Distinguishing pheochromocytoma from adrenal adenoma by using modified computed tomography criteria

Diagnostic performance of magnetic resonance imaging features to differentiate adrenal pheochromocytoma from adrenal tumors with positive biochemical testing results

BACKGROUND

It is extremely essential to accurately differentiate pheochromocytoma from Adrenal incidentalomas (AIs) before operation, especially biochemical tests were inconclusive. We aimed to evaluate the value of magnetic resonance imaging (MRI) features to differentiate pheochromocytomas among adrenal tumors, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive.

METHODS

With institutional review board approval, this study retrospectively compared 35 pheochromocytoma (PHEO) patients with 27 non-pheochromocytoma(non-PHEO) patients between January 2022 to September 2023, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive. T test was used for the independent continuous data and the chi-square test was used for categorical variables. Univariate and multivariate logistic regression were applied to find the independent variate of the features to differentiate PHEO from non-PHEO and ROC analysis was applied to evaluate the diagnostic value of the independent variate.

RESULTS

We found that the T2-weighted (T2W) signal intensity in patients with pheochromocytoma was higher than other adrenal tumors, with greatly significant (p < 0.001). T2W signal intensity ratio (T2W nodule-to-muscle SI ratio) was an independent risk factor for the differential diagnosis of adrenal PHEOs from non-PHEOs. This feature alone had 91.4% sensitivity and 81.5% specificity to rule out pheochromocytoma based on optimal threshold, with an area under the receiver operating characteristics curve (AUC‑ROC) of 0.910(95% C I: 0.833-0.987).

CONCLUSION

Our study confirms that T2W signal intensity ratio can differentiate PHEO from non-PHEO, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive.

Validation of the modified CT criteria for identifying non-adenomas

PURPOSE

Although adrenal computed tomography (CT) percentage washout is a potentially powerful imaging technique for differentiating adrenal adenomas from non-adenomas, its application to non-adenomas can be problematic. Recently, modified criteria for diagnosing pheochromocytomas using adrenal CT were developed based on data from 199 patients with surgically proven pheochromocytomas and adenomas. However, these criteria have not been thoroughly validated. The purpose of this study was to validate the performance of the modified criteria for diagnosing non-adenomas including pheochromocytomas.

METHODS

The conventional and modified criteria were applied to 266 patients from two cohorts who had surgically proven lipid-poor adenomas (155/266, 58.3%) and non-adenomas (111/266, 41.7%) and underwent adrenal CT. Two radiologists calculated the attenuation on each dynamic phase and percentage washout of adrenal masses. The final assessments based on the conventional and modified criteria were categorized into adenomas or non-adenomas. The diagnostic performance of each criterion for diagnosing non-adenomas was evaluated using the area under the receiver operating characteristic curve (AUC). False negatives and positives were also compared.

RESULTS

The AUC for the diagnosis of non-adenomas was 0.806 for conventional criteria and 0.858 for modified criteria (p = 0.047). The false-negative rate of conventional criteria for the diagnosis of non-adenomas was 29.7%. Use of modified criteria could have reduced the false-negative rate by to 7.2%. The false-positive rate increased from 9% to 21.3% when using the modified criteria.

CONCLUSION

The utilization of modified criteria has the potential to identify additional non-adenomas that would otherwise be misdiagnosed as adenomas using conventional criteria alone.

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

BACKGROUND

There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen.

METHODS

We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method.

RESULTS

All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979.

CONCLUSIONS

The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.

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.

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.

