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Post-processing software enjoys a wide sphere of use thanks to advances in technology, and the fact that central venous insufficiency may be associated with tinnitus has been revealed using MRI. Our aim in this study was to use a relatively easier and shorter MRI method (4D-MRAngiography) to reveal whether or not there is an association between tinnitus and jugular venous reflux by adding the valsalva maneuver to technique. Thirty patients with unilateral tinnitus and undergoing contrast enhanced MRI with a special protocol were included in the study. Thick slab dynamic maximum intensity' projection (MIP) images were obtained following short imaging with TWIST-MRA lasting a total of 90-110 sec. Reflux degree was graded (as grade 0, 1, 2) on MIP images obtained during valsalva maneuver. In all cases no dural arteriovenous fistula was defined. Jugular venous reflux was not identified (grade 0) in 20 cases. Grade 1 reflux was determined on the right in five of the remaining cases and on the left in two. Reflux past the base of the skull and reaching the cortical veins and cavernous sinuses was determined during TWIST-MRA in three cases exhibiting a significant increase in tinnitus severity with valsava in their clinical histories. This study reveals a relation between jugular venous reflux and tinnitus using an objective, non-operator dependent modality. In our opinion, determination of this etiology will have a positive impact on the entire diagnosis-treatment- follow-up algorithm in cases of refractory tinnitus resistant to other treatments and induced with valsava. [ABSTRACT FROM AUTHOR]

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care., Competing Interests: Conflict of interest disclosure: Deniz Alis is the CEO and co-founder of Hevi AI Health Tech. The authors declared no conflicts of interest.

Objective: To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning., Methods: We used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning. We used the area under the receiver operating characteristic (AUROC) curve to evaluate patient-level performance in detecting csPCa., Results: The PI-CAI training data had 425 scans with csPCa, while the in-house testing and fine-tuning data had 288 and 50 scans with csPCa, respectively. The nnU-Net model achieved an AUROC of 0.888 and 0.889 on the hidden validation and testing data. The model performed with an AUROC of 0.886 on the in-house testing data, with a slight decrease in performance to 0.870 using transfer learning., Conclusions: The state-of-the-art deep learning method using prostate masks trained on large-scale bi-parametric MRI data provides high performance in detecting csPCa in internal and external testing data with different characteristics, demonstrating the robustness and generalizability of deep learning within and across datasets., Clinical Relevance Statement: A self-adapting deep network, utilizing prostate masks and trained on large-scale bi-parametric MRI data, is effective in accurately detecting clinically significant prostate cancer across diverse datasets, highlighting the potential of deep learning methods for improving prostate cancer detection in clinical practice., (© 2023. The Author(s).)

The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI 96.25-99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO., (© 2023. The Author(s).)

Objective: To investigate whether commercially available deep learning (DL) software improves the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency on bi-parametric MRI among radiologists with various levels of experience; to assess whether the DL software improves the performance of the radiologists in identifying clinically significant prostate cancer (csPCa)., Methods: We retrospectively enrolled consecutive men who underwent bi-parametric prostate MRI at a 3 T scanner due to suspicion of PCa. Four radiologists with 2, 3, 5, and > 20 years of experience evaluated the bi-parametric prostate MRI scans with and without the DL software. Whole-mount pathology or MRI/ultrasound fusion-guided biopsy was the reference. The area under the receiver operating curve (AUROC) was calculated for each radiologist with and without the DL software and compared using De Long's test. In addition, the inter-rater agreement was investigated using kappa statistics., Results: In all, 153 men with a mean age of 63.59 ± 7.56 years (range 53-80) were enrolled in the study. In the study sample, 45 men (29.80%) had clinically significant PCa. During the reading with the DL software, the radiologists changed their initial scores in 1/153 (0.65%), 2/153 (1.3%), 0/153 (0%), and 3/153 (1.9%) of the patients, yielding no significant increase in the AUROC (p > 0.05). Fleiss' kappa scores among the radiologists were 0.39 and 0.40 with and without the DL software (p = 0.56)., Conclusions: The commercially available DL software does not increase the consistency of the bi-parametric PI-RADS scoring or csPCa detection performance of radiologists with varying levels of experience., (© 2023. The Author(s).)

