Adrenal infections update: how radiologists can contribute to patient care

Adrenal infections are considered clinically important but often go unrecognized, with a significant number of cases only diagnosed post-mortem. The limited evidence regarding imaging findings in the literature emphasizes the need to detect and diagnose these infections early in disease course to improve patient outcomes. A range of microorganisms, including fungi, viruses, parasites, and bacteria, can directly or indirectly affect the morphology and function of the adrenal glands. When evaluating a patient with adrenal infection, several immunological and hormonal factors should be considered, such as the status of the hypothalamic-pituitary-adreno cortical axis and the serum cortisol level. Moreover, certain microorganisms specifically target one of the zones of the adrenal glands or vascular supply, resulting in distinct imaging manifestations. The purpose of this article is to describe the fundamental clinical features and imaging manifestations associated with adrenal infections, enabling radiologists to make informed interpretations and contribute to accurate diagnostic assessments.

Exploring the Differential Diagnosis of Adrenal Adenoma in the Context of Situs Ambiguous: A Clinical Case Study

Situs anomalies, including situs inversus and situs ambiguous (SAMB), are rare congenital conditions typically noted in pediatric populations, with SAMB being particularly uncommon in adults. This case study addresses the incidental discovery of situs ambiguous with polysplenia in a 65-year-old man evaluated for suspected adrenal adenoma. The patient's medical history included benign prostatic hyperplasia and tuberculous pleurisy. Methods included a thorough physical examination and laboratory tests, which showed normal cortisol levels and ACTH rhythm. Contrast-enhanced CT imaging revealed multiple spleens near the right adrenal region, altered liver positioning, a truncated pancreas, and a right-sided stomach, while the right adrenal gland was not visualized. Notably, the patient exhibited minimal symptoms despite these significant anatomical anomalies. The findings underscore the rarity of situs ambiguous in adults and its unexpected association with endocrine pathology. This case highlights the importance of comprehensive imaging and a multidisciplinary approach in managing patients with unusual anatomical presentations. It suggests that situs anomalies may be more prevalent in adult populations than previously recognized and emphasizes the need for increased clinical awareness and evaluation in similar cases.

Radiomics in the characterization of lipid-poor adrenal adenomas at unenhanced CT: time to look beyond usual density metrics

OBJECTIVES

In this study, we developed a radiomic signature for the classification of benign lipid-poor adenomas, which may potentially help clinicians limit the number of unnecessary investigations in clinical practice. Indeterminate adrenal lesions of benign and malignant nature may exhibit different values of key radiomics features.

METHODS

Patients who had available histopathology reports and a non-contrast-enhanced CT scan were included in the study. Radiomics feature extraction was done after the adrenal lesions were contoured. The primary feature selection and prediction performance scores were calculated using the least absolute shrinkage and selection operator (LASSO). To eliminate redundancy, the best-performing features were further examined using the Pearson correlation coefficient, and new predictive models were created.

RESULTS

This investigation covered 50 lesions in 48 patients. After LASSO-based radiomics feature selection, the test dataset's 30 iterations of logistic regression models produced an average performance of 0.72. The model with the best performance, made up of 13 radiomics features, had an AUC of 0.99 in the training phase and 1.00 in the test phase. The number of features was lowered to 5 after performing Pearson's correlation to prevent overfitting. The final radiomic signature trained a number of machine learning classifiers, with an average AUC of 0.93.

CONCLUSIONS

Including more radiomics features in the identification of adenomas may improve the accuracy of NECT and reduce the need for additional imaging procedures and clinical workup, according to this and other recent radiomics studies that have clear points of contact with current clinical practice.

CLINICAL RELEVANCE STATEMENT

The study developed a radiomic signature using unenhanced CT scans for classifying lipid-poor adenomas, potentially reducing unnecessary investigations that scored a final accuracy of 93%.

KEY POINTS

• Radiomics has potential for differentiating lipid-poor adenomas and avoiding unnecessary further investigations. • Quadratic mean, strength, maximum 3D diameter, volume density, and area density are promising predictors for adenomas. • Radiomics models reach high performance with average AUC of 0.95 in the training phase and 0.72 in the test phase.

18F-FDG-PET/CT-based machine learning model evaluates indeterminate adrenal nodules in patients with extra-adrenal malignancies

BACKGROUND

To assess the value of an 18F-FDG-positron emission tomography/computed tomography (PET/CT)-based machine learning model for distinguishing between adrenal benign nodules (ABNs) and adrenal metastases (AMs) in patients with indeterminate adrenal nodules and extra-adrenal malignancies.

