Search

Background: Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM). Methods: Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE. Results: The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01). Conclusion: When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.

Cardiac masses diagnosis and treatment are a true challenge, although they are infrequently encountered in clinical practice. They encompass a broad set of lesions that include neoplastic (primary and secondary), non-neoplastic masses and pseudomasses. The clinical presentation of cardiac tumors is highly variable and depends on several factors such as size, location, relation with other structures and mobility. The presumptive diagnosis is made based on a preliminary non-invasive diagnostic work-up due to technical difficulties and risks associated with biopsy, which is still the diagnostic gold standard. The findings should always be interpreted in the clinical context to avoid misdiagnosis, particularly in specific conditions (e.g., infective endocarditis or thrombi). The modern multi-modality imaging techniques has a key role not only for the initial assessment and differential diagnosis but also for management and surveillance of the cardiac masses. Cardiovascular magnetic resonance (CMR) allows an optimal non-invasive localization of the lesion, providing multiplanar information on its relation to surrounding structures. Moreover, with the additional feature of tissue characterization, CMR can be highly effective to distinguish pseudomasses from masses, as well as benign from malignant lesions, with further differential diagnosis of the latter. Although histopathological assessment is important to make a definitive diagnosis, CMR plays a key role in the diagnosis of suspected cardiac masses with a great impact on patient management. This literature review aims to provide a comprehensive overview of cardiac masses, from clinical and imaging protocol to pathological findings.

In this study, we report our experience regarding the occurrence of false radionuclide findings in adrenal iodine-131 MIBG scintigraphy. We present a total of five patients in which nuclear images were false negative or positive in three and two cases, respectively, according to the standard radionuclide established criteria. In particular, the three cases of false-negative MIBG images consisted of two patients with necrotic or cystic pheochromocytomas (Cases 1 and 3) and a patient with a small pheochromocytoma (Case 2); the two cases of false-positive MIBG imaging consisted of a patient with an adenoma showing intense tracer uptake and of a large primary necrotic carcinoma with heterogeneous tracer concentration.

The diagnosis of myocardial infarction with non-obstructive coronary arteries (MINOCA) necessitates documentation of an acute myocardial infarction (AMI), non-obstructive coronary arteries, using invasive coronary angiography or coronary computed tomography angiography and no clinically overt cause for AMI. Historically patients with MINOCA represent a clinical dilemma with subsequent uncertain clinical management. Differential diagnosis is crucial to choose the best therapeutic option for ischemic and non-ischemic MINOCA patients. Cardiovascular magnetic resonance (CMR) is able to analyze cardiac structure and function simultaneously and provides tissue characterization. Moreover, CMR could identify the cause of MINOCA in nearly two-third of patients providing valuable information for clinical decision making. Finally, it allows stratification of patients with worse outcomes which resulted in therapeutic changes in almost half of the patients. In this review we discuss the features of CMR in MINOCA; from exam protocols to imaging findings.

Background and aims Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Methods Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0–2 vs CAD-RADS 3–5). Time of analysis for both physicians and CNN were recorded. Results Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) Conclusions Deep CNN yielded accurate automated classification of patients with CAD-RADS.

PURPOSE The aim of this study was to assess the reliability of a 2D dark-blood phase-sensitive late gadolinium enhancement sequence (2D-DBPSLGE) compared with 2D phase-sensitive inversion recovery late gadolinium enhancement sequence (2D-BBPSLGE) in patients with ischemic cardiomyopathy (ICM). MATERIALS AND METHODS A total of 73 patients with a clinical history of ICM were prospectively enrolled. The following endpoints were evaluated: (a) comparison of image quality between 2D-BBPSLGE and 2D-DBPSLGE for differentiation between blood pool-late gadolinium enhancement (LGE), remote myocardium-LGE, and blood pool-remote myocardium; (b) diagnostic accuracy of 2D-DBPSLGE compared with gold standard 2D-BBPSLGE for the evaluation of infarcted segments; (c) diagnostic accuracy of 2D-DBPSLGE for the evaluation of microvascular obstruction (MVO); (d) comparison of transmurality index between 2D-BBPSLGE and 2D-DBPSLGE; (e) comparison of papillary muscle hyperenhancement between 2D-BBPSLGE and 2D-DBPSLGE; inter-reader agreement for depiction of hyperenhanced segments in both LGE sequences. Data were analyzed using paired t test, Wilcoxon test, and McNemar test, and η coefficient and intercorrelation coefficient (ICC). RESULTS Image quality was superior for 2D-DBPSLGE for differentiation of blood pool-LGE (P