Objectives: To compare the effectiveness of individual multiparametric prostate MRI (mpMRI) sequences-T2W, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE)-in assessing prostate cancer (PCa) index lesion volume using whole-mount pathology as the ground-truth; to assess the impact of an endorectal coil (ERC) on the measurements., Materials and Methods: We retrospectively enrolled 72 PCa patients who underwent 3T mpMRI with (n = 39) or without (n = 33) an ERC. A pathologist drew the index lesion borders on whole-mount pathology using planimetry (whole-mount vol ). A radiologist drew the borders of the index lesion on each mpMRI sequence-T2W vol , DWI vol , ADC vol , and DCE vol . Additionally, we calculated the maximum index lesion volume for each patient (maxMRI vol ). The correlation and differences between mpMRI and whole-mount pathology in measuring the index lesion volume and the impact of an ERC were investigated., Results: The median T2W vol , DWI vol , ADC vol , DCE vol , and maxMRI vol were 0.68 cm 3 , 0.97 cm 3 , 0.98 cm 3 , 0.82 cm 3 , and 1.13 cm 3 . There were good positive correlations between whole-mount vol and mpMRI sequences. However, all mpMRI-derived volumes underestimated the median whole-mount vol volume of 1.97 cm 3 (P ≤ 0.001), with T2W vol having the largest volumetric underestimation while DWI vol and ADC vol having the smallest. The mean relative index lesion volume underestimations of maxMRI vol were 39.16% ± 32.58% and 7.65% ± 51.91% with and without an ERC (P = 0.002)., Conclusion: T2W vol , DWI vol , ADC vol , DCE vol , and maxMRI vol substantially underestimate PCa index lesion volume compared with whole-mount pathology, with T2W vol having the largest volume underestimation. Additionally, using an ERC exacerbates the volume underestimation., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2022 The Author(s).)

To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center's PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18-89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute., (© 2022. The Author(s).)

Purpose: In this study, we assessed the performance of apparent diffusion coefficient (ADC) and diffusion-weighted imaging (DWI) metrics and their ratios across different magnetic resonance imaging (MRI) acquisition settings, with or without an endorectal coil (ERC), for the evaluation of prostate cancer (PCa) aggressiveness using whole-mount specimens as a reference., Methods: We retrospectively reviewed the data of prostate carcinoma patients with a Gleason score (GS) of 3+4 or higher who underwent prostate MRI using a 3T unit at our institution. They were divided into two groups based on the use of ERC for MRI acquisition, and patients who underwent prostate MRI with an ERC constituted the ERC (n = 55) data set, while the remaining patients accounted for the non-ERC data set (n = 41). DWI was performed with b-values of 50, 500, 1000, and 1,400 s/mm2, and ADC maps were automatically calculated. Additionally, computed DWI (cDWI) was performed with a b-value of 2000 s/mm2. Six ADC and two cDWI parameters were evaluated. In the ERC data set, receiver operating characteristic (ROC) curves were plotted for each metric to determine the best cutoff threshold values for differentiating GS 3+4 PCa from that with a higher GS. The performance of these cutoff values was assessed in non-ERC dataset. The diagnostic accuracies and area under the curves (AUCs) of the metrics were compared using Fisher's exact test and De Long's method, respectively., Results: Among all metrics, the ADCmean-ratio yielded the highest AUC, 0.84, for differing GS 3+4 PCa from that with a higher GS. The best threshold cutoff values of ADCmean-ratio (£0.51) for discriminating GS 3+4 PCa from that with a higher GS classified 48 patients out of 55 with an accuracy of 87.27%. However, there was no significant difference between each metric in terms of accuracy and AUC (p = 0.163 and 0.214). Similarly, in the non-ERC data set, the ADCmean-ratio provided the highest diagnostic accuracy (82.92%) by classifying 34 patients out of 41. However, Fisher's exact test yielded no significant difference between DWI and ADC metrics in terms of diagnostic accuracy in non-ERC data (p = 0.561)., Conclusion: The mean ADC ratio of the tumor to the normal prostate showed the highest accuracy and AUC in differentiating GS 3+4 PCa and PCa with a higher GS across different MRI acquisition settings; however, the performance of different ADC and DWI metrics did not differ significantly.

