Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art

Subtyping of Cushing's Syndrome: A Step Ahead

Cushing's Syndrome (CS) is a rare disease due to chronic endogenous cortisol secretion. In recent years, new developments have broadened the spectrum of differential diagnosis, traditionally categorized as adrenocorticotropic hormone (ACTH)-dependent and ACTH-independent forms. Moreover, increased awareness of the detrimental effects of cortisol on cardiometabolic health and the risk of cardiovascular events lead to increased diagnosis of mild forms, especially in the context of adrenal incidentalomas.This review provides an up-to-date narrative of the most recent literature regarding the challenges of CS diagnosis. After the description of the diagnostic tools available, the functional non-neoplastic hypercortisolism (formerly known as pseudo-Cushing state) is characterized, followed by the subtyping of the different conditions of hypercortisolism, including the differential diagnosis of ACTH-dependent forms and the management of adrenal hypercortisolism, with peculiar attention to the new genetic classification of adrenal CS, mild autonomous cortisol secretion, and bilateral adrenal adenomas.

French AFU Cancer Committee Guidelines - Update 2024-2026 : Assessment of an adrenal incidentaloma and oncological management

INTRODUCTION

The aim of this publication is to review the initial management procedure for adrenal incidentalomas, and where appropriate, to establish a carcinological management procedure for malignant adrenal tumours.

MATERIALS AND METHODS

The multidisciplinary working group updated the CCAFU 2022 recommendations for the carcinological management of adrenal incidentalomas on the basis of a comprehensive PubMed review of the literature.

RESULTS

Although the majority of adrenal masses are benign and nonfunctional, it is important to investigate them because of their serious endocrine potential, and because of certain cancers. Malignant adrenal tumours (MCTs) are essentially adrenocortical carcinomas (ACCs), malignant pheochromocytomas (MPCs), and adrenal metastases (AMs). The work-up for the malignancy of an adrenal incidentaloma includes a full history, physical examination, and a biochemical/hormonal assessment to look for subclinical hormone secretion. Diagnostic hypotheses are sometimes available at this stage, but morphological and functional imaging and histological analysis enable the malignancy assessment to be completed, and a carcinological diagnosis to be made.

CONCLUSIONS

SCC and MCC are mostly sporadic, but a hereditary origin is always possible. SCC is suspected preoperatively, but a diagnosis of certainty is histological. The diagnosis of PCM is more delicate and is based on clinical, biological, and imaging findings. The definitive diagnosis of MS requires a percutaneous biopsy. All cases must then be discussed within the COMETE adrenal cancer network.

Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm

PURPOSE

The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC).

MATERIALS AND METHODS

Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard.

RESULTS

Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 ± 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 ± 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87).

CONCLUSION

A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC.

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.

Three-dimensional CT cinematic rendering of adrenal masses: Role in tumor analysis and management

The adrenal gland is home to an array of complex physiological and neoplastic disease processes. While dedicated adrenal computed tomography (CT) is the gold standard imaging modality for adrenal lesions, there exists significant overlap among imaging features of adrenal pathology. This can often make radiological diagnosis and subsequent determination of the optimal surgical approach challenging. Cinematic rendering (CR) is a novel CT post-processing technique that utilizes advanced light modeling to generate highly photorealistic anatomic visualization. This generates unique prospects in the evaluation of adrenal masses. As one of the first large tertiary care centers to incorporate CR into routine diagnostic workup, our preliminary experience with using CR has been positive, and we have found CR to be a valuable adjunct during surgical planning. Herein, we highlight the unique utility of CR techniques in the workup of adrenal lesions and provide commentary on the opportunities and obstacles associated with the application of this novel display method in this setting.

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.

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.

Computed tomographic manifestations of celiac ganglia between hypertensive and non-hypertensive population

The celiac ganglion (CG) is associated with the sympathetic nervous system (SNS) and plays an important role in the pathogenesis of hypertension. The characteristics of the CG in patients with hypertension remain unknown. The aim of our study was to explore the differences in celiac ganglia (CGs) characteristics between hypertensive and non-hypertensive populations using computed tomography (CT). CGs manifestations on multidetector row CT in 1003 patients with and without hypertension were retrospectively analyzed. The morphological characteristics and CT values of the left CGs were recorded. The CT values of the ipsilateral adrenal gland (AG) and crus of the diaphragm (CD) were also measured. The left CG was located between the left AG and CD, and most CGs were long strips. The frequency of visualization of the left CGs was higher in the hypertension group than in the non-hypertension group (p < .05). There were no significant differences in the maximum diameter, size, and shape ratio of the left CGs between the two groups (p > .05). Except for the left CG in the arterial phase, the CT values of the left CG and AG in the non-hypertensive group were higher than those in the hypertension group (p < .05). The venous phase enhancement of the left CG in the non-hypertension group was significantly higher than that in the hypertension group (p < .05). Our findings reveal that CGs have characteristic manifestations in the hypertensive population. As important targets of the SNS, CGs have the potential to regulate blood pressure.

An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

PURPOSE

to perform an external validation of our predictive model to rule out pheochromocytoma (PHEO) based on unenhanced CT in a cohort of patients with PHEOs and adenomas who underwent adrenalectomy.

METHODS

The predictive model was previously developed in a retrospective cohort of 1131 patients presenting with adrenal lesions. In the present study, we performed an external validation of the model in another cohort of 214 patients with available histopathological results.

