The value of 15-minute delayed contrast-enhanced CT to differentiate hyperattenuating adrenal masses compared with chemical shift MR imaging

Clinical‑imaging‑radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas

The aim of the present study was to develop and evaluate a clinical-imaging-radiomic nomogram based on pre-enhanced computed tomography (CT) for pre-operative differentiation lipid-poor adenomas (LPAs) from metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas (AIs). A total of 196 consecutive patients with lung cancer, who underwent initial chest or abdominal pre-enhanced CT scan with small hyperattenuating AIs, were included. The patients were randomly divided into a training cohort with 71 cases of LPAs and 66 cases of metastases, and a testing cohort with 31 cases of LPAs and 28 cases of metastases. Plain CT radiological and clinical features were evaluated, including sex, age, size, pre-enhanced CT value (CTpre), shape, homogeneity and border. A total of 1,316 radiomic features were extracted from the plain CT images of the AIs, and the significant features selected by the least absolute shrinkage and selection operator were used to establish a Radscore. Subsequently, a clinical-imaging-radiomic model was developed by multivariable logistic regression incorporating the Radscore with significant clinical and imaging features. This model was then presented as a nomogram. The performance of the nomogram was assessed by calibration curves and decision curve analysis (DCA). A total of 4 significant radiomic features were incorporated in the Radscore, which yielded notable area under the receiver operating characteristic curves (AUCs) of 0.920 in the training dataset and 0.888 in the testing dataset. The clinical-imaging-radiomic nomogram incorporating the Radscore, CTpre, sex and age revealed favourable differential diagnostic performance (AUC: Training, 0.968; testing, 0.915) and favourable calibration curves. The nomogram was revealed to be more useful than the Radscore and the clinical-imaging model in clinical practice by DCA. The clinical-imaging-radiomics nomogram based on initial plain CT images by integrating the Radscore and clinical-imaging factors provided a potential tool to effectively differentiate LPAs from metastases in patients with lung cancer with small hyperattenuating AIs.

Impact of different peak tube voltage settings on adrenal adenomas attenuation at unenhanced CT

OBJECTIVES

To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.

MATERIALS AND METHODS

IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion's surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).

RESULTS

We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1-4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92-97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (-1 HU, IQR (-5 to 3.6), vs, 2.5 HU, IQR (-1.5 to 8.5); p < 0.001) whereas we didn't find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.

CONCLUSION

AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at "standard" 120 kVp.

CLINICAL RELEVANCE STATEMENT

AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.

KEY POINTS

CT scans are often acquired using peak tube voltage settings different from the "standard" 120 kVp. AA attenuation varies if CT scans are acquired using different tube peak voltage settings. At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.

Non-contrast spectral CT vs chemical-shift MRI in discriminating lipid-poor adrenal lesions

OBJECTIVES

To compare the diagnostic performance of conventional non-contrast CT, dual-energy spectral CT, and chemical-shift MRI (CS-MRI) in discriminating lipid-poor adenomas (> 10-HU on non-contrast CT) from non-adenomas.

METHODS

A total of 110 patients (69 men; 41 women; mean age 66.5 ± 13.4 years) with 80 lipid-poor adenomas and 30 non-adenomas who underwent non-contrast dual-layer spectral CT and CS-MRI were retrospectively identified. For each lesion, non-contrast attenuation on conventional 120-kVp images, ΔHU-index ([attenuation difference between virtual monoenergetic 140-keV and 40-keV images]/conventional attenuation × 100), and signal intensity index (SI-index) were quantified. Each parameter was compared between adenomas and non-adenomas using the Mann-Whitney U-test. The area under the receiver operating characteristic curve (AUC) and sensitivity to achieve > 95% specificity for adenoma diagnosis were determined.

RESULTS

Conventional non-contrast attenuation was lower in adenomas than in non-adenomas (22.4 ± 8.6 HU vs 32.8 ± 48.5 HU), whereas ΔHU-index (148.0 ± 103.2 vs 19.4 ± 25.8) and SI-index (41.6 ± 19.6 vs 4.2 ± 10.2) were higher in adenomas (all, p < 0.001). ΔHU-index showed superior performance to conventional non-contrast attenuation (AUC: 0.919 [95% CI: 0.852-0.963] vs 0.791 [95% CI: 0.703-0.863]; sensitivity: 75.0% [60/80] vs 27.5% [22/80], both p < 0.001), and near equivalent to SI-index (AUC: 0.952 [95% CI: 0.894-0.984], sensitivity 85.0% [68/80], both p > 0.05). Both the ΔHU-index and SI-index provided a sensitivity of 96.0% (48/50) for hypoattenuating adenomas (≤ 25 HU). For hyperattenuating (> 25 HU) adenomas, SI-index showed higher sensitivity than ΔHU-index (66.7% [20/30] vs 40.0% [12/30], p = 0.022).