There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.

Background Diastolic dysfunction in hypertrophic cardiomyopathy (HCM) patients is a frequent, yet poorly understood phenomenon. Purpose The purpose of this study is to assess the relationship between the myocardial fibrosis and diastolic dysfunction in patients with HCM. Materials and Methods We retrospectively investigated the impact of the myocardial fibrosis, as assessed by the extent of late gadolinium enhancement (LGE-%) on cardiac magnetic resonance imaging (CMRI), on diastolic dysfunction in 110 patients with HCM. The diastolic dysfunction was evaluated by the left atrial (LA) volume index measured on CMRI and lateral septal E/E' ratio calculated on echocardiography. Results : There was a moderate correlation between the LGE-% and LA volume ( r = 0.59, p < 0.0001). The logistic regression model of LGE-%, mitral regurgitation, and total left ventricular mass that investigated the independent predictors of LA volume identified LGE-% as the only independent parameter associated with the LA volume index ( β = 0.30, p = 0.003). No correlation was observed between the LGE-% and E/E'( r = 0.24, p = 0.009). Conclusions Myocardial fibrosis in HCM patients is associated with a chronic diastolic burden as represented by increased LA volume. However, the fibrosis does not influence the E/E' ratio, which is a well-known parameter of ventricular relaxation, restoring forces, and filling pressure., Competing Interests: Conflicts of Interest There are no conflicts of interest. Financial Support and Sponsorship None. Ethical Statement and Consent to Participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards., (Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: To test the hypothesis that making a diagnosis of left ventricular noncompaction (LVNC) on cardiac magnetic resonance imaging (CMRI) using a noncompacted-to-compacted (NC/C) myocardium ratio > 2.3 would yield significant errors, and also to test a diagnostic flowchart in patients who undergo CMRI and have clinical and echocardiographic findings suggesting LVNC could improve the diagnosis of LVNC., Methods: A total of 84 patients with LVNC and 162 controls consisting of patients with other diseases and healthy participants who had CMRI and echocardiograms were selected. The diagnostic flowchart of the study involved the use of CMRI with all available sequences for patients with a high pre-test probability of LVNC. Two blinded independent cardiologists evaluated echocardiograms, and patients with suggestive echocardiographic and clinical findings for LVNC were enrolled in the high pre-test probability of LVNC group. Two independent blinded radiologists established the diagnosis of LVNC based on NC/C ratio > 2.3 on CMRI, and they were allowed to re-assess the patients following the diagnostic flowchart., Results: An NC/C ratio > 2.3 identified 83 of 84 LVNC patients, yet incorrectly classified 48 of the 162 controls as having LVNC. Radiologists changed their decision in 23 of 48 patients with incorrect diagnoses, resulted in improved specificity (70.4% to 84.6%). The use of the CMRI diagnostic flowchart in the high pre-test probability group yielded a high specificity (97.2%) and accuracy (95.9%)., Conclusions: LVNC diagnosed by CMRI based on the NC/C criterion can lead to overdiagnosis, whereas only using CMRI in patients with a high pre-test probability of LVNC with all available sequences may improve the diagnostic performance.