Adrenal Neoplasms: Lessons from Adrenal Multidisciplinary Tumor Boards

The radiologic diagnosis of adrenal disease can be challenging in settings of atypical presentations, mimics of benign and malignant adrenal masses, and rare adrenal anomalies. Misdiagnosis may lead to suboptimal management and adverse outcomes. Adrenal adenoma is the most common benign adrenal tumor that arises from the cortex, whereas adrenocortical carcinoma (ACC) is a rare malignant tumor of the cortex. Adrenal cyst and myelolipoma are other benign adrenal lesions and are characterized by their fluid and fat content, respectively. Pheochromocytoma is a rare neuroendocrine tumor of the adrenal medulla. Metastases to the adrenal glands are the most common malignant adrenal tumors. While many of these masses have classic imaging appearances, considerable overlap exists between benign and malignant lesions and can pose a diagnostic challenge. Atypical adrenal adenomas include those that are lipid poor; contain macroscopic fat, hemorrhage, and/or iron; are heterogeneous and/or large; and demonstrate growth. Heterogeneous adrenal adenomas may mimic ACC, metastasis, or pheochromocytoma, particularly when they are 4 cm or larger, whereas smaller versions of ACC, metastasis, and pheochromocytoma and those with washout greater than 60% may mimic adenoma. Because of its nonenhanced CT attenuation of less than or equal to 10 HU, a lipid-rich adrenal adenoma may be mimicked by a benign adrenal cyst, or it may be mimicked by a tumor with central cystic and/or necrotic change such as ACC, pheochromocytoma, or metastasis. Rare adrenal tumors such as hemangioma, ganglioneuroma, and oncocytoma also may mimic adrenal adenoma, ACC, metastasis, and pheochromocytoma. The authors describe cases of adrenal neoplasms that they have encountered in clinical practice and presented to adrenal multidisciplinary tumor boards. Key lessons to aid in diagnosis and further guide appropriate management are provided. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

Inter-method agreement between wash-in and wash-out computed tomography for characterizing hyperattenuating adrenal lesions as adenomas or non-adenomas

OBJECTIVES

To investigate inter-method agreement between wash-out and wash-in computed tomography (CT) to determine whether hyperattenuating adrenal lesions are characterized as adenomas or non-adenomas.

METHODS

We evaluated 243 patients who underwent wash-out CT for a solid enhancing hyperattenuating (i.e., > 10 Hounsfield unit [HU]) adrenal mass of ≥ 1 to < 4 cm. Wash-out (absolute percentage wash-out [APW]; relative percentage wash-out [RPW]) and wash-in values (enhancement ratio [ER]; relative enhancement ratio [RER]) were analyzed by two independent readers. Diagnostic criteria of wash-out CT for adenoma were APW ≥ 60% or RPW ≥ 40% (conventional method). Three different criteria for wash-in CT were set: ER ≥ 3.0; RER ≥ 200%; and RER ≥ 210%. Concordance rate and inter-method agreement between wash-out and wash-in CT were investigated using Gwet's AC1.

RESULTS

For all lesions, concordance rates between wash-out and wash-in CT were > 83%. AC1 between conventional method and ER ≥ 3.0 or between conventional method and RER ≥ 200% were identically 0.843 for reader 1 and 0.776 for reader 2. AC1 between conventional method and RER ≥ 210% were 0.780 for reader 1 and 0.737 for reader 2. For lesions of > 10 to ≤ 30 HU, concordance rates between wash-out and wash-in CT were > 89%. AC1 between conventional method and ER ≥ 3.0 or between conventional method and RER ≥ 200% were identically 0.914 for reader 1 and 0.866 for reader 2. AC1 between conventional method and RER ≥ 210% were 0.888 for reader 1 and 0.874 for reader 2.

CONCLUSION

In approximately 90% of patients with a hyperattenuating adrenal lesion of ≥ 1 to < 4 cm and >10 to ≤ 30 HU, wash-out CT with 15-min contrast-enhanced images may be replaced by wash-in CT.

KEY POINTS

• An enhancement ratio of ≥ 3.0 or a relative enhancement ratio of ≥ 200% appears to be appropriate as the threshold of wash-in computed tomography (CT) comprising unenhanced and 1-min contrast-enhanced CT. • Measurement of enhancement ratio or relative enhancement ratio was reproducible. • We found good agreement between wash-in and wash-out CT for determining whether hyperattenuating adrenal lesions of ≥ 1 to < 4 cm and >10 to ≤ 30 Hounsfield unit would be characterized as adenomas.