CONCLUSIONS

Non-contrast spectral CT and CS-MRI outperformed conventional non-contrast CT in distinguishing lipid-poor adenomas from non-adenomas. While CS-MRI demonstrated superior sensitivity for adenomas measuring > 25 HU, non-contrast spectral CT provided high discriminative values for adenomas measuring ≤ 25 HU.

CLINICAL RELEVANCE STATEMENT

Spectral attenuation analysis improves the diagnostic performance of non-contrast CT in discriminating lipid-poor adrenal adenomas, potentially serving as an alternative to CS-MRI and obviating the necessity for additional diagnostic workup in indeterminate adrenal incidentalomas, particularly for lesions measuring ≤ 25 HU.

KEY POINTS

Incidental adrenal lesion detection has increased as abdominal CT use has become more frequent. Non-contrast spectral CT and CS-MRI differentiated lipid-poor adenomas from non-adenomas better than conventional non-contrast CT. For lesions measuring ≤ 25 HU, spectral CT may obviate the need for additional evaluation.

Prevalence of 'Fat-Poor' Adrenal Adenomas at Chemical-Shift MRI

OBJECTIVE

: To determine the prevalence of 'fat-poor' adrenal adenomas at chemical-shift-MRI.

MATERIALS AND METHODS

: This prospective IRB approved study identified 104 consecutive patients with 127 indeterminate adrenal masses that underwent 1.5-T chemical-shift-MRI between 2021-2023. Two blinded radiologists independently measured: 1) 2-Dimensionsal (2D) chemical-shift signal intensity (SI)-index on 2D Chemical-shift-MRI (SI-index >16.5% diagnosed presence of microscopic fat), 2) unenhanced CT attenuation (in cases where unenhanced CT was available).

RESULTS

: From 127 adrenal masses, there were 94% (119/127) adenomas and 6% (8/127) other masses (2 pheochromocytoma, 5 metastases, 1 lymphoma). 98% (117/119) adenomas had SI-Index >16.5%, only 2% (2/119) adenomas were 'fat-poor' by MRI. SI-Index >16.5% was 100% specific for adenoma, all other masses had SI-Index <16.5%. Unenhanced CT was available in 43% (55/127) lesions (50 adenomas, 5 other masses). 34% (17/50) adenomas were lipid-poor (>10 HU). Percentage of adenomas with SI-Index >16.5% were: 1) ≤10 HU, 100% (33/33), 2) 11-29 HU, 100% (12/12), 3) ≥30 HU, 60% (3/5). No other masses had attenuation ≤10 HU (0/5).

CONCLUSION

: Fat-poor adrenal adenomas are uncommon using 2D chemical-shift signal intensity index >16.5% at 1.5-T, occurring in approximately 2% of adenomas in this large prospective series.

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.

Dual-energy CT in the differentiation between adrenal adenomas and metastases: Usefulness of material density maps and monochromatic images

OBJECTIVE

To evaluate the behavior of adrenal adenomas and metastases with dual-energy CT, analyzing the attenuation coefficient in monochromatic images at three different levels of energy (45, 70, and 140 keV) and the tissue concentrations of fat, water, and iodine in material density maps, with the aim of establishing optimal cutoffs for differentiating between these lesions and comparing our results against published evidence.

MATERIALS AND METHODS

This retrospective case-control study included oncologic patients diagnosed with adrenal metastases in the 6-12 months prior to the study who were followed up in our hospital between January and June 2020. For each case (patient with metastases) included in the study, we selected a control (patient with an adrenal adenoma) with a nodule of similar size. All patients were studied with a rapid-kilovoltage-switching dual-energy CT scanner, using a biphasic acquisition protocol. We analyzed the concentration of iodine in paired water-iodine images, the concentration of fat in the paired water-fat images, and the concentration of water in the paired iodine-water and fat-water images, in both the arterial and portal phases. We also analyzed the attenuation coefficient in monochromatic images (at 55, 70, and 140 keV) in the arterial and portal phases.