METHODS

A total of 303 patients who underwent 18F-FDG-PET/CT with indeterminate adrenal nodules and extra-adrenal malignancies from March 2015 to June 2021 were included in this retrospective study (training dataset (n = 182): AMs (n = 97), ABNs (n = 85); testing dataset (n = 121): AMs (n = 68), ABNs (n = 55)). The clinical and PET/CT imaging features of the two groups were analyzed. The predictive model and simplified scoring system for distinguishing between AMs and ABNs were built based on clinical and PET/CT risk factors using multivariable logistic regression in the training cohort. The performances of the predictive model and simplified scoring system in both the training and testing cohorts were evaluated by the areas under the receiver operating characteristic curves (AUCs) and calibration curves. The comparison of AUCs was evaluated by the DeLong test.

RESULTS

The predictive model included four risk factors: sex, the ratio of the maximum standardized uptake value (SUVmax) of adrenal lesions to the mean liver standardized uptake value, the value on unenhanced CT (CTU), and the clinical stage of extra-adrenal malignancies. The model achieved an AUC of 0.936 with a specificity, sensitivity and accuracy of 0.918, 0.835, and 0.874 in the training dataset, respectively, while it yielded an AUC of 0.931 with a specificity, sensitivity, and accuracy of 1.00, 0.735, and 0.851 in the testing dataset, respectively. The simplified scoring system had comparable diagnostic value to the predictive model in both the training (AUC 0.938, sensitivity: 0.825, specificity 0.953, accuracy 0.885; P = 0.5733) and testing (AUC 0.931, sensitivity 0.735, specificity 1.000, accuracy 0.851; P = 1.00) datasets.

CONCLUSIONS

Our study showed the potential ability of a machine learning model and a simplified scoring system based on clinical and 18F-FDG-PET/CT imaging features to predict AMs in patients with indeterminate adrenal nodules and extra-adrenal malignancies. The simplified scoring system is simple, convenient, and easy to popularize.

Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study

OBJECTIVES

To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population.

METHODS

This retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong's test, calibration curve, and decision curve.

RESULTS

Lesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits.

CONCLUSION

Unenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations.

KEY POINTS

• All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population's adrenal benign lesions and metastases. • Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models. • Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.

Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article

The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-operative differentiation of "early stage" adrenal metastases from lipid-poor adenomas (LPAs). This retrospective study included 188 patients who underwent abdominal CECT (training cohort: LPAs, 68; metastases, 64; validation cohort: LPAs, 29; metastases, 27). Abdominal CECT included plain, arterial, portal, and venous imaging. Clinical and CECT radiological features were assessed and significant features were selected. Radiomic features of the adrenal lesions were extracted from four-phase CECT images. Significant radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression. The clinical-radiological, unenhanced radiomics, arterial radiomics, portal radiomics, venous radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher than those of the clinical-radiological model (0.788) in the testing dataset. The AUC of the combined radiomics model (incorporating plain and venous radiomics features) was further improved to 0.953, which was significantly higher than portal radiomics model (P = .033) and clinical-radiological model (P = .009), with the highest accuracy (89.13%) and a relatively stable sensitivity (91.67%) and specificity (86.36%). As the optimal model, the combined radiomics model based on biphasic CT images is effective enough to differentiate "early stage" adrenal metastases from LPAs by reducing the radiation dose.

Distinguishing between metastatic and benign adrenal masses in patients with extra-adrenal malignancies

BACKGROUND AND OBJECTIVES

The adrenal gland is a common organ involved in metastasis. This study aimed to compare adrenal metastases (AMs) and adrenal benign masses (ABMs) of patients with extra-adrenal malignancies during the staging or follow-up.

METHODS

We retrospectively collected data from 120 patients with AMs and 87 patients with ABMs. The clinical characteristics, imaging features, pathology, and treatment regimes were analyzed.

RESULTS

The most common types of extra-adrenal malignancies in patients with ABMs included thyroid, kidney, and gynecological cancers. On the other hand, lung and kidney cancers and lymphoma were the most frequent primary cancers of AMs. The age and incidence of symptoms were significantly higher in patients with AM. Radiological analysis showed that AMs tended to have larger tumor sizes and higher attenuation values than ABMs on pre-contrast computed tomography (CT). The diagnostic accuracy of positron emission tomography-CT for AM was 94.1%. An adrenal biopsy had a diagnostic accuracy of 92.5%. A multivariate logistic regression model demonstrated that the origins of extra-adrenal malignancies, the enhancement pattern, and attenuation values in pre-contrast CT were independent predictors of AMs. The sensitivity and specificity of this predictive model of combination was 92.5% and 74.1%, respectively.