Objective: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that plays an important role in coronary atherosclerosis. In this study, our aim was to investigate the relationship between long-term major adverse cardiovascular events (MACEs) and EAT volume detected by coronary computed tomography angiography (CCTA) in patients with Type 2 diabetes mellitus (T2-DM) without previous coronary events., Methods: A total of 127 patients with diabetes who underwent CCTA between 2012 and 2014 were enrolled retrospectively. The study population was divided into 2 groups according to whether they experienced or did not experience MACE, which was defined as cardiac death, non-fatal myocardial infarction or unstable angina requiring hospitalization, coronary revascularizations (percutaneous coronary intervention or coronary artery bypass grafting surgery), heart failure, peripheral arterial disease, or ischemic stroke. In both groups, EAT volumes were measured by CCTA., Results: During 60±7 months follow-up period, 22 participants experienced MACEs. Data were evaluated with univariate and multivariate analyses and receiver operating characteristic (ROC) analysis. Age, male sex, coronary artery disease, hemoglobin A1c, glucose, creatinine, C- reactive protein, and cholesterol levels were found to be associated with MACE. EAT volume (odds ratio [OR]: 1.027; 95% confidence interval [CI]: 1.010‒1.044, p=0.002) and low-density lipoprotein (OR: 1.015; 95% CI: 1.000‒1.030, p=0.050) were found to be independent predictors for MACE. ROC analysis indicated that EAT volumes >123.2 mL had a 72.7% sensitivity and a 77.1% specificity for predicting long-term MACE in patients with T2-DM (area under the curve: 0.820; 95% CI: 0.733-0.908)., Conclusion: EAT volume is an independent predictor of long-term MACE in patients with T2-DM without previous coronary events. EAT volume may be used additionally in risk stratification for MACE besides the well-known vascular risk factors in patients with T2-DM.

Objective: The aim of this study was to evaluate the diagnostic accuracy of the Lake Louise consensus criteria using cardiac magnetic resonance (CMR) imaging assessment of edema, hyperemia, and late gadolinium enhancement (LGE) in the diagnostic determination of acute myocarditis., Methods: A total of 44 patients with acute myocarditis and 24 healthy controls were included in this retrospective study. The presence of edema was defined as a myocardial mean signal intensity >1.9 times that of the skeletal muscle in the same slice on T2-weighted short tau inversion-recovery sequences. Hyperemia was defined as an early gadolinium enhancement ratio (EGEr) ≥4 calculated using the contrast-enhancement of the myocardium and skeletal muscle on TSE T1-weighted sequences, and LGE was assessed by visual examination. The reference methods used to determine the presence of myocarditis were based on the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases guidelines for clinical and biochemical findings., Results: The diagnostic accuracy of edema, hyperemia, LGE, and the Lake Louise criteria (at least 2 of 3 components) was 75.7%, 64.2%, 88.5%, and 84.2%, respectively. Among the 3 components of the Lake Louise criteria, edema had the highest specificity (100%), and LGE had the highest sensitivity (86%). The use of LGE and/or edema as a criterion for acute myocarditis revealed the highest diagnostic accuracy (92.8%) among the CMR sequences and combinations of components examined., Conclusion: LGE and/or edema as a sole criterion for the diagnosis of acute myocarditis demonstrated better diagnostic accuracy than the Lake Louise criteria. The use of EGEr did not improve the performance of CMR-based diagnosis. Alternatives to the use of EGEr are recommended for the diagnosis of acute myocarditis.

Aim: The aim of this study is to evaluate the diagnostic value of machine learning- (ML-) based quantitative texture analysis in the differentiation of benign and malignant thyroid nodules., Materials and Methods: A sum of 306 quantitative textural features of 235 thyroid nodules (102 malignant, 43.4%; 133 benign, 56.4%) of a total of 198 patients were investigated using the random forest ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy, sensitivity, specificity, and area under curve (AUC) of the proposed method were compared with the histopathological or cytopathological findings as reference methods., Results: Of the 306 initial texture features, 284 (92.2%) showed good reproducibility (intraclass correlation ≥0.80). The random forest classifier accurately identified 87 out of 102 malignant thyroid nodules and 117 out of 133 benign thyroid nodules, which is a diagnostic sensitivity of 85.2%, specificity of 87.9%, and accuracy of 86.8%. The AUC of the model was 0.92., Conclusions: Quantitative textural analysis of thyroid nodules using ML classification can accurately discriminate benign and malignant thyroid nodules. Our findings should be validated by multicenter prospective studies using completely independent external data., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2019 Bulent Colakoglu et al.)