Aims We evaluated the relationship between epicardial adipose tissue (EAT) and coronary vascular function assessed by rubidium-82 (82Rb) positron emission tomography/computed tomography (PET/CT) in patients with suspected coronary artery disease (CAD). Methods and results The study population included 270 patients with suspected CAD and normal myocardial perfusion at stress–rest 82Rb PET/CT. Coronary artery calcium (CAC) score and EAT volume were measured. Absolute myocardial blood flow (MBF) was computed in mL/min/ from the dynamic rest and stress imaging. Myocardial perfusion reserve (MPR) was defined as the ratio of hyperaemic to baseline MBF and it was considered reduced when 0 (n = 156). Conclusion In patients with suspected CAD and normal myocardial perfusion, EAT volume predicts hyperaemic MBF and reduced MPR, confirming that visceral pericardium fat may influence coronary vascular function. Thus, EAT evaluation has a potential role in the early identification of coronary vascular dysfunction.

Paratesticular leiomyoma is a rare benign neoplasm that may arise from smooth muscle cells contained in either the epididymis, the spermatic cord, or the tunica albuginea. Usually patients present a palpable, asymptomatic mass, with a higher prevalence among the fourth and fifth decade of life. In this case report we describe a 57-year-old man with bilateral scrotal palpable masses evaluated with ultrasound and magnetic resonance imaging that were suggestive for leiomyoma. The lesions were surgically removed and pathology revealed no signs of malignancy confirming the diagnostic hypothesis of leiomyoma. Ultrasound is considered the imaging modality of choice for the initial evaluation of testicular masses since it allows an accurate localization (ie testicular vs paratesticular) and can identify signs of malignancy. Magnetic resonance imaging is less frequently performed but can considerably improve lesion characterization. Keywords: Paratesticular, Leiomyoma, US, MRI, Diagnostics

Objectives To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. Methods Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE. Results In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02). Conclusions A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task. Key Points • Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists. • A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets. • The performance of the algorithm was not significantly different from that of an experienced radiologist.

Background Delivering consistent levels of training in cardiac imaging to radiologist is of pivotal importance because of the increasing clinical indications to coronary computed tomography angiography (CCTA) and cardiac magnetic resonance (CMR). Our study sought to capture the heterogeneity of cardiac imaging training programs and to explore residents’ vision on cardiac imaging both in the present and in the future. Methods Two web-based surveys were created. The first was administered to all chief residents from the 42 University Hospitals within Italy, aiming to explore the local educational offer in cardiac imaging. The second was administered via social media to all Italian residents, including questions about their overall vision regarding cardiac imaging. Results 42/42 University Hospitals responded to the first survey and 235 residents to the second. There was at least a 64-slice CT scanner and a 1.5 T MR scanner per center. In the majority of sites, the weekly routine consisted of more than 10 CCTA and more than 5 CMR. Approximately, half of the centers used advanced CCTA and CMR techniques. The majority of the interviewed resident (94%) perceived cardiac imaging training to be moderately to very important, while requirement for external educational resources was advocated in 25% of the cases. Conclusion Our survey highlighted a significant awareness of radiology residents regarding the importance of cardiac imaging in their training curriculum. All centers met the technical requirements for cardiac imaging, limiting its use to basic applications in around half of cases. Implementation of an educational network might be the key for supporting the growth of this subspecialty field.

Purpose We aimed to assess the role of radiologists, cardiologists, and other medical and non-medical figures in cardiac magnetic resonance imaging (MRI) research in the last 34 years, focusing on first and last authorship, number of published studies, and journal impact factors (IF). Methods Articles in the field of cardiac MRI were considered in this systematic review and retrospective bibliometric analysis. For included studies, the first and last authors were categorized as cardiologists, radiologists/nuclear medicine physicians, medical doctors (MD) with specialties in both cardiology and radiology/nuclear medicine, and other MD and non-MD. Differences in the number of papers published overall and by year and institution location for the first and last author category were assessed. Mean IF differences between author categories were also investigated. Results A total of 2053 articles were included in the final analysis. For the first authors (n = 2011), 52% were cardiologists, 22% radiologists/nuclear medicine physicians, 16% other MD, 10% other non-MD, and 1% both cardiologists and radiologists/nuclear medicine physicians. Similarly, the last authors (n = 2029) resulted 54% cardiologists, 22% radiologists/nuclear medicine physicians, 15% other MD, 8% other non-MD, and 2% both cardiologists and radiologists/nuclear medicine physicians. No significant differences due to institution location in the first and last authorship proportions were found. Average journal IF was significantly higher for cardiologist first and last authors when compared to that of radiologists/nuclear medicine physicians (both p < 0.0001). Conclusion Over 50% of studies in the field of cardiac MRI published in the last 34 years are conducted by cardiologists.