Dual-energy spectral detector computed tomography differential diagnosis of adrenal adenoma and pheochromocytoma: Changes in the energy level curve, a phenomenon caused by lipid components?

Background and objectives

Pheochromocytoma and adrenal adenoma are common space-occupying lesions of the adrenal gland, and incorrect surgery may lead to adrenal crisis. We used a new method, dual-energy spectral detector computed tomography (SDCT), to differentiate between the two.

Materials and methods

We analysed the imaging images of patients with SDCT scans and pathologically confirmed adrenal adenomas (n=70) and pheochromocytomas (n=15). The 40, 70, and 100 KeV virtual monoenergetic images (VMIs) were reconstructed based on the SCDT arterial phase, and the correlation between the arterial/venous phase iodine concentration (AP-IC/VP-IC), the effective atomic number (Z-effect), the slope of the Hounsfield unit attenuation plot (VMI slope) and the pathological results was tested. The Shapiro-Wilk test was used to determine whether the above data conformed to a normal distribution. For parameters with P greater than 0.05, Student's t test was used, and the Mann-Whitney test was used for the remaining parameters. A ROC curve was drawn based on the results.

Results

Student's t test showed that the 40 KeV VMI and the VMI slope were both statistically significant (P<0.01). The Mann-Whitney U test showed that ID-A was statistically significant (P=0.004). ROC curve analysis showed that 40 keV VMI (AUC=0.818), AP-IC (AUC=0.736), difference (AUC=0.817) and VMI-Slope (0.817) could be used to differentiate adrenal adenoma from pheochromocytoma.

Conclusion

The effect of lipid components on SDCT parameters can be used to differentiate adrenal adenoma from pheochromocytoma.

Diagnostic value of the relative enhancement ratio of the portal venous phase to unenhanced CT in the identification of lipid-poor adrenal tumors

PURPOSE

Adrenal incidentalomas are common lesions found on abdominal imaging, most of which are lipid-rich adrenal adenomas. Imaging diagnoses differentiating lipid-poor adrenal adenomas (LPA) from non-adenomas (NA) are presently challenging to perform. The aim of the study was to investigate the diagnostic performance of the relative enhancement ratio parameter in identifying LPA from NA.

METHODS

We retrospectively evaluated consecutively presenting patients with lipid-poor adrenal lesions (January 2015 to August 2021). Lesions were divided into LPA and NA (including hyperenhancing and hypoenhancing NA). Kruskal-Wallis and Bonferroni tests were used to determine the differences in feature parameters between these three groups. Receiver operating characteristic curve analysis was performed to determine the sensitivity for diagnosing LPA and NA at 95% specificity; the parameters were compared using the McNemar test.

RESULTS

A total of 253 patients (mean age, 55 ± 12 years; 135 men), 121 with LPA and 132 with NA, were analyzed herein. The sensitivity (achieved at 95% specificity) of the relative enhancement ratio was higher than that of unenhanced attenuation in differentiating LPA from NA (60% vs. 52%, p = 0.064). The relative enhancement ratio yielded a higher sensitivity than unenhanced attenuation (79% vs. 59%, p < 0.001) in differentiating LPA from hypoenhancing NA, and a lower sensitivity (26% vs. 69%, p < 0.001) in differentiating LPA from hyperenhancing NA.

CONCLUSION

The relative enhancement ratio showed better diagnostic performance than unenhanced attenuation in differentiating LPA from hypoenhancing NA, while simultaneously showing poor diagnostic performance in identifying LPA from all NA.

Optimal and novel imaging of the adrenal glands

PURPOSE OF REVIEW

Adrenal imaging forms an important role in the workup of adrenal masses. The purpose of this review is to briefly review the traditional role of imaging in adrenal diseases and highlight the most recent research and new applications aimed to improve diagnostic accuracy.