RESULTS

In the monochromatic images, in both the arterial and portal phases, the attenuation coefficient at all energy levels was significantly higher in the group of patients with metastases than in the group of patients with adenomas. This enabled us to calculate the optimal cutoffs for classifying lesions as adenomas or metastases, except for the arterial phase at 55 KeV, where the area under the receiver operating characteristic curve (AUC) for the estimated threshold (0.68) was not considered accurate enough to classify the lesions. For the arterial phase at 70 keV, the AUC was 0.76 (95% CI: 0.663‒0.899); the optimal cutoff (42.4 HU) yielded 92% sensitivity and 60% specificity. For the arterial phase at 140 keV, the AUC was 0.94 (95% CI: 0.894‒0.999); the optimal cutoff (18.9 HU) yielded 88% sensitivity and 94% specificity). For the portal phase at 55 keV, the AUC was 0.76 (95% CI: 0.663‒0.899); the optimal cutoff (95.4 HU) yielded 68% sensitivity and 84% specificity. For the portal phase at 70 keV, the AUC was 0.82 (95% CI: 0.757‒0.955); the optimal cutoff (58.4 HU) yielded 80% sensitivity and 84% specificity. For the portal phase at 140 keV, the AUC was 0.9 (95% CI: 0.834‒0.987); the optimal cutoff (16.35 HU) yielded 96% sensitivity and 84% specificity. In the material density maps, in the arterial phase, significant differences were found only for the iodine-water pair, where the concentration of water was higher in the group with metastases (1018.8 ± 7.6 mg/cm3 vs. 998.6 ± 8.0 mg/cm3 for the group with adenomas, p < 0.001). The AUC was 0.97 (95% CI: 0.893‒0.999); the optimal cutoff (1012.5 mg/cm3) yielded 88% sensitivity and 96% specificity. The iodine-water pair was also significantly higher in metastases (1019.7 ± 12.1 mg/cm3 vs. 998.5 ± 9.1 mg/cm3 in adenomas, p < 0.001). The AUC was 0.926 (95% CI: 0.807‒0.977); the optimal cutoff (1009.5 mg/cm3) yielded 92% sensitivity and 92% specificity. Although significant results were also observed for the fat-water pair in the portal phase, the AUC was insufficient to enable a sufficiently accurate cutoff for classifying the lesions. No significant differences were found in the fat-water maps or iodine-water maps in the arterial or portal phase or in the water-fat map in the arterial phase.

CONCLUSIONS

Monochromatic images show differences between the behavior of adrenal adenomas and metastases in oncologic patients studied with intravenous-contrast-enhanced CT, where the group of metastases had higher attenuation than the group of adenomas in both the arterial and portal phases; this pattern is in line with the evidence published for adenomas. Nevertheless, to our knowledge, no other publications report cutoffs for this kind of differentiation in contrast-enhanced monochromatic images obtained in rapid-kilovoltage-switching dual-energy CT scanners, and this is the first new contribution of our study. Regarding the material density maps, our results suggest that the water-iodine pair is a good tool for differentiating between adrenal adenomas and metastases, in both the arterial and portal phases. We propose cutoffs for differentiating these lesions, although to our knowledge no cutoffs have been proposed for portal-phase contrast-enhanced images obtained with rapid-kilovoltage-switching dual-energy CT scanners.

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.

Small hyperattenuating adrenal nodules in patients with lung cancer: Differentiation of metastases from adenomas on biphasic contrast-enhanced computed tomography

Objective

The objective of this study was to evaluate the value of biphasic contrast-enhanced computed tomography (CECT) in the differential diagnosis of metastasis and lipid-poor adenomas (LPAs) in lung cancer patients with unilateral small hyperattenuating adrenal nodule.

Materials and methods

This retrospective study included 241 lung cancer patients with unilateral small hyperattenuating adrenal nodule (metastases, 123; LPAs, 118). All patients underwent plain chest or abdominal computed tomography (CT) scan and biphasic CECT scan, including arterial and venous phases. Qualitative and quantitative clinical and radiological characteristics of the two groups were compared using univariate analysis. An original diagnostic model was developed using multivariable logistic regression, and then, according to odds ratio (OR) of the risk factors of metastases, a diagnostic scoring model was developed. The areas under the receiver operating characteristic curves (AUCs) of the two diagnostic models were compared by DeLong test.

Results

Compared with LAPs, metastases were older and showed more frequently irregular in shape and cystic degeneration/necrosis (all p < 0.05). Enhancement ratios on venous (ERV) and arterial (ERA) phase of LAPs were noticeably higher than that of metastases, whereas CT values in unenhanced phase (UP) of LPAs were noticeably lower than that of metastases (all p < 0.05). Compared with LAPs, the proportions of male and III/IV clinical stage and small-cell lung cancer (SCLL) were significantly higher for metastases (all p < 0.05). As for peak enhancement phase, LPAs showed relatively faster wash-in and earlier wash-out enhancement pattern than metastases (p < 0.001). Multivariate analysis revealed age ≥ 59.5 years (OR: 2.269; p = 0.04), male (OR: 3.511; p = 0.002), CT values in UP ≥ 27.5 HU (OR: 6.968; p < 0.001), cystic degeneration/necrosis (OR: 3.076; p = 0.031), ERV ≤ 1.44 (OR: 4.835; p < 0.001), venous phase or equally enhanced (OR: 16.907; p < 0.001 or OR: 14.036; p < 0.001), and clinical stage II or III or IV (OR: 3.550; p = 0.208 or OR: 17.535; p = 0.002 or OR: 20.241; p = 0.001) were risk factors for diagnosis of metastases. AUCs of the original diagnostic model and the diagnostic scoring model for metastases were 0.919 (0.883-0.955) and 0.914 (0.880-0.948), respectively. There was no statistical significance of AUC between the two diagnostic model (p = 0.644).

