Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art
Abstract
:Simple Summary
Abstract
1. Introduction
2. Genetic Syndromes
3. Normal Adrenal Glands on Imaging
4. Adrenal Hyperplasia
5. Other Non-Tumoral Adrenal Gland Abnormalities
6. Typical Incidental Adrenal Lesions on Imaging
7. Hyperfunctioning Adrenal Lesions
7.1. Cortisol-Secreting Adrenal Lesions
7.2. Metanephrine-Secreting Adrenal Lesions
7.3. Aldosterone-Secreting Adrenal Lesions
8. Hypofunctional States
9. Conventional Imaging to Differentiate between Benign and Malignant Adrenal Lesions
9.1. Computed Tomography
9.2. Magnetic Resonance Imaging
10. Nuclear Medicine and Radiotracers
10.1. 18F-FDG PET/CT
10.2. 131I-6β-Iodomethyl-19-norcholesterol (Noriodocholesterol)
10.3. Metonidate
10.4. 123-Iodine Labeled Meta-Iodobenzylguanidine (123I-MIBG)
10.5. 18F-FDOPA and 68Ga-DOTA-Somatostin Analogs (SSA)
10.6. Other Radiotracers That May Be Useful for Adrenal Functional Imaging
11. Adrenal Collision Tumors
12. Minimally Invasive Radiological Methods for the Characterization of Adrenal Lesions
12.1. Adrenal Vein Sampling
12.2. Percutaneous Biopsy
13. New Technologies for Adrenal Lesion Characterization
13.1. Principles and Definitions
13.2. Current Results and Future Applications
13.3. AI for the Diagnosis of Benign Adrenal Lesions
13.4. AI for the Diagnosis of Adrenal Malignancy
13.5. Future Applications
14. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
3D | Three-dimensional |
AA | Adrenal adenoma |
ACC | Adrenocortical carcinoma |
ACTH | Adrenocorticotrophic hormone |
ADC | Apparent diffusion coefficient |
AH | Adrenal hyperplasia |
AI | Artificial intelligence |
APW | Absolute percentage of washout |
AUC | Area under the curve |
AUROC | Area under the receiving operator characteristic curve |
CT | Computed tomography |
DL | Deep learning |
DWI | Diffusion-weighted imaging |
FDG | Fluorodeoxyglucose |
HU | Hounsfield unit |
IP | In-phase |
MIBG | Metaiodobenzylguanidine |
ML | Machine learning |
MRI | Magnetic resonance imaging |
MRS | Magnetic resonance spectroscopy |
NPV | Negative predictive value |
OP | Out-of-phase |
PET | Positron emission tomography |
PPV | Positive predictive value |
ROC | Receiving operator characteristics |
RPW | Relative percentage of washout |
SDH | Succinate dehydrogenase |
SPECT | Single-photon emission computed tomography |
SSA | Somatostatin analog |
SUV | Standardized uptake value |
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Non-Secreting Disease (60%) | Secreting Syndrome (40%) | ||
---|---|---|---|
Adrenal disease | Systemic disease | ACTH-dependent (85%) | ACTH-independent (15%) |
Hematoma | Metastasis | Pituitary adenoma | Macronodular hyperplasia |
Infection | Infiltrative disease | Ectopic secretion | Primary pigmented nodular adrenocortical disease |
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Barat, M.; Cottereau, A.-S.; Gaujoux, S.; Tenenbaum, F.; Sibony, M.; Bertherat, J.; Libé, R.; Gaillard, M.; Jouinot, A.; Assié, G.; et al. Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art. Cancers 2022, 14, 569. https://doi.org/10.3390/cancers14030569
Barat M, Cottereau A-S, Gaujoux S, Tenenbaum F, Sibony M, Bertherat J, Libé R, Gaillard M, Jouinot A, Assié G, et al. Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art. Cancers. 2022; 14(3):569. https://doi.org/10.3390/cancers14030569
Chicago/Turabian StyleBarat, Maxime, Anne-Ségolène Cottereau, Sébastien Gaujoux, Florence Tenenbaum, Mathilde Sibony, Jérôme Bertherat, Rossella Libé, Martin Gaillard, Anne Jouinot, Guillaume Assié, and et al. 2022. "Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art" Cancers 14, no. 3: 569. https://doi.org/10.3390/cancers14030569
APA StyleBarat, M., Cottereau, A.-S., Gaujoux, S., Tenenbaum, F., Sibony, M., Bertherat, J., Libé, R., Gaillard, M., Jouinot, A., Assié, G., Hoeffel, C., Soyer, P., & Dohan, A. (2022). Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art. Cancers, 14(3), 569. https://doi.org/10.3390/cancers14030569