CONCLUSIONS

The differential diagnosis between AMs and ABMs is extremely important. The combination of origin of first malignancy, enhancement pattern and CT value in non-enhanced phase is a valuable model for predicting AMs.

Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish?

INTRODUCTION

Distant metastases are found in the many of patients with lung cancer at time of diagnosis. Several diagnostic tools are available to distinguish between metastatic spread and benign lesions in the adrenal gland. However, all require additional diagnostic steps after the initial CT. The purpose of this study was to evaluate if texture analysis of CT-abnormal adrenal glands on the initial CT correctly differentiates between malignant and benign lesions in patients with confirmed lung cancer.

MATERIALS AND METHODS

In this retrospective study 160 patients with endoscopic ultrasound-guided biopsy from the left adrenal gland and a contrast-enhanced CT in portal venous phase were assessed with texture analysis. A region of interest encircling the entire adrenal gland was used and from this dataset the slice with the largest cross section of the lesion was analyzed individually.

RESULTS

Several texture parameters showed statistically significantly difference between metastatic and benign lesions but with considerable between-groups overlaps in confidence intervals. Sensitivity and specificity were assessed using ROC-curves, and in univariate binary logistic regression the area under the curve ranged from 36 % (Kurtosis 0.5) to 69 % (Entropy 2.5) compared to 73 % in the best fitting model using multivariate binary logistic regression.

CONCLUSION

In lung cancer patients with abnormal adrenal gland at imaging, adrenal gland texture analyses appear not to have any role in discriminating benign from malignant lesions.

The role of percutaneous CT-guided biopsy of an adrenal lesion in patients with known or suspected lung cancer

PURPOSE

To determine the sensitivity, specificity, and complication rate of percutaneous adrenal biopsy in patients with known or suspected lung cancer.

METHODS

This study was approved by the Institutional Review Board at our institution as a retrospective analysis; therefore, the need for informed consent was waived. All percutaneous adrenal biopsies performed between April 1993 and May 2019 were reviewed. 357 of 582 biopsies were performed on 343 patients with known or suspected lung cancer (M:F 164:179; mean age 66 years). The biopsy results were classified into malignant, benign, or non-diagnostic. The final diagnosis was established by pathology (biopsy and/or surgical resection) or imaging follow-up on CT for at least 12 months following the biopsy. Patients with less than 12 months follow-up were excluded (n = 44). Complications were recorded.

RESULTS

The final diagnosis was metastatic lung cancer in 235 cases (77.8%), metastasis from an extrapulmonary primary in 2 cases (0.7%), pheochromocytoma in 2 cases (0.7%), and benign lesions in 63 cases (20.9%). Percutaneous adrenal gland biopsy had a sensitivity of 97% and specificity of 100% for lung cancer metastases. The non-diagnostic rate was 0.6%. Larger lesions were more likely to be malignant (p = 0.0000) and to be correctly classified as a lung metastasis (p = 0.025). The incidence of minor complications was 1.1%. There were no major complications.

CONCLUSION

Over 20% of adrenal lesions in patients with known or suspected lung cancer were not related to lung cancer. Percutaneous adrenal gland biopsy is a safe procedure, with high sensitivity and specificity for lung cancer metastases.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis

RATIONALE AND OBJECTIVES

This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis.

MATERIALS AND METHODS

Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest.

RESULTS

The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively.

CONCLUSION

ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept

INTRODUCTION

To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool.

MATERIALS AND METHODS

This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features.

RESULTS

Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024).

DISCUSSION

Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.

Patients with extra-adrenal malignancies and adrenal lesions have similar rates of subclinical hypercortisolism compared with patients with true adrenal incidentalomas

OBJECTIVE

During follow-up in cancer patients, adrenal lesions are frequently found by computer tomography imaging. In these patients, the frequency of subclinical Cushing's syndrome (SCS) has not been fully explored. The aim of the present study was to investigate the presence of SCS in cancer patients with adrenal lesions in comparison to patients with true adrenal incidentalomas.

DESIGN

We studied 95 patients with adrenal lesions: 57 patients (group A, 20 males and 37 females) had a history of extra-adrenal malignancy and adrenal lesions were discovered during staging of the primary cancer, and 38 patients (group B, 6 males and 32 females) had adrenal incidentalomas. The two groups had similar BMI. All patients had unenhanced HU < 10 in computed tomography to ensure low risk of adrenal metastatic disease. Patients' morning plasma cortisol levels and ACTH were measured. An overnight 1 mg dexamethasone suppression test (ODST) was performed in all participants; in case of abnormal results, 24-h urine cortisol and the low-dose dexamethasone suppression test were additionally conducted. The cutoffs of morning cortisol values used for ODST were 1.8 and 5 μg/dl.