Conclusions

Biphasic CECT performed well diagnostic ability in differentiating metastases from LAPs. The diagnostic scoring model is easy to popularize due to simplicity and convenience.

Using CT radiomic features based on machine learning models to subtype adrenal adenoma

BACKGROUND

Functioning and non-functioning adrenocortical adenoma are two subtypes of benign adrenal adenoma, and their differential diagnosis is crucial. Current diagnostic procedures use an invasive method, adrenal venous sampling, for endocrinologic assessment.

METHODS

This study proposes establishing an accurate differential model for subtyping adrenal adenoma using computed tomography (CT) radiomic features and machine learning (ML) methods. Dataset 1 (289 patients with adrenal adenoma) was collected to develop the models, and Dataset 2 (54 patients) was utilized for external validation. Cuboids containing the lesion were cropped from the non-contrast, arterial, and venous phase CT images, and 1,967 features were extracted from each cuboid. Ten discriminative features were selected from each phase or the combined phases. Random forest, support vector machine, logistic regression (LR), Gradient Boosting Machine, and eXtreme Gradient Boosting were used to establish prediction models.

RESULTS

The highest accuracies were 72.7%, 72.7%, and 76.1% in the arterial, venous, and non-contrast phases, respectively, when using radiomic features alone with the ML classifier of LR. When features from the three CT phases were combined, the accuracy of LR reached 83.0%. After adding clinical information, the area under the receiver operating characteristic curve increased for all the machine learning methods except for LR. In Dataset 2, the accuracy of LR was the highest, reaching 77.8%.

CONCLUSION

The radiomic features of the lesion in three-phase CT images can potentially suggest the functioning or non-functioning nature of adrenal adenoma. The resulting radiomic models can be a non-invasive, low-cost, and rapid method of minimizing unnecessary testing in asymptomatic patients with incidentally discovered adrenal adenoma.

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.

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.

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.

Adrenal incidentalomas: imaging challenges—role of MDCT scan versus MRI in evaluating adrenal incidentalomas

Background

The term adrenal incidentaloma (AI), by definition, is an adrenal mass that is unexpectedly detected through an imaging procedure performed for reasons unrelated to adrenal dysfunction or suspected dysfunction. Despite their frequent appearance, the challenge remains in recognizing and treating the small percentage of AI that poses a significant risk, either because of their hormonal activity or because of their malignant histology. The aim of this study is to study the role of MRI, specifically chemical shift imaging (CSI), against various MDCT scans (non-enhanced, enhanced, and delayed) in the characterization of incidentally discovered adrenal masses to offer a way for the patients to avoid unnecessary time and money-wasting imaging modalities used to reach a diagnosis of their incidentally discovered adrenal lesions. We examined a total number of 20 patients with total of 22 adrenal lesions. The mean age was 51.1 ± 15.27.

Results

In our study, we found that among CT parameters, APW and RPW showed the highest sensitivity and specificity for detection of lipid-rich adenomas. CSI has also proven to be the best MR technique. However, there is no statistically significant difference in the diagnostic capability of CSI versus the CT washout technique. Both modalities could be conducted, according to specific patient preferences and/or limitations, with comparable highly accurate outcomes.

Conclusion

This study demonstrates that a similar diagnostic outcome is obtained from contrast-enhanced CT (CECT) and MRI with CSI of adrenal lesions.

ACR Appropriateness Criteria® Adrenal Mass Evaluation: 2021 Update

The appropriate evaluation of adrenal masses is strongly dependent on the clinical circumstances in which it is discovered. Adrenal incidentalomas are masses that are discovered on imaging studies that have been obtained for purposes other than adrenal disease. Although the vast majority of adrenal incidentalomas are benign, further radiological and biochemical evaluation of these lesions is important to arrive at a specific diagnosis. Patients with a history of malignancy or symptoms of excess hormone require different imaging evaluations than patients with incidentalomas. This document reviews imaging approaches to adrenal masses and the various modalities utilized in evaluation of adrenal lesions. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Comparison of MRI features in lipid-rich and lipid-poor adrenal adenomas using subjective and quantitative analysis

OBJECTIVE

To compare MR-imaging features in benign lipid-rich and lipid-poor adrenal adenomas.

