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Prevalence of Malignancy in Adrenal Nodules With Heterogeneous Microscopic Fat on Chemical-Shift MRI: A Multiinstitutional Study. AJR Am J Roentgenol 2023; 220:86-94. [PMID: 35920707 DOI: 10.2214/ajr.22.27976] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND. Homogeneous microscopic fat within adrenal nodules on chemical-shift MRI (CS-MRI) is diagnostic of benign adrenal adenoma, but the clinical relevance of heterogeneous microscopic fat is not well established. OBJECTIVE. This study sought to determine the prevalence of malignancy in adrenal nodules with heterogeneous microscopic fat on dual-echo T1-weighted CS-MRI. METHODS. We performed a retrospective study of adult patients with adrenal nodules detected on MRI performed between August 2007 and November 2020 at seven institutions. Eligible nodules had a short-axis diameter of 10 mm or larger with heterogeneous microscopic fat (defined by an area of signal loss of < 80% on opposed-phase CS-MRI). Two radiologists from each center, blinded to reference standard results, determined the signal loss pattern (diffuse, two distinct parts, speckling pattern, central loss, or peripheral loss) within the nodules. The reference standard used was available for 283 nodules (pathology for 21 nodules, ≥ 1 year of imaging follow-up for 245, and ≥ 5 years of clinical follow-up for 17) in 282 patients (171 women and 111 men; mean age, 60 ± 12 [SD] years); 30% (86/282) patients had prior malignancy. RESULTS. The mean long-axis diameter was 18.7 ± 7.9 mm (range, 10-80 mm). No malignant nodules were found in patients without prior cancer (0/197; 95% CI, 0-1.5%). Four of the 86 patients with prior malignancy (hepatocellular carcinoma [HCC], renal cell carcinoma [RCC], lung cancer, or both colon cancer and RCC) (4.7%; 95% CI, 1.3-11.5%) had metastatic nodules. Detected patterns were diffuse heterogeneous signal loss (40% [114/283]), speckling (28% [80/283]), two distinct parts (18% [51/283]), central loss (9% [26/283]), and peripheral loss (4% [12/283]). Two metastases from HCC and RCC showed diffuse heterogeneous signal loss. Lung cancer metastasis manifested as two distinct parts, and the metastasis in the patient with both colon cancer and RCC showed peripheral signal loss. CONCLUSION. Presence of heterogeneous microscopic fat in adrenal nodules on CS-MRI indicates a high likelihood of benignancy, particularly in patients without prior cancer. This finding is also commonly benign in patients with cancer; however, caution is warranted when primary malignancies may contain fat or if the morphologic pattern of signal loss may indicate a collision tumor. CLINICAL IMPACT. In the absence of prior cancer, adrenal nodules with heterogeneous microscopic fat do not require additional imaging evaluation.
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Barat M, Gaillard M, Cottereau AS, Fishman EK, Assié G, Jouinot A, Hoeffel C, Soyer P, Dohan A. Artificial intelligence in adrenal imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:37-42. [PMID: 36163169 DOI: 10.1016/j.diii.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 09/14/2022] [Indexed: 01/10/2023]
Abstract
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.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France.
| | - Martin Gaillard
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Digestive, Hepatobiliary and Pancreatic Surgery, Hôpital Cochin, AP-HP, Paris 75014, France
| | - Anne-Ségolène Cottereau
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Nuclear Medicine, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Guillaume Assié
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Endocrinology, Center for Rare Adrenal Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | - Anne Jouinot
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Endocrinology, Center for Rare Adrenal Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | | | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France
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