Background The Starr-Edwards ball valve prosthesis was successfully introduced in 1961–62 and largely used for aortic and mitral valve replacement. Even if Starr-Edwards valves have been widely replaced in clinical practice by other mechanical valves, they define a standard concerning long-term durability. Case presentation We describe the case of a 55-year-old man referred to our Department to perform a cardiac computed tomography (CCT), to better evaluate a severe dilation of ascending aorta discovered at echocardiography. The patient had been surgically treated 46 years earlier to correct a supra-cristal type ventricular septal defect. Both mitral and aortic valves were replaced, respectively due to bacterial mitral endocarditis and a fibrous sub-valvular aortic stenosis. In addition, the right coronary artery (RCA) was found to arise from the left coronary sinus. Conclusion We report the longest lasting durability (46 years) of aortic and mitral Starr-Edwards valves successfully implanted in a patient simultaneously carrying a malignant anomalous origin of RCA.

Purpose The aim of this study was to assess the reliability of single-shot 2-dimensional multislice late gadolinium enhancement (2D-MSLGE) compared with gold standard single-slice 2D inversion recovery segmented gradient echo (2D-SSLGE). Materials and methods Sixty-seven patients prospectively underwent clinically indicated cardiac magnetic resonance (CMR) imaging and were enrolled. The image quality was assessed using a 4-point scale. Segments positive for LGE were classified as ischemic or nonischemic for 2D-MSLGE and 2D-SSLGE. Interobserver and intraobserver variability was assessed for both sequences by 2 readers. The endpoints were as follows: (a) detection of myocardial segments involved by LGE and (b) classification of LGE as ischemic and nonischemic pattern. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy value were calculated for the 2 endpoints. Results 2D-MSLGE and 2D-SSLGE were successfully performed in all patients with comparable image quality (1.56±0.59 vs. 1.54±0.58, P=0.84). For the overall population, 2D-MSLGE correctly identified 1093 of 1139 myocardial segments positive for LGE (96%; 95% confidence interval [CI]: 95%-97%), as compared with 2D-SSLGE. Similarly, 2D-MSLGE correctly identified 1128 of 1139 (99%; 95% CI: 98%-99%) and 1108 of 1139 (97%; 95% CI: 96%-98%) of nonischemic and ischemic LGE patterns.Interobserver and intraobserver variability for quantification of LGE using 2D-MSLGE was 0.98 and 0.99, respectively. The acquisition time was shorter for 2D-MSLGE as compared with 2D-SSLGE (2.0±0.5 vs. 6.0±2.0 min, P: 0.01). Conclusions As compared with 2D-SSLGE, 2D-MSLGE is a reliable tool in both ischemic and nonischemic cardiac disease; it is associated with shorter scan times without the need for prolonged breath holding and may be beneficial for those with dysrhythmia.

BACKGROUND Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. PURPOSE/HYPOTHESIS To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach. STUDY TYPE Retrospective, observational study. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. FIELD STRENGTH/SEQUENCE Unenhanced T1 -weighted in-phase (IP) and out-of-phase (OP) as well as T2 -weighted (T2 -w) MR images acquired at 3T. ASSESSMENT Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T2 -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. STATISTICAL TESTS The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. RESULTS A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T2 -w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. DATA CONCLUSION Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

We aim to validate echocardiographic left ventricular (LV) mass (echoLVM) in sixty-one patients with hypertrophic cardiomyopathy (HCM), using cardiac magnetic resonance measures (cmrLVM) as gold standard. cmrLVM was calculated using LV short-axis images, from base to apex, whereas echoLVM by LV epicardial minus LV endocardial volumes in 4 and 2 chamber views, using Simpson disk summation; trabeculae and papillary muscle were excluded in both cmrLVM and echoLVM. cmrLVM and echoLVM were not different by paired t test (145 ± 66 vs 147 ± 61; p = 0.240), and their correlation was good (r = 0.977; p

Extragonadal germ cell tumors (EGCTs) represent 1% to 5% of all germ cell tumors (GCTs) (1). Morphological type includes mature/immature teratoma, seminoma, yolk sac tumor, embryonal carcinoma, choriocarcinoma and mixed gonadal GCTs. Their anatomic distribution varies widely and includes the mediastinum, sacrococcygeal region, neck, retroperitoneum and other rare anatomic sites. Diagnosis of EGCTs is made on the basis of histological findings and excluding the presence of a testicular/ovarian involvement. Laboratory is pivotal for diagnosis of EGCTs, often demonstrating high levels of human chorionic gonadotropin (β-HCG) and/or α-fetoprotein (α-FP). The prognosis of patients with EGCTs is variable for the possible occurrence of aggressive behavior and distant metastasis (2). We describe computed tomography (CT) and magnetic resonance (MR) imaging findings of EGCTs reporting a case series of three male patients with different disease’s locations that underwent contrast-enhanced CT with or without positron emission tomography (PET) and/or MR examinations. The diagnostic workflow of all patients is also illustrated.