MATERIALS AND METHODS

With institutional review board approval, we compared 23 consecutive lipid-poor adenomas (chemical shift [CS] signal intensity [SI] index < 16.5%) imaged with MRI to 29 consecutive lipid-rich adenomas (CS-SI index ≥ 16.5%) imaged during the same time period. A blinded radiologist measured T2-weighted (T2W) SI ratio (adrenal adenoma/psoas muscle), dynamic enhancement wash-in (WI) and wash-out (WO) indices, and T2W texture features. Two blinded Radiologists (R1/R2) assessed T2W-SI (relative to renal cortex) and T2W heterogeneity (using 5-Point Likert scales). Comparisons were performed between groups using independent t tests and Chi-square with Holm-Bonferroni correction.

RESULTS

There was no difference in age or gender between groups (p = 0.594, 0.051 respectively). Subjectively, all lipid-rich and lipid-poor adenomas were rated hypointense or isointense compared to renal cortex and T2W-SI did not differ between groups (p = 0.129, 0.124 for R1, R2). Agreement was substantial (Kappa = 0.67). There was no difference in T2W SI ratio (1.8 ± 0.9 [0.5-4.3] lipid rich versus 2.2 ± 1.0 [0.6-4.3] lipid poor, p = 0.139). Enhancement WI and WO did not differ comparing lipid-rich and lipid-poor adenomas (p = 0.759, 0.422 respectively). There was no difference comparing lipid-rich and lipid-poor adenomas T2W heterogeneity judged subjectively (p = 0.695, 0.139 for R1, R2; Kappa = 0.19) or by texture analysis (entropy, kurtosis, skewness; p = 0.134-0.191) with all adenomas except for one rated as mostly or completely homogeneous.

CONCLUSIONS

There is no difference in T2W signal intensity, enhancement pattern or T2W heterogeneity judged subjectively or by quantitative texture analysis comparing lipid-poor and lipid-rich adrenal adenomas.

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.

Unenhanced Dual-Layer Spectral-Detector CT for Characterizing Indeterminate Adrenal Lesions

Background Unenhanced dual-layer spectral-detector CT may facilitate adrenal lesion characterization; however, no studies have evaluated its incremental diagnostic yield for indeterminate lesions (unenhanced attenuation >10 HU) in comparison to that with conventional unenhanced CT. Purpose To determine whether spectral attenuation analysis improves characterization of lipid-poor adrenal adenomas from nonadenomas compared to that with mean attenuation and histogram analysis of conventional CT images. Materials and Methods This retrospective study included patients with indeterminate adrenal lesions who underwent unenhanced dual-layer spectral-detector CT between March 2018 and June 2020. Mean attenuation on conventional 120-kVp images (HU_conv), histogram-based percentage negative pixels (proportion of all pixels <0 HU) on conventional 120-kVp images, and mean attenuation on virtual monoenergetic images (VMIs) at 40-140 keV were measured for each lesion. The attenuation difference between virtual monoenergetic 140- and 40-keV images (ΔHU; ie, Hounsfield unit at 140 keV - Hounsfield unit at 40 keV) and ΔHU indexed with HU_conv (ΔHU index; ie, ΔHU/HU_conv × 100) were calculated. Conventional and virtual monoenergetic imaging parameters were compared between lipid-poor adenomas and nonadenomas by using the Mann-Whitney U test. Receiver operating characteristic analysis was performed to determine the sensitivity for attaining at least 95% specificity in characterizing adenomas from nonadenomas; sensitivity was compared by using the McNemar test. Results A total of 232 patients (mean age ± standard deviation, 67 years ± 11; 145 men) with 129 lipid-poor adenomas and 103 nonadenomas were evaluated. HU_conv and mean attenuation on VMIs at 40-140 keV were lower and the percentage negative pixels, ΔHU, and ΔHU index higher in lipid-poor adenomas than in nonadenomas (P < .001 for all). Attenuation differences between adenomas and nonadenomas on VMIs were maximal at 40 keV (23 HU at 40 keV vs 5 HU at 140 keV). The highest sensitivities for differentiating adenomas and nonadenomas were achieved for virtual monoenergetic ΔHU index (77% [99 of 129 adenomas]), attenuation on 40-keV images (71% [91 of 129 adenomas]), and ΔHU (67% [87 of 129 adenomas]) compared to HU_conv (35% [45 of 129 adenomas]) and percentage negative pixels (30% [39 of 129 adenomas]) (P < .001 for all; specificity, 95% [98 of 103 nonadenomas]). Conclusion Spectral attenuation analysis enabled differentiation of lipid-poor adenomas from nonadenomas with higher sensitivity than mean attenuation or histogram analysis of conventional CT images. © RSNA, 2021 Online supplemental material is available for this article.