Purpose: To evaluate patients, diagnosed with non-muscle invasive bladder cancer, according to patient specific parameters including hemoglobin level, estimated glomerular filtration rate (eGFR), body mass index (BMI) and cigarette smoking and to identify if any of these parameters matters in terms of recurrence prediction., Methods: 231 patients who have undergone transurethral resection of the bladder (TURB) between January 2015 and January 2018 and diagnosed with non-muscle invasive bladder cancer (NMIBC) were included. Patient demographic characteristics including age, sex, BMI and cigarette smoking were assessed. Hemoglobin, creatinine and eGFR values were recorded. Follow-up was performed according to the European Association of Urology (EAU) guidelines' recommendations. Recurrence and progression during follow-up were recorded., Results: 231 patients were included in the study. Median patient BMI, Hb levels, and eGFR values were 26.51 kg/m2 (IQR 5.48), 14,2 g/dL (IQR 2.50), and 83.25 ml/min/1.73m2 (IQR 27.83), respectively. Among all patients, 105 (45%) were ex-smokers and 78 (33%) were current smokers, 41 had anemia (17.7%), 37 (16%) patients were obese; 104 (45%) had mildly impaired renal function and 34 (14.7%) had impaired renal function. During follow-up, 67 (29%) patients had disease recurrence and 21 (9.1%) had disease progression (9.1%). Univariate and multivariate analyses revealed significant relationship between recurrence and obesity, impaired renal function and cigarette smoking., Conclusions: Recurrence is a commonly encountered unfortunate consequence of NMIBC, and obesity, renal failure, history of smoking and anemia seem to increase the rate of recurrence among bladder cancer patients.

Aim: Differential diagnosis of parenchymal thyroid diseases by gray-scale ultrasound is quite difficult for a radiologist as the findings are very similar to each other. In this study we aimed to assess some quantitative spectral Doppler parameters, resistivity index (RI), acceleration time (AT), and quantitative elastography [shear wave velocity (SWV)] together to show their reliability for differential diagnosis of parenchymal thyroid diseases., Materials and Methods: We retrospectively reviewed findings of 227 patients (179 females, 48 males) that underwent spectral Doppler ultrasound and acoustic radiation force impulse between October 2013 and March 2016. Ages of the patients were between 18 and 74 years (39.52 ± 12.67). Based on clinical and laboratory findings, patients were divided into five groups (N: Normal, EH: Early Hashimoto, H: Late Hashimoto, M: Nodular Thyroid Disease, HM: Hashimoto + Nodular Thyroid Disease). Detailed statistical analyses were done on parameters such as age, gender, volume information, and RI, AT (ms), SWV (m/s)., Results: No significant effect of gender or volume on the differentiation of disease pattern (Chi-square test: P = 0.306, Kruskal-Wallis test: P = 0.290) was found in this study. RI (0.41 ± 0.06) and SWV values (1.19 ± 0.18 m/s) were the lowest. AT values (>55 ms) were the highest in EH group (area under the curve: 0.913). Existence of H decreased RI and SWV values, while it extended AT in a different thyroid disease., Conclusion: Thyroid parenchymal diseases could be classified and differentiated from each other by measuring RI, AT, and SWV values quantitatively. So, in suspicious cases, these parameters could be a reliable asset for differential diagnosis., Competing Interests: There are no conflicts of interest.

Objectives: We aimed to prospectively assess the diagnostic value of apparent diffusion coefficient (ADC) measurement in the differentiation of benign and malignant mediastinal lymphadenopathies., Materials and Methods: The study included 63 consecutive patients (28 women, 35 men; mean age 59.3 years) with 125 mediastinal lymphadenopathies. Echoplanar diffusion-weighted magnetic resonance imaging of the mediastinum was performed with b-factors of 0 and 600 mm 2 /s before mediastinoscopy and mediastinotomy, and ADC values were measured. The ADC values were compared with the histological results, and statistical analysis was done. P < 0.05 was considered statistically significant., Results: The mean ADC value of malignant mediastinal lymphadenopathy (1.030 ± 0.245 × 10 -3 mm 2 /s) was significantly lower ( P < 0.05) when compared to benign lymphadenopathies (1.571 ± 0.559 × 10 -3 mm 2 /s). For differentiating malignant from benign mediastinal lymphadenopathy, the best result was obtained when an ADC value of 1.334 × 10 -3 mm 2 /s was used as a threshold value; area under the curve 0.848, accuracy 78.4%, sensitivity 66%, specificity of 86%, positive predictive value 76.7%, and negative predictive value of 79.2%. Interobserver agreement was excellent for ADC measurements., Conclusions: ADC measurements could be considered an important supportive method in differentiating benign from malignant mediastinal lymphadenopathies., Competing Interests: There are no conflicts of interest.