RESULTS

For the external validation, 115 patients with PHEOs and 99 with adenomas were included. Our previously described predictive model combining the variables of high lipid content and tumor size in unenhanced CT (AUC-ROC: 0.961) had a lower diagnostic accuracy in our current study population for the prediction of PHEO (AUC: 0.750). However, when we excluded atypical adenomas (with Hounsfield units (HU) > 10, n = 39), the diagnostic accuracy increased to 87.4%. In addition, in the whole cohort (including atypical adenomas), when MRI information was included in the model, the diagnostic accuracy increased to up to 85% when the variables tumor size, high lipid content in an unenhanced CT scan, and hyperintensity in the T2 sequence in MRI were included. The probability of PHEO was <0.3% for adrenal lesions <20 mm with >10 HU and without hyperintensity in T2.

CONCLUSION

Our study confirms that our predictive model combining tumor size and lipid content has high reliability for the prediction of PHEO when atypical adrenal lesions are excluded. However, for atypical adrenal lesions with >10 HU in an unenhanced CT scan, MRI information is necessary for a proper exclusion of the PHEO diagnosis.

Prediction of 2-[18F]FDG PET-CT SUVmax for Adrenal Mass Characterization: A CT Radiomics Feasibility Study

BACKGROUND

Indeterminate adrenal masses (AM) pose a diagnostic challenge, and 2-[¹⁸F]FDG PET-CT serves as a problem-solving tool. Aim of this study was to investigate whether CT radiomics features could be used to predict the 2-[¹⁸F]FDG SUVmax of AM.

METHODS

Patients with AM on 2-[¹⁸F]FDG PET-CT scan were grouped based on iodine contrast injection as CT contrast-enhanced (CE) or CT unenhanced (NCE). Two-dimensional segmentations of AM were manually obtained by multiple operators on CT images. Image resampling and discretization (bin number = 16) were performed. 919 features were calculated using PyRadiomics. After scaling, unstable, redundant, and low variance features were discarded. Using linear regression and the Uniform Manifold Approximation and Projection technique, a CT radiomics synthetic value (RadSV) was obtained. The correlation between CT RadSV and 2-[¹⁸F]FDG SUVmax was assessed with Pearson test.

RESULTS

A total of 725 patients underwent PET-CT from April 2020 to April 2021. In 150 (21%) patients, a total of 179 AM (29 bilateral) were detected. Group CE consisted of 84 patients with 108 AM (size = 18.1 ± 4.9 mm) and Group NCE of 66 patients with 71 AM (size = 18.5 ± 3.8 mm). In both groups, 39 features were selected. No statisticallyf significant correlation between CT RadSV and 2-[¹⁸F]FDG SUVmax was found (Group CE, r = 0.18 and p = 0.058; Group NCE, r = 0.13 and p = 0.27).

CONCLUSIONS

It might not be feasible to predict 2-[¹⁸F]FDG SUVmax of AM using CT RadSV. Its role as a problem-solving tool for indeterminate AM remains fundamental.

The True Value of Quantitative Imaging for Adrenal Mass Characterization: Reality or Possibility?

The widespread use of cross-sectional imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), in the evaluation of abdominal disorders has significantly increased the number of incidentally detected adrenal abnormalities, particularly adrenal masses [...].

Artificial intelligence in adrenal imaging: A critical review of current applications

In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.

Clinical, pathophysiologic, genetic and therapeutic progress in Primary Bilateral Macronodular Adrenal Hyperplasia

Patients with primary bilateral macronodular adrenal hyperplasia (PBMAH) usually present bilateral benign adrenocortical macronodules at imaging and variable levels of cortisol excess. PBMAH is a rare cause of primary overt Cushing's syndrome, but may represent up to one third of bilateral adrenal incidentalomas with evidence of cortisol excess. The increased steroidogenesis in PBMAH is often regulated by various G-protein coupled receptors aberrantly expressed in PBMAH tissues; some receptor ligands are ectopically produced in PBMAH tissues creating aberrant autocrine/paracrine regulation of steroidogenesis. The bilateral nature of PBMAH and familial aggregation, led to the identification of germline heterozygous inactivating mutations of the ARMC5 gene, in 20-25% of the apparent sporadic cases and more frequently in familial cases; ARMC5 mutations/pathogenic variants can be associated with meningiomas. More recently, combined germline mutations/pathogenic variants and somatic events inactivating the KDM1A gene were specifically identified in patients affected by GIP-dependent PBMAH. Functional studies demonstrated that inactivation of KDM1A leads to GIP-receptor (GIPR) overexpression and over or down-regulation of other GPCRs. Genetic analysis is now available for early detection of family members of index cases with PBMAH carrying identified germline pathogenic variants. Detailed biochemical, imaging, and co-morbidities assessment of the nature and severity of PBMAH is essential for its management. Treatment is reserved for patients with overt or mild cortisol/aldosterone or other steroid excesses taking in account co-morbidities. It previously relied on bilateral adrenalectomy; however recent studies tend to favor unilateral adrenalectomy, or less frequently, medical treatment with cortisol synthesis inhibitors or specific blockers of aberrant GPCR.

Adrenal nodules for the non-specialist: What to look out for and when to refer

Almost all medical specialities utilise cross-sectional imaging of the abdomen to evaluate many different medical conditions. This ever-increasing use of cross-sectional imaging has led to a dramatic increase in the detection rate of adrenal nodules. Following appropriate biochemical and radiological evaluation, the vast majority of these are shown to be benign adrenal adenomas. A small minority are diagnosed with a functional or malignant lesion that may result in significant morbidity and mortality requiring specialist management.

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >-275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.