) on PET-CT images. For the definitive diagnosis, an ultrasound-guided core needle biopsy was performed. The histopathological examination was consistent with EMH. This case aims to highlight the peri-renal involvement of EMH, which mimics renal and peri-renal malignancies.

The Efficiency of Fractional Anisotropy, Apparent Diffusion Coefficient, and Contrast Enhancement Index in Liver Fibrosis Staging.

Objectives: Even though magnetic resonance imaging has been described as the most effective imaging method for the diagnosis of liver fibrosis, an accepted magnetic resonance imaging (MRI) technique is yet to be defined. The aim of this study is to determine the efficiency of MRI in the staging of liver fibrosis. Methods: Patients with chronic hepatitis B infection and had upper abdominal MRI with hepatocyte specific contrast agent were evaluated. Twenty-nine patients that had undergone liver biopsy were included in the study. ADC, FA, and signal intensity values of liver parenchyma were measured by two observers and contrast enhancement index (CEI) was calculated as well. Patients were grouped as early (A) and late fibrosis(B) according to Ishak grading system and then the correlations between the stage and MRI findings were analysed. The intraclass correlation coefficient was used to analyze the inter-rater agreements. ADC, FA, and CEI were compared with Student t-test between early and late fibrosis groups. Pearson's correlation was used to assess the correlation between ADC and FA values. Spearman correlation was used to evaluate the relationship between pathologic fibrosis grade and MRI parameters that were measured. Results: Twenty-two patients were staged as 1 and 2 (group A), seven patients were staged as 3 and above fibrosis(group B). Statistically, there was a strong, negative correlation between the FA values and the degree of fibrosis (r=-0.582, p=0.001). There was no correlation between the CEI and hepatocyte activity index (r=-0.88, p=0.655) and degree of fibrosis (r=0.0001, p=0.997). In terms of FA values, there was a statistically significant difference between two groups (group A=0.429 ± 0.06, group B=0.349 ± 0.06) (p=0.004). Conclusion: Correlation of FA values with fibrosis stage and significant difference in FA values between early-late stage fibrosis patients shows that diffusion tensor imaging can be a promising technique in the staging and follow-up of liver fibrosis.

Classification of retinoblastoma-1 gene mutation with machine learning-based models in bladder cancer.

Purpose: This study aims to evaluate the potential of machine learning algorithms built with radiomics features from computed tomography urography (CTU) images that classify RB1 gene mutation status in bladder cancer. Method: The study enrolled CTU images of 18 patients with and 54 without RB1 mutation from a public database. Image and data preprocessing were performed after data augmentation. Feature selection steps were consisted of filter and wrapper methods. Pearson's correlation analysis was the filter, and a wrapper-based sequential feature selection algorithm was the wrapper. Models with XGBoost, Random Forest (RF), and k-Nearest Neighbors (kNN) algorithms were developed. Performance metrics of the models were calculated. Models' performances were compared by using Friedman's test. Results: 8 features were selected from 851 total extracted features. Accuracy, sensitivity, specificity, precision, recall, F1 measure and AUC were 84%, 80%, 88%, 86%, 80%, 0.83 and 0.84, for XGBoost; 72%, 80%, 65%, 67%, 80%, 0.73 and 0.72 for RF; 66%, 53%, 76%, 67%, 53%, 0.60 and 0.65 for kNN, respectively. XGBoost model had outperformed kNN model in Friedman's test (p = 0.006). Conclusions: Machine learning algorithms with radiomics features from CTU images show promising results in classifying bladder cancer by RB1 mutation status non-invasively.

Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection.

BACKGROUND: The management of a solitary pulmonary nodule is a challenging issue in pulmonary disease. Although many factors have been defined as predictors for malignancy in solitary pulmonary nodules, the accurate diagnosis can only be established with the permanent histological diagnosis. OBJECTIVE: We tried to clarify the possible predictors of malignancy in solitary pulmonary nodules in patients who had definitive histological diagnosis. METHODS: We made a retrospective study to collect the data of patients with solitary pulmonary nodules who had histological diagnosis either before or after surgery. We made a statistical analysis of both the clinic and radiological features of these nodules with respect to malignancy both in contingency tables and with logistic regression analysis. RESULTS: We had a total of 223 patients with a radiological diagnosis of solitary pulmonary nodule. Age, smoking status and pack years of smoking, maximum standardized uptake value (SUVmax), and radiological features such as solid component, spiculation, pleural tag, lobulation, calcification, and higher density were significant predictors of malignancy in contingency tables. Age, smoking status and smoking (pack/year), SUVmax, and radiological features including spiculation, pleural tag, lobulation, calcification, and higher density were the significant predictors in univariate analysis. However, multivariate analysis revealed only SUVmax greater than 2.5 (p < 0.0001), spiculation (p = 0.009), and age older than 61 years (p = 0.015) as the significant predictors for malignancy. CONCLUSION: Age, SUVmax, and spiculation are the independent predictors of malignancy in patients with solitary pulmonary nodules.