Bilateral congenital cystic adrenal neuroblastoma (NB) with cystic liver metastasis is a very rare condition and only few cases have been reported in the literature. Herein we report a case of a congenital bilateral cystic adrenal NB with cystic liver metastasis and briefly discuss characteristic imaging features of cystic NB.

Aim: The aim of this study is to test the diagnostic success of strain elastography in distinguishing benign from malignant thyroid nodules., Materials and Methods: The size, echogenicity, and halo integrity of 293 thyroid nodules and the presence of microcalcification in these nodules were evaluated on gray-scale examination. Doppler characteristics and elastography patterns were also evaluated and recorded. Nodules were classified in four categories (patterns 1-4) based on elastographic examination., Results: According to the cytopathological findings, 222 nodules were benign, and 71 nodules were malignant. The risk of a nodule to be malignant was 3.8 times increased by hypoechogenicity, 7.7 times increased by the presence of microcalcification, and 11.5 times increased by the absence of halo. On Doppler patterns, the presence of central vascularity increased the malignancy risk of a nodule by 5.8 times. According to the receiver operating characteristic analysis, patterns 3 and 4 were malignant, and patterns 1 and 2 were benign. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of elastography were 100%, 80.2%, 61.7%, 100%, and 85%, respectively., Conclusion: Strain elastography can be used as a noninvasive method in distinguishing benign from malignant thyroid nodules and in identifying the patients who would undergo surgery., Competing Interests: There are no conflicts of interest.

Pseudoaneurysm of a cystic artery is a rare entity that commonly occurs secondary to biliary procedures. Most of the cases in literature are consisted of ruptured aneurysms and to our knowledge, except our case, there were only 3 cases with unruptured aneurysms, which incidentally were detected by radiological methods. When cystic artery pseudoaneurysm is present with acute cholecystitis, most of the reports in literature suggested open cholecystectomy with the ligation of the cystic artery as a main treatment option. In this paper we present a case of acute cholecystitis with unruptured cystic artery pseudoaneurysm that incidentally was detected by computed tomography (CT). Cystic artery pseudoaneurysm was handled laparoscopically with simultaneous cholecystectomy. Due to high risk of rupture, surgeons have evaded laparoscopic approach to acute cholecystitis, which accompanied cystic artery pseudoaneurysm. However herein, we proved that laparoscopic management of cystic artery pseudoaneurysm with simultaneous cholecystectomy is feasible and reliable method.

Appendicovesical fistula (AVF) is an uncommon type of enterovesical fistula and a very rare complication of acute appendicitis. Herein, we report a case of 39-year-old male patient who presented with persistent urinary tract infection, recurrent abdominal pain, and pneumaturia. Imaging techniques including ultrasonography (USG), computed tomography (CT), and magnetic resonance imaging (MRI) were performed to identify the abnormality. However, definitive diagnosis of AVF was made by cystoscopy.

Background: Intravascular papillary endothelial hyperplasia (IPEH), also known as masson tumour, is a lesion composed of proliferating endothelial cells., Case Report: In this article we explained clinical, histological and radiological features of IPEH involving the scalp, localized on the left side of the skull and in the periauricular region., Conclusions: Radiologically, intravascular papillary endothelial hyperplasia could be misdiagnosed as malignant or benign vascular tumour. On cross-sectional imaging it is useful demonstrating the extremely vascular component of IPEH. But IPEH has no specific radiologic features that we can use to differentiate from the aforementioned lesions. Due to that, histopathological examinations are needed to diagnose IPEH.