RECENT FINDINGS

The current review will focus on new applications of computed tomography (CT), MRI and PET/CT imaging, addressing the implications of artificial intelligence and radiomics in progressing diagnostic accuracy.

SUMMARY

The new applications of adrenal imaging are improving diagnostic accuracy and expanding the role of imaging, particularly with novel PET radiotracers and the use of artificial intelligence.

Clinical significance of a 10-mm cutoff size for adrenal lesions: a retrospective study with 547 non-oncologic patients undergoing adrenal computed tomography

PURPOSE

To investigate the proportion of clinically significant adrenal lesions in patients with a subcentimeter adrenal lesion, and the sensitivity of a cutoff size of 10 mm on computed tomography (CT).

METHODS

This retrospective study included consecutive 547 non-oncologic patients who underwent adrenal CT. Clinically significant adrenal lesions were defined as those that were biochemically abnormal (n = 99) or surgically resected according to the clinician's decision (n = 23). Long-axis diameters (LDs) and short-axis diameters (SDs) of the lesions were measured on CT by two independent readers. Likelihood of the focal lesion was analyzed using a five-point scale (1 = very low; 5 = very high). 66 Sensitivities for clinically significant lesions were analyzed according to cutoff size. Proportions of the clinically significant lesions for subcentimeter lesions were analyzed according to the visual score.

RESULTS

Sensitivities for clinically significant lesions for cutoffs of 10, 15, and 20 mm were 93%, 79%, and 63% for LD and 85%, 61%, and 49% for SD for Reader 1 and 89%, 78%, and 65% for LD and 80%, 65%, and 48% for SD for Reader 2, respectively (p < 0.001 for 10 mm versus the other cutoffs). In subcentimeter lesions with visual scores of 1-3, the proportions of clinically significant lesions were 5.4% for LD or SD for Reader 1 and 6.6% for LD and 7.7% for SD for Reader 2, respectively.

CONCLUSION

A lesion LD of ≥ 10 mm was a reasonable cutoff for determining adrenal abnormality. Subcentimeter lesions without visually high suspicion had a low risk of clinical significant lesions in our study cohort. Higher cutoffs significantly decreased sensitivity.

Predictive model of pheochromocytoma based on the imaging features of the adrenal tumours

The purpose of our study was to develop a predictive model to rule out pheochromocytoma among adrenal tumours, based on unenhanced computed tomography (CT) and/or magnetic resonance imaging (MRI) features. We performed a retrospective multicentre study of 1131 patients presenting with adrenal lesions including 163 subjects with histological confirmation of pheochromocytoma (PHEO), and 968 patients showing no clinical suspicion of pheochromocytoma in whom plasma and/or urinary metanephrines and/or catecholamines were within reference ranges (non-PHEO). We found that tumour size was significantly larger in PHEO than non-PHEO lesions (44.3 ± 33.2 versus 20.6 ± 9.2 mm respectively; P < 0.001). Mean unenhanced CT attenuation was higher in PHEO (52.4 ± 43.1 versus 4.7 ± 17.9HU; P < 0.001). High lipid content in CT was more frequent among non-PHEO (83.6% versus 3.8% respectively; P < 0.001); and this feature alone had 83.6% sensitivity and 96.2% specificity to rule out pheochromocytoma with an area under the receiver operating characteristics curve (AUC-ROC) of 0.899. The combination of high lipid content and tumour size improved the diagnostic accuracy (AUC-ROC 0.961, sensitivity 88.1% and specificity 92.3%). The probability of having a pheochromocytoma was 0.1% for adrenal lesions smaller than 20 mm showing high lipid content in CT. Ninety percent of non-PHEO presented loss of signal in the “out of phase” MRI sequence compared to 39.0% of PHEO (P < 0.001), but the specificity of this feature for the diagnosis of non-PHEO lesions low. In conclusion, our study suggests that sparing biochemical screening for pheochromocytoma might be reasonable in patients with adrenal lesions smaller than 20 mm showing high lipid content in the CT scan, if there are no typical signs and symptoms of pheochromocytoma.