Can peritoneal thickening and enhancement be used to determine perforation in patients with acute appendicitis?

BACKGROUND: Acute appendicitis (AA) is a common cause of abdominal pain in developed countries. In patients with suspected AA, computed tomography (CT) is considered as the gold standard with the highest sensitivity and specificity, and it is also an im-portant modality, especially in patients with complicated AA. In this study, we aimed to evaluate age and laboratory findings, as well as specific CT findings in differentiating between perforated and non-perforated appendicitis. METHODS: We retrospectively analyzed 252 patients diagnosed with AA and underwent appendectomy between November 2015 and December 2019 in Somalia Mogadishu Recep Tayyip Erdogan Education and Research Hospital. Patients under 18 years of age and those with no pre-operative CT scans were excluded from the study. The demographic, laboratory, CT findings, and pathological data of all patients were evaluated. RESULTS: This study included 80 patients, 32 (40%) classified as perforated appendicitis (Group-1) and 48 (60%) as non-perforated appendicitis (Group-2). The C-reactive protein value was found to be statistically higher in Group-1 than in Group-2 (177.5±118.9 and 100.2±87.3 mg / L, respectively; p=0.001). The appendix lumen diameter (p=0.002), appendix wall defect (p<0.001), peritoneal thickening and enhancement (p<0.001), ascites (p=0.031), intra-abdominal abscess (p=0.003), jejunal thickening (p=0.019), ileal thick-ening (p=0.008), and ileus (p=0.035) values were significantly higher in Group-1. In the binominal logistic regression analysis performed with statistically significant data, an appendiceal wall defect (OR: 0.069, 95% CI=0.014-0.327, p=0.001) and peritoneal thickening and enhancement (OR: 0.131, 95% CI=0.024-0.714, p=0.019) were identified as independent variables for perforated appendicitis. CONCLUSION: Among CT findings, appendix wall defects and peritoneal thickening and enhancement play an important role in detecting perforation.

Primary Hepatic Angiosarcoma: A Rare and Very Aggressive Liver Tumour.

Primary hepatic angiosarcoma (PHA) is a sporadic and aggressive tumour of the liver that originates from mesenchymal cells and represents less than 2% of all primary liver tumours. It is known to be associated with several environmental and industrial carcinogens; however, in 75% of cases, aetiology remains unclear. Patients generally present with nonspecific symptoms and laboratory findings. Imaging has a limited role in the diagnosis.  We herein present a case of a 52-year-old man with a history of hepatitis B-related cirrhosis who was referred to our hospital for liver transplantation assessment. Magnetic resonance imaging (MRI) revealed two small nodular lesions of 5 and 6 mm in segment IV of the liver, categorised as Liver Imaging Reporting and Data System (LI-RADS) category 3. The patient was discussed at a multidisciplinary tumour meeting, and an MRI follow-up in three months was planned. Three months later, MRI depicted a substantial increase in the lesion size measured 8.5 cm. An ultrasound-guided tru-cut biopsy was performed, and the diagnosis of PHA was confirmed by pathology. In this report, we aim to highlight PHA's MRI features and underline this rare entity's rapid and fatal progression.

Prediction of clinical outcomes of single- and double- bundle anterior cruciate ligament reconstruction techniques using magnetic resonance imaging.

BACKGROUND: The objective of the study is to compare the clinical results of the single-bundle (SB) and double-bundle (DB) anterior cruciate ligament (ACL) repairing techniques using magnetic resonance imaging (MRI). METHODS: Thirty-eight patients were randomized by block randomization into two different groups of ACL reconstruction: DB (n=19) and SB techniques (n=19). MRI evaluation and clinical examination with modified Cincinnati Knee Rating Score and Lysholm knee scores were performed pre-operatively and at the end of a follow-up period of 36 months. RESULTS: No significant differences were found in the ACL angle, posterior cruciate ligament angle, and tibial translation between the DB and the SB groups. Regarding the clinical scores, there were no significant differences between the techniques. As for the correlation of radiologic results with clinical scores in the SB group, there was a strong and significant correlation between the post-operative ACL angle values and the Lysholm clinical score (r=-0.66; p=0.002). CONCLUSION: The post-operative ACL angle can predict the degree of clinical recovery in patients undergoing SB ACL reconstruction.

Correlation of laboratory parameters and Chest CT findings in young adults with COVID-19 and comparison of imaging findings with children.