Hyperattenuating adrenal lesions in lung cancer: biphasic CT with unenhanced and 1-min enhanced images reliably predicts benign lesions

OBJECTIVES

To investigate usefulness of biphasic computed tomography (CT) in characterizing hyperattenuating adrenal lesions in lung cancer.

METHODS

This retrospective study included 239 patients with lung cancer who underwent adrenal CT for hyperattenuating (> 10 Hounsfield unit) adrenal lesions. Adrenal CT comprised unenhanced and 1-min and 15-min enhanced images. We dichotomized adrenal lesions depending on benign or metastatic lesions. Reference standard for benignity was histologic confirmation or ≥ 6-month stability on follow-up CT. Two independent readers analyzed absolute (APW) or relative percentage wash-out (RPW) using triphasic CT, and enhancement ratio (ER) or percentage wash-in (PWI) using biphasic CT (i.e., unenhanced and 1-min enhanced CT). Criteria for benignity were as follows: criteria 1, (a) APW ≥ 60% or (b) RPW ≥ 40%, and criteria 2, (a) ER > 3 and (b) PWI > 200%. We analyzed area under the curve (AUC) and accuracy for benignity, and inter-reader agreement.

RESULTS

Proportion of benign adrenal lesion was 71.1% (170/239). For criteria 1 and 2, AUCs were 0.872 (95% confidence interval [CI], 0.822-0.911) and 0.886 (95% CI, 0.838-0.923), respectively, for reader 1 (p = 0.566) and 0.816 (95% CI, 0.761-0.863) and 0.814 (95% CI, 0.759-0.862), respectively, for reader 2 (p = 0.955), and accuracies were 87.9% (210/239) and 86.2% (206/239), respectively, for reader 1 (p = 0.479) and 81.2% (194/239) and 80.3% (192/239), respectively, for reader 2 (p = 0.763). Weighted kappa was 0.725 (95% CI, 0.634-0.816) for criteria 1 and 0.736 (95% CI, 0.649-0.824) for criteria 2.

CONCLUSION

Biphasic CT can reliably characterize hyperattenuating adrenal lesions in patients with lung cancer.

KEY POINTS

• Criteria from biphasic computed tomography (CT) for diagnosing benign adrenal lesions were enhancement ratio of > 3 and percentage wash-in of > 200%. • In the analysis by two independent readers, area under the curve between criteria 1 and 2 was not significantly different (0.872 and 0.886 for reader 1; 0.816 and 0.814, for reader 2; p > 0.05 for each comparison). • Wash-in characteristics from biphasic CT are helpful to predict benign adrenal lesions in lung cancer.

Utility of T2-weighted MRI to Differentiate Adrenal Metastases from Lipid-Poor Adrenal Adenomas

Purpose

To evaluate T2-weighted MRI features to differentiate adrenal metastases from lipid-poor adenomas.

Materials and Methods

With institutional review board approval, this study retrospectively compared 40 consecutive patients (mean age, 66 years ± 10 [standard deviation]) with metastases to 23 patients (mean age, 60 years ± 15) with lipid-poor adenomas at 1.5- and 3-T MRI between June 2016 and March 2019. A blinded radiologist measured T2-weighted signal intensity (SI) ratio (SI_nodule/SI_{psoas muscle}), T2-weighted histogram features, and chemical shift SI index. Two blinded radiologists (radiologist 1 and radiologist 2) assessed T2-weighted SI and T2-weighted heterogeneity using five-point Likert scales.

Results

Subjectively, T2-weighted SI (P < .001 for radiologist 1 and radiologist 2) and T2-weighted heterogeneity (P < .001, for radiologist 1 and radiologist 2) were higher in metastases compared with adenomas when assessed by both radiologists. Agreement between the radiologists was substantial for T2-weighted SI (Cohen κ = 0.67) and T2-weighted heterogeneity (κ = 0.62). Metastases had higher T2-weighted SI ratio than adenomas (3.6 ± 1.7 [95% confidence interval {CI}: 0.2, 8.2] vs 2.2 ± 1.0 [95% CI: 0.6, 4.3], P < .001) and higher T2-weighted entropy (6.6 ± 0.6 [95% CI: 4.9, 7.5] vs 5.0 ± 0.8 [95% CI: 3.5, 6.6], P < .001). At multivariate analysis, T2-weighted entropy was the best differentiating feature (P < .001). Chemical shift SI index did not differ between metastases and adenomas (P = .748). Area under the receiver operating characteristic curve (AUC) for T2-weighted SI ratio and T2-weighted entropy were 0.76 (95% CI: 0.64, 0.88) and 0.94 (95% CI: 0.88, 0.99). The logistic regression model combining T2-weighted SI ratio with T2-weighted entropy yielded AUC of 0.95 (95% CI: 0.91, 0.99) and did not differ compared with T2-weighted entropy alone (P = .268). There was no difference in logistic regression model accuracy comparing the data by either field strength, 1.5- or 3-T MRI (P > .05).