Computed Tomography-Based Machine Learning Differentiates Adrenal Pheochromocytoma From Lipid-Poor Adenoma

OBJECTIVES

To assess the accuracy of computed tomography (CT)-based machine learning models for differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in patients with adrenal incidentalomas.

PATIENTS AND METHODS

The study included 188 tumors in the 183 patients with LPA and 92 tumors in 86 patients with sPHEO. Pre-enhanced CT imaging features of the tumors were evaluated. Machine learning prediction models and scoring systems for differentiating sPHEO from LPA were built using logistic regression (LR), support vector machine (SVM) and random forest (RF) approaches.

RESULTS

The LR model performed better than other models. The LR model (M1) including three CT features: CT_pre value, shape, and necrosis/cystic changes had an area under the receiver operating characteristic curve (AUC) of 0.917 and an accuracy of 0.864. The LR model (M2) including three CT features: CT_pre value, shape and homogeneity had an AUC of 0.888 and an accuracy of 0.832. The S2 scoring system (sensitivity: 0.859, specificity: 0.824) had comparable diagnostic value to S1 (sensitivity: 0.815; specificity: 0.910).

CONCLUSIONS

Our results indicated the potential of using a non-invasive imaging method such as CT-based machine learning models and scoring systems for predicting histology of adrenal incidentalomas. This approach may assist the diagnosis and personalized care of patients with adrenal tumors.

Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method

OBJECTIVES

The purpose of this study was to establish a risk prediction model for differential diagnosis of pheochromocytomas (PCCs) from lipid-poor adenomas (LPAs) using a grouping method based on tri-phasic CT image features.

METHODS

In this retrospective study, we enrolled patients that were assigned to a training set (136 PCCs and 183 LPAs) from two medical centers, along with an external independent validation set (30 PCCs and 54 LPAs) from another center. According to the attenuation values in unenhanced CT (CTu), the lesions were divided into three groups: group 1, 10 HU < CTu ≤ 25 HU; group 2, 25 HU < CTu ≤ 40 HU; and group 3, CTu > 40 HU. Quantitative and qualitative CT imaging features were calculated and evaluated. Univariate, ROC, and binary logistic regression analyses were applied to compare these features.

RESULTS

Cystic degeneration, CTu, and the peak value of enhancement in the arterial and venous phase (DEpeak) were independent risk factors for differential diagnosis of adrenal PCCs from LPAs. In all subjects (groups 1, 2, and 3), the model formula for the differentiation of PCCs was as follows: Y = -7.709 + 3.617*(cystic degeneration) + 0.175*(CTu ≥ 35.55 HU) + 0.068*(DEpeak ≥ 51.35 HU). ROC curves were drawn with an AUC of 0.95 (95% CI: 0.927-0.973) in the training set and 0.91 (95% CI: 0.860-0.929) in the external validation set.

CONCLUSION

A reliable and practical prediction model for differential diagnosis of adrenal PCCs and LPAs was established using a grouping method.

Differentiation of lipid-poor adenoma from pheochromocytoma on biphasic contrast-enhanced CT

PURPOSE

To evaluate the diagnostic performance of biphasic contrast-enhanced CT in differentiation of lipid-poor adenomas from pheochromocytomas.

METHODS

129 patients with 132 lipid-poor adenomas and 93 patients with 97 pheochromocytomas confirmed by pathology were included in this retrospective study. Patients underwent unenhanced abdominal CT scan followed by arterial and venous phase. Quantitative and qualitative imaging features were compared between the two groups using univariate analysis. Risk factors for pheochromocytomas were evaluated by multivariate logistic regression analysis and a diagnostic scoring model was established based on odd ratio (OR) of the risk factors.