PURPOSE: We aimed to compare COVID-19 imaging findings of young adults (19-35 years of age) with those of children (0-18 years) and to correlate imaging findings of young adults with their laboratory tests. MATERIALS AND METHODS: This retrospective study included Real Time-Polymerase Chain Reaction (RT-PCR) confirmed 130 young adults (mean age: 28.39 ± 4.77; 65 male, 65 female) and 36 children (mean age: 12.41 ± 4.51; 17 male, 19 female), between March and June 2020. COVID-19 related imaging findings on chest CT were examined in young adults and compared with children by the Mann-Whitney U, and Chi-square or Fisher's exact test. Laboratory examinations of young adults were assessed in terms of correlation with radiological findings by the Spearman's correlation analysis. RESULTS: Bilateral multiple distributions (p = 0.014), subpleural involvement, and pleural thickening (p = 0.004), GGOs with internal consolidations were more frequent in adults (p = 0.009). Infiltrations were significantly larger than 20 mm in young adults (p = 0.011). The rates of feeding vessel sign, vascular enlargement, and halo sign were significantly higher in young adults (p < 0.003). Highly significant positive correlations were found between radiological and biochemical parameters. CONCLUSION: Distribution, size, and pattern of COVID-19 related imaging findings differed in children and young adults. Radiological findings were correlated with biochemical parameters but not with blood count results of young adults.

Comparison of MRI and 18F-FDG PET/CT in the Liver Metastases of Gastrointestinal and Pancreaticobiliary Tumors.

OBJECTIVES: To compare the efficacy of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT) and magnetic resonance imaging (MRI) in the detection of liver metastases originating from the gastrointestinal system (GIS) and the pancreaticobiliary (PB) system. METHODS: This retrospective study included 42 patients with primary GIS (stomach or colorectal) or PB system malignancies that metastasized to the liver, histopathologically confirmed diagnoses, and MRI and 18F-FDG PET/CT images. The MRI and 18F-FDG PET/CT images were analyzed. Student's t-test was used to compare the two modalities in terms of determining the number of metastases, and Cohen's kappa test was conducted to determine the agreement between the modalities. RESULTS: Twenty-eight (66.7%) of the patients included in this study were male, and the mean age was 60.67±9.4 years. Colon (n=25; 59.5%) and pancreatic (n=7; 16.6%) adenocarcinomas were the most common primary tumors that had metastasized to the liver. MRI detected more metastases in 12 (28.5%) patients, less in seven (16.6%), and an equal number of metastases in 23 (54.7%). No statistically significant difference was observed between the number of metastases detected by MRI and 18F-FDG PET/CT (7.55±7.96 and 6.36±7.28, respectively; p=0.11). There was a moderate agreement between the two modalities (kappa value=0.423). Most of the metastases detected on MRI but not seen on 18F-FDG PET/CT (n=10, 23.8%) were lesions smaller than 10 mm. For the eight (19%) patients with lymph node metastases, the number of metastatic lymph nodes detected by MRI and 18F-FDG PET/CT was similar (12 and 14, respectively, p>0.05). CONCLUSION: MRI can detect small lesions at an early stage, and 18F-FDG PET/CT shows the metabolic activity of lesions; therefore, the combined use of the two modalities can potentially offer a beneficial outcome for patients.

Are radiologists ready to evaluate true response to immunotherapy?

BACKGROUND: Standardized response criteria for evaluating patients radiological imaging have an essential role in oncological management. Immunotherapy, using immune checkpoint inhibitors (ICIs), including drugs targeting cytotoxic T-lymphocyte-associated antigen 4 and programmed cell death protein 1 or its ligand, promise a new role that has demonstrated improvement management in cancers resistant to chemotherapy. This article reviews the literature to understand the most useful response evaluation criteria for optimal patient management under immunotherapy treatment. Areas that warrant further research are described. CONCLUSION: In conclusion, ICIs have become more widely accepted and used by medical oncologists. Radiologists face challenges in assessing tumor response and becoming more involved in the management of treatment. The latest published immune-RECIST criteria can be used in response assessment, but further prospective evaluation is needed with registration clinical trials to be definitively validated.

Measurement of iliotibial band diameter in iliotibial band friction syndrome and comparison with an asymptomatic population.

BACKGROUND: Iliotibial band friction syndrome (ITBFS) is an overuse injury of the lateral aspect of the knee. This syndrome classically affects the active young population. PURPOSE: To determine the diameter of the ITB using magnetic resonance imaging (MRI) in patients clinically diagnosed with ITBFS, compare the results with asymptomatic patients, and assess the inter-observer agreement between a senior and a junior radiologist with different levels of experience in musculoskeletal imaging. MATERIAL AND METHODS: From April 2014 to October 2019, 78 knee MRI scans of 78 patients were included in the study group who were referred from the orthopedic clinic with a clinical diagnosis of ITBFS. In the control group, there were 114 knee MRI scans of 114 patients who had knee MRI for various reasons and had no radiological abnormality on the performed knee MRI. The ITB diameters, cut-off values, and interclass correlation coefficient (ICC) were calculated. RESULTS: Mean thickness of the ITB was higher in the study group compared to the control group in measurements done by both the senior and junior radiologists and this was statistically significant (P < 0.001). Cut-off values of the diameters of the ITB were calculated as 2.385 for the senior radiologist and 2.420 for the junior radiologist. ICC of 0.80 was determined, which showed excellent agreement among interpreters. CONCLUSION: ITB thickness in the study group was significantly higher than in the control group. There was also excellent agreement among the two observers. Measurement of ITB thickness on axial plane knee MRI is one of the reliable criteria for ITBFS.