Conclusion

Logistic regression models combining T2-weighted SI and T2-weighted heterogeneity can differentiate metastases from lipid-poor adenomas. Validation of these preliminary results is required.Keywords: Adrenal, MR-Imaging, UrinarySupplemental material is available for this article.© RSNA, 2020.

Differentiation between adrenal adenomas and nonadenomas using dynamic contrast-enhanced computed tomography

This study was performed to evaluate the findings including the time density curve (TD curve), the relative percentage of enhancement washout (Washr) and the absolute percentage of enhancement washout (Washa) at dynamic contrast-enhanced computed tomography (DCE-CT) in 70 patients with 79 adrenal masses (including 44 adenomas and 35 nonadenomas) confirmed histopathologically and/or clinically. The results demonstrated that the TD curves of adrenal masses were classified into 5 types, and the type distribution of the TD curves was significantly different between adenomas and nonadenomas. Types A and C were characteristic of adenomas, whereas types B, D and E were features of nonadenomas. The sensitivity, specificity and accuracy for the diagnosis of adenoma based on the TD curves were 93%, 80% and 87%, respectively. Furthermore, when myelolipomas were excluded, the specificity and accuracy for adenoma were 90% and 92%, respectively. The Washr and the Washa values for the adenomas were higher than those for the nonadenomas. The diagnostic efficiency for adenoma was highest at 7-min delay time at DCE-CT; Washr was more efficient than Washa. Washr ≥34% and Washa ≥43% were both suggestive of adenomas and, on the contrary, suspicious of nonadenomas. The sensitivity, specificity and accuracy for the diagnosis of adenoma were 84%, 77% and 81%, respectively. When myelolipomas were precluded, the diagnostic specificity and accuracy were 87% and 85%, respectively. Therefore, DCE-CT aids in characterization of adrenal tumors, especially for lipid-poor adenomas which can be correctly categorized on the basis of TD curve combined with the percentage of enhancement washout.

Determination of a cutoff attenuation value on single-phase contrast-enhanced CT for characterizing adrenal nodules via chemical shift MRI

PURPOSE

This study aims to determine the optimal cutoff attenuation value on single-phase contrast-enhanced CT (CECT) at which chemical shift MRI (CSMRI) yields sufficient accuracy to replace the standard CT adrenal protocol for the diagnosis of adrenal adenomas.

METHODS

Between January 2010 and December 2014, a total of 49 patients (age: 20-81 years; 23 men and 26 women) with 60 adrenal tumors (48 adenomas and 12 non-adenomas) who underwent both CECT in portal venous phase and CSMRI were included in the study. Attenuation on portal venous phase CECT, adrenal-to-spleen chemical shift ratio (ASR), and signal-intensity index (SII) were obtained for each adrenal mass.

RESULTS

Among different cutoff values on CECT (from <70 to <120 HU), the diagnostic accuracies for those lesions measuring <80 HU were the highest and most similar to dedicated adrenal CT. The sensitivities and specificities of SII were up to 96% (25/26) and 100% (7/7) for those measuring <80 HU, but reduced to 73% (16/22) and 80% (4/5) for those ≥80 HU. The overall sensitivities and specificities for diagnosing adrenal adenoma using SII vs. ASR were 85% (41/48) and 92% (11/12) vs. 71% (34/48) and 100% (12/12), respectively.

CONCLUSIONS

CSMRI may replace CT adrenals in the work-up of patients with adrenal nodules below 80 HU on single-phase CECT, hence reducing radiation exposure and iodinated contrast administration. Adrenal nodules greater than 80 HU cannot be accurately diagnosed by CSMRI. CT adrenal protocol remains the appropriate investigative modality in those cases.

Adrenal imaging for adenoma characterization: imaging features, diagnostic accuracies and differential diagnoses

Adrenocortical adenoma is the most common adrenal tumour. This lesion is frequently encountered on cross-sectional imaging that has been performed for unrelated reasons. Adrenal adenoma manifests various imaging features on CT, MRI and positron emission tomography/CT. The learning objectives of this review are to describe the imaging findings of adrenocortical adenoma, to compare the sensitivities of different imaging modalities for adenoma characterization and to introduce differential diagnoses.