RESULTS

Pheochromocytomas were larger and showed cystic degeneration more frequently compared with lipid-poor adenomas (p < 0.01). No significant difference was found in peak enhancement phase between the two groups (p = 0.348). Attenuation values on unenhanced phase (CTU), arterial phase (CTA), and venous phase (CTV) of pheochromocytomas were significantly higher than that of lipid-poor adenomas while enhancement ratio on arterial and venous phase (ERA, ERV) of pheochromocytomas was significantly lower than that of lipid-poor adenomas (all p < 0.05). Multivariate analysis revealed lesion size > 29 mm (OR: 5.74; 95% CI 2.51-13.16; p < 0.001), CTA > 81 HU (OR: 2.54; 95% CI 1.04-6.17; p = 0.04), CTV > 97 HU (OR: 11.19; 95% CI 3.21-38.97; p < 0.001), ERV ≤ 1.5 (OR: 20.23; 95% CI 6.30-64.87; p < 0.001), and the presence of cystic degeneration (OR: 6.22, 95% CI 1.74-22.25; p = 0.005) were risk factors for pheochromocytomas. The diagnostic scoring model yielded an area under the curve (AUC) of 0.911.

CONCLUSIONS

Biphasic contrast-enhanced CT showed good diagnostic performance in differentiation of lipid-poor adenomas from pheochromocytomas.

Relative Enhancement Ratio of Portal Venous Phase to Unenhanced CT in the Diagnosis of Lipid-poor Adrenal Adenomas

Background The development of an accurate, practical, noninvasive, and widely available diagnostic approach to characterize lipid-poor adrenal lesions (greater than 10 HU at unenhanced CT) remains an ongoing demand. Purpose To investigate whether combined assessment of unenhanced and portal venous phase CT allows for the differentiation of lipid-poor adrenal adenomas from nonadenomas. Materials and Methods Patients with lipid-poor adrenal lesions who underwent unenhanced and portal venous phase CT with a single-energy scanner between January 2016 and March 2020 were identified retrospectively. For each lesion, the unenhanced and contrast-enhanced attenuation were measured; the absolute enhancement (contrast-enhanced minus unenhanced attenuation [HU]) and relative enhancement ratio ([absolute enhancement divided by unenhanced attenuation] × 100%) were calculated. The sensitivity achieved at 95% specificity to distinguish adenomas from nonadenomas was determined with receiver operating characteristic curve analysis and compared among parameters with use of the McNemar test. Results A total of 220 patients (mean age ± standard deviation, 66 years ± 12; 134 men) with 131 lipid-poor adenomas and 89 nonadenomas were analyzed. The sensitivity (achieved at 95% specificity) of the relative enhancement ratio (86% [113 of 131 adenomas; 95% CI: 79, 92] at a threshold of >210%) was higher than that of unenhanced attenuation (50% [66 of 131 adenomas; 95% CI: 42, 59] at a threshold of ≤21 HU), contrast-enhanced attenuation (3% [four of 131 adenomas; 95% CI: 1, 8] at a threshold of >120 HU), and absolute enhancement (24% [32 of 131 adenomas; 95% CI: 17, 33] at a threshold of >74 HU; all P < .001). The sensitivities of the relative enhancement ratio were 100% (58 of 58 adenomas; 95% CI: 94, 100), 83% (52 of 63 adenomas; 95% CI: 71, 91), and 30% (three of 10 adenomas; 95% CI: 7, 65) for adenomas measuring unenhanced attenuation of more than 10 HU up to 20 HU, 21-30 HU, and more than 30 HU, respectively. Conclusion A relative enhancement ratio threshold of greater than 210%, measured at unenhanced and portal venous phase CT, accurately differentiated lipid-poor adenomas from nonadenomas, particularly for lesions with unenhanced attenuation of 10-30 HU. © RSNA, 2021 Online supplemental material is available for this article.