RESULTS

When the cutoff of 1.8 μg/dl for suppressed morning cortisol was used, 42.1% of group A and 39.5% of group B had abnormal results (p = 0.95). By using the threshold of 5 μg/dl after ODST, 5.3% of group A and 13.2% of group B did not have suppressed cortisol levels with the 1 mg ODST (p = 0.18). The main factors found to influence suppressed cortisol levels after ODST in both groups were BMI and size of the adrenal lesion.

CONCLUSIONS

Patients with extra-adrenal malignancies and adrenal lesions had similar rates of subclinical hypercortisolemia compared to patients with true adrenal incidentalomas.

Renal and adrenal masses containing fat at MRI: Proposed nomenclature by the society of abdominal radiology disease-focused panel on renal cell carcinoma

This article proposes a consensus nomenclature for fat-containing renal and adrenal masses at MRI to reduce variability, improve understanding, and enhance communication when describing imaging findings. The MRI appearance of "macroscopic fat" occurs due to a sufficient number of aggregated adipocytes and results in one or more of: 1) intratumoral signal intensity (SI) loss using fat-suppression techniques, or 2) chemical shift artifact of the second kind causing linear or curvilinear India-ink (etching) artifact within or at the periphery of a mass at macroscopic fat-water interfaces. "Macroscopic fat" is most commonly observed in adrenal myelolipoma and renal angiomyolipoma (AML) and only rarely encountered in other adrenal cortical tumors and renal cell carcinomas (RCC). Nonlinear noncurvilinear signal intensity loss on opposed-phase (OP) compared with in-phase (IP) chemical shift MRI (CSI) may be referred to as "microscopic fat" and is due to: a) an insufficient amount of adipocytes, or b) the presence of fat within tumor cells. Determining whether the signal intensity loss observed on CSI is due to insufficient adipocytes or fat within tumor cells cannot be accomplished using CSI alone; however, it can be inferred when other imaging features strongly suggest a particular diagnosis. Fat-poor AML are homogeneously hypointense on T₂ -weighted (T₂ W) imaging and avidly enhancing; signal intensity loss at OP CSI is uncommon, but when present is usually focal and is caused by an insufficient number of adipocytes within adjacent voxels. Conversely, clear-cell RCC are heterogeneously hyperintense on T₂ W imaging and avidly enhancing, with the signal intensity loss observed on OP CSI being typically diffuse and due to fat within tumor cells. Adrenal adenomas, adrenal cortical carcinoma, and adrenal metastases from fat-containing primary malignancies also show signal intensity loss on OP CSI due to fat within tumor cells and not from intratumoral adipocytes. Level of Evidence: 5 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;49:917-926.

Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?

OBJECTIVE

The purpose of this study is to determine whether second-order texture analysis can be used to distinguish lipid-poor adenomas from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT (CECT), and chemical-shift MRI.

MATERIALS AND METHODS

In this retrospective study, 23 adrenal nodules (15 lipid-poor adenomas and eight adrenal malignancies) in 20 patients (nine female patients and 11 male patients; mean age, 59 years [range, 15-80 years]) were assessed. All patients underwent unenhanced CT, CECT, and chemical-shift MRI. Twenty-one second-order texture features from the gray-level cooccurrence matrix and gray-level run-length matrix were calculated in 3D. The mean values for 21 texture features and four imaging features (lesion size, unenhanced CT attenuation, CECT attenuation, and signal intensity index) were compared using a t test. The diagnostic performance of texture analysis versus imaging features was also compared using AUC values. Multivariate logistic regression models to predict malignancy were constructed for texture analysis and imaging features.

RESULTS

Lesion size, unenhanced CT attenuation, and the signal intensity index showed significant differences between benign and malignant adrenal nodules. No significant difference was seen for CECT attenuation. Eighteen of 21 CECT texture features and nine of 21 unenhanced CT texture features revealed significant differences between benign and malignant adrenal nodules. CECT texture features (mean AUC value, 0.80) performed better than CECT attenuation (mean AUC value, 0.60). Multivariate logistic regression models showed that CECT texture features, chemical-shift MRI texture features, and imaging features were predictive of malignancy.

CONCLUSION

Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.