Purpose: To assess the accuracy and reproducibility of 3D-cine k-adaptative-t-autocalibrating reconstruction for cartesian sampling (3D cine kat-ARC) for quantification of biventricular volumes, ejection fraction and LV mass in clinical practice. Method: 74 patients underwent cardiac magnetic resonance for clinical indications. In the whole population 3D cine kat-ARC and 2D cine bSSFP images were acquired on short axis view. Subsequently, the population was divided in three subgroups (dilated, hypetrophic, other phenotypes). Two experienced observers performed analysis of volumes, biventricular function and left ventricular mass in the overall population and subgroups using an off-line workstation. Statistical analysis was performed using Student's t-test, linear regression and Bland-Altman plot, correlation coefficient η2 and the intraclass correlation coefficient (ICC). A cut-off value of p < 0.05 was considered statistically significant. Results: Biventricular volumes, function and left ventricular mass evaluated with 3D cine kat-ARC sequences did not show any significant difference compared to 2D bSSFP sequences in the overall population (p > 0.05). Bland-Altman analysis showed limited bias and narrow limits of the agreement for all measurements in overall population. Subgroup analysis showed a statistically significant difference (p = 0.04) for left ventricular ejection fraction (LVEF) in patients with a dilated phenotype; showing a minimum overestimation tendency for 3D cine kat ARC (2D cine bSSFP LVEF = 46.44 ± 15.83% vs 3D cine kat-ARC LVEF = 48.36 ± 16.50 %). Conclusions: 3D cine kat-ARC 3D sequences allow an accurate evaluation of biventricular volumes and function in a single breath hold.

Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest.To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach.Retrospective, observational study.Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL.Unenhanced TAdrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and TThe feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test.A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and TMachine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions.4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

PURPOSE: Cardiac sympathetic denervation may be detectable in patients with Anderson-Fabry disease (AFD), suggesting its usefulness for early detection of the disease. However, the relationship between sympathetic neuronal damage measured by 123I-metaiodobenzylguanidine (MIBG) imaging with myocardial fibrosis on cardiac magnetic resonance (CMR) is still unclear. METHODS: Cardiac sympathetic innervation was assessed by 123I-MIBG single-photon emission computed tomography (SPECT) in 25 patients with genetically proved AFD. Within one month from MIBG imaging, all patients underwent contrast-enhanced CMR. MIBG defect size and fibrosis size on CMR were measured for the left ventricle (LV) and expressed as %LV. RESULTS: Patients were divided into three groups according to MIBG and CMR findings: (1) matched normal, without MIBG defects and without fibrosis on CMR (n = 10); (2) unmatched, with MIBG defect but without fibrosis (n = 5); and (3) matched abnormal, with MIBG defect and fibrosis (n = 10). The three groups did not differ with respect to age, gender, α-galactosidase, proteinuria, glomerular filtration rate, and troponin I, while New York Heart Association class (p = 0.008), LV hypertrophy (p = 0.05), and enzyme replacement therapy (p = 0.02) were different among groups. Although in patients with matched abnormal findings, there was a significant correlation between MIBG defect size and area of fibrosis at CMR (r2 = 0.98, p < 0.001), MIBG defect size was larger than fibrosis size (26 ± 23 vs. 18 ± 13%LV, p = 0.02). CONCLUSION: Sympathetic neuronal damage is frequent in AFD patients, and it may precede myocardial damage, such as fibrosis. Thus, 123I-MIBG imaging can be considered a challenging technique for early detection of cardiac involvement in AFD.

Summary Background Evaluation of a patient with melanoma in whom an adrenal mass was detected on CT and MR during follow-up and further characterized with PET-CT and MIBG scintigraphy. Case Report In this case report, we describe a patient with melanoma in whom an adrenal mass was detected on CT and MRI during post-surgical follow-up and was further characterized with radionuclide studies consisting of PET-CT and MIBG scintigraphy. Although the results of imaging studies suggested that the mass was a pheochromocytoma, a cortical adrenal adenoma was histologically proven. Conclusions Integrated structural and functional imaging is recommended to characterize adrenal tumors; however, mistakes may occur and therefore careful imaging evaluation is required.