Work-up of the Incidental Adrenal Mass

CONTEXT

Incidental adrenal masses (or adrenal incidentalomas [AI]) are a common finding during imaging and are present in up to 5% of the computed tomography (CT) scans performed on the general population. The best way to manage these lesions is still under discussion.

OBJECTIVE

To evaluate recent literature and available guidelines regarding the work-up of AIs.

EVIDENCE ACQUISITION

We used a medical search engine to identify studies published in the past 5 yr regarding AIs. We also evaluated current guidelines and the most relevant papers published before 2010.

EVIDENCE SYNTHESIS

Unenhanced and contrast-enhanced CT, with laboratory tests to exclude functional lesions, are the most sensitive and specific methods currently available for the characterisation of adrenal masses. Magnetic resonance imaging, positron emission tomography-CT and fine-needle aspiration biopsy can be used as adjunct diagnostic tools in indeterminate lesions but are rarely indicated. In a relatively high number of indeterminate nodules, follow-up or surgery is suggested, although most of these lesions turn out to be benign.

CONCLUSIONS

Various imaging modalities, with CT being most important, are available to diagnose malignant and functional lesions in AIs. An improved identification of benign lesions is warranted to reduce the number of unnecessary surgeries and follow-up examinations in patients with benign lesions.

PATIENT SUMMARY

We performed a review of the literature on and guidelines for the management of incidental adrenal masses. It is possible to detect the presence of lesions that require surgery in the majority of cases. Follow-up is required for lesions that are not treated surgically.

Adrenal Imaging and Intervention

The adrenal glands are a common site for primary benign and malignant tumors and metastatic disease. Computed tomography (CT), MR imaging, and fluorine-18 fluorodeoxyglucose PET combined with CT are the most common imaging modalities used to assess the adrenal glands. There are established morphologic criteria for both CT and MR imaging that can be used to assess whether an adrenal mass is benign or malignant, and whether follow-up, biopsy, or resection should be performed. In the setting of a known primary malignancy, CT, MR imaging, and PET can help differentiate most benign masses from metastasis.

Extravascular incidental findings in run-off CT angiography in patients with acute limb ischaemia: incidence and clinical relevance

AIM

To evaluate the incidence and clinical relevance of extravascular incidental findings (EVIFs) in CT angiography of the abdominal aorta and lower extremities (run-off CTA) in patients presenting with acute limb ischaemia (ALI).

MATERIALS AND METHODS

In this institutional review board-approved, retrospective study, 141 run-off CTA examinations conducted between 2005 and 2013 of patients (67 women, mean age 80 years; 74 men, mean age 69 years) presenting with clinical symptoms of ALI were re-evaluated by two radiologists (2 and 7 years of experience in interpreting run-off CTA). Imaging was conducted using 16- and 64-section CT systems. Image acquisition ranged from the costodiaphragmatic recess to the forefoot. The medical history form of each patient served as the standard of reference for assessment of incidence of EVIFs. CT morphology was assessed to assign EVIFs to one of three categories of clinical relevance: (I) immediate, (II) potential, and (III) no clinical relevance.

RESULTS

Thirty-eight patients had category I findings, including four patients (2.8%) with previously unknown malignancy and 67 patients with category II EVIFs. In total 473 extravascular EVIFs were found in 141 patients: 52 category I, 163 category II and 258 category III.

CONCLUSION

EVIFs with immediate clinical relevance are very common in run-off CTA in patients presenting with acute peripheral artery disease. Therefore, it is important to evaluate all body regions included in a CT examination carefully, even if the clinical focus is on vascular evaluation. The adequate classification of these EVIFs is required to avoid possible unnecessary diagnostic work-up with associated risks and costs.

Clinical utility of quantitative imaging

Quantitative imaging (QI) is increasingly applied in modern radiology practice, assisting in the clinical assessment of many patients and providing a source of biomarkers for a spectrum of diseases. QI is commonly used to inform patient diagnosis or prognosis, determine the choice of therapy, or monitor therapy response. Because most radiologists will likely implement some QI tools to meet the patient care needs of their referring clinicians, it is important for all radiologists to become familiar with the strengths and limitations of QI. The Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force has explored the clinical application of QI and summarizes its work in this review. We provide an overview of the clinical use of QI by discussing QI tools that are currently used in clinical practice, clinical applications of these tools, approaches to reporting of QI, and challenges to implementing QI. It is hoped that these insights will help radiologists recognize the tangible benefits of QI to their patients, their referring clinicians, and their own radiology practice.