Bilateral adrenal abnormalities: imaging review of different entities

Bilateral adrenal abnormalities are not infrequently encountered during routine daily radiology practice. The differential diagnoses of bilateral adrenal abnormalities include neoplastic and non-neoplastic entities. The bilateral adrenal tumors include metastasis, lymphoma, neuroblastoma, pheochromocytoma, adenoma, and myelolipoma. Non-neoplastic bilateral adrenal masses include infectious processes and haematomas. There are different diffuse bilateral adrenal changes such as adrenal atrophy, adrenal enlargement, adrenal calcifications, and altered adrenal enhancement. In this pictorial review article, we will discuss the imaging features of these entities with emphasis on their clinical implications.

Perirenal Fat: A Unique Fat Pad and Potential Target for Cardiovascular Disease

Although visceral obesity is recognized as a risk factor for cardiovascular diseases (CVDs), the efficacy of omental fat removal in CVD treatment is still controversial. There is a need to identify other visceral fat depots for CVD management. This review aims to provide a summary on perirenal fat as an important risk factor for CVD. Studies on epidemiology, anatomy, and function of perirenal fat were reviewed. Observational studies in humans suggest that excessive perirenal fat increases the risk of hypertension and coronary heart disease. Anatomy studies prove that perirenal fat is unique compared to other connective tissues in that it is well vascularized, innervated, and drains into the lymphatic system. Other special morphological features include a complete fascia border, sympathetic-independent development of architecture, and proximity to the kidneys. Based on these anatomical features, perirenal fat regulates the cardiovascular system presumably via neural reflex, adipokine secretion, and fat-kidney interaction. These new insights suggest that perirenal fat may constitute a promising target for CVD management.

Recurrent renal cell carcinoma following nephrectomy and ablation therapy: Radiology perspective

Renal cell carcinoma (RCC) is the most common renal malignancy, accounting for approximately 2% of adult malignancies and 4% of new cancer cases in the United States every year. Imaging guided ablative therapy, including radiofrequency (RF) ablation, cryotherapy and microwave has gained popularity over the last decade in treatment of small tumors. Antiangiogenic therapy has set itself to be the standard of care for many patients with metastasis these days. With hope for more research, survival rates of metastatic RCC may increase from a current 2-year survival rate of approximately 20%. Variation in imaging surveillance protocol in terms of frequency, modality, and duration is noted among guidelines developed by several organizations. In this review article, we will discuss follow-up imaging protocols, patterns of RCC recurrence following different modalities of treatment, imaging appearance, as well as usual and unusual sites of metastatic disease.

MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation

OBJECTIVES

To assess the value of the MR-based radiomics signature in differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).

METHODS

One hundred fifty-seven patients with pathology-proven OAL (84 patients) and IOI (73 patients) were divided into primary and validation cohorts. Eight hundred six radiomics features were extracted from morphological MR images. The least absolute shrinkage and selection operator (LASSO) procedure and linear combination were used to select features and build radiomics signature for discriminating OAL from IOI. Discriminating performance was assessed by the area under the receiver-operating characteristic curve (AUC). The predictive results were compared with the assessment of radiologists by chi-square test.

RESULTS

Five radiomics features were included in the radiomics signature, which differentiated OAL from IOI with an AUC of 0.74 and 0.73 in the primary and validation cohorts respectively. There was a significant difference between the classification results of the radiomics signature and those of a radiology resident (p < 0.05), although there was no significant difference between the results of the radiomics signature and those of a more experienced radiologist (p > 0.05).

CONCLUSIONS

Radiomics features have the potential to differentiate OAL from IOI.

KEY POINTS

• Clinical and imaging findings of OAL and IOI often overlap, which makes diagnosis difficult. • Radiomics features can potentially differentiate OAL from IOI non invasively. • The radiomics signature discriminates OAL from IOI at the same level as an experienced radiologist.

Update on CT and MRI of Adrenal Nodules

OBJECTIVE

The objective of this article is to review the current role of CT and MRI for the characterization of adrenal nodules.

CONCLUSION

Unenhanced CT and chemical-shift MRI have high specificity for lipid-rich adenomas. Dual-energy CT provides comparable to slightly lower sensitivity for the diagnosis of lipid-rich adenomas but may improve characterization of lipid-poor adenomas. Nonadenomas containing intracellular lipid pose an imaging challenge; however, nonadenomas that contain lipid may be potentially diagnosed using other imaging features. Multiphase adrenal washout CT can be used to differentiate lipid-poor adenomas from metastases but is limited for the diagnosis of hypervascular malignancies and pheochromocytoma.