{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:46:06Z","timestamp":1790696766017,"version":"4.1.0"},"reference-count":61,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100008980","name":"Soci\u00e9t\u00e9 Fran\u00e7aise de Radiologie","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008980","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100022184","name":"Institut Servier","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100022184","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","em-consulte.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Diagnostic and Interventional Imaging"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1016\/j.diii.2022.09.003","type":"journal-article","created":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T02:22:55Z","timestamp":1663986175000},"page":"37-42","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":31,"title":["Artificial intelligence in adrenal imaging: A critical review of current applications"],"prefix":"10.1016","volume":"104","author":[{"given":"Maxime","family":"Barat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1539-2118","authenticated-orcid":false,"given":"Martin","family":"Gaillard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4805-4564","authenticated-orcid":false,"given":"Anne-S\u00e9gol\u00e8ne","family":"Cottereau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-1658","authenticated-orcid":false,"given":"Elliot K.","family":"Fishman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Assi\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Jouinot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2551-3545","authenticated-orcid":false,"given":"Christine","family":"Hoeffel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Soyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Dohan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.diii.2022.09.003_bib0001","doi-asserted-by":"crossref","first-page":"G1","DOI":"10.1530\/EJE-16-0467","article-title":"Management of adrenal incidentalomas: European society of endocrinology clinical practice guideline in collaboration with the european network for the study of adrenal tumors","volume":"175","author":"Fassnacht","year":"2016","journal-title":"Eur J Endocrinol"},{"key":"10.1016\/j.diii.2022.09.003_bib0002","doi-asserted-by":"crossref","DOI":"10.1001\/jamasurg.2022.3544","article-title":"American association of endocrine surgeons guidelines for adrenalectomy: executive summary","author":"Yip","year":"2022","journal-title":"JAMA Surg"},{"key":"10.1016\/j.diii.2022.09.003_bib0003","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/S2213-8587(20)30218-7","article-title":"Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study","volume":"8","author":"Bancos","year":"2020","journal-title":"Lancet Diabetes Endocrinol"},{"key":"10.1016\/j.diii.2022.09.003_bib0004","doi-asserted-by":"crossref","first-page":"569","DOI":"10.3390\/cancers14030569","article-title":"Adrenal mass characterization in the era of quantitative imaging: state of the art","volume":"14","author":"Barat","year":"2022","journal-title":"Cancers"},{"key":"10.1016\/j.diii.2022.09.003_bib0005","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1148\/radiol.2223010766","article-title":"Adrenal masses: characterization with combined unenhanced and delayed enhanced CT","volume":"222","author":"Caoili","year":"2002","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2022.09.003_bib0006","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.diii.2021.10.004","article-title":"Can we use radiomics in ultrasound imaging? Impact of preprocessing on feature repeatability","volume":"102","author":"Duron","year":"2021","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0007","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.diii.2022.09.003_bib0008","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1148\/rg.2017170056","article-title":"CT texture analysis: definitions, applications, biologic correlates, and challenges","volume":"37","author":"Lubner","year":"2017","journal-title":"Radiographics"},{"key":"10.1016\/j.diii.2022.09.003_bib0009","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1200\/JCO.2008.18.5678","article-title":"Gene expression profiling reveals a new classification of adrenocortical tumors and identifies molecular predictors of malignancy and survival","volume":"27","author":"de Reynies","year":"2009","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.diii.2022.09.003_bib0010","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.7150\/jca.26356","article-title":"Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma","volume":"9","author":"Yi","year":"2018","journal-title":"J Cancer"},{"key":"10.1016\/j.diii.2022.09.003_bib0011","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1002\/jmri.25954","article-title":"Characterization of adrenal lesions on unenhanced MRI using texture analysis: a machine-learning approach","volume":"48","author":"Romeo","year":"2018","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0012","doi-asserted-by":"crossref","first-page":"554","DOI":"10.2214\/AJR.18.20097","article-title":"Can texture analysis be used to distinguish benign from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT, or in-phase and opposed-phase MRI?","volume":"212","author":"Ho","year":"2019","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.diii.2022.09.003_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.crad.2020.01.012","article-title":"Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma","volume":"75","author":"Ahmed","year":"2020","journal-title":"Clin Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0014","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.diii.2020.10.001","article-title":"A primer for understanding radiology articles about machine learning and deep learning","volume":"101","author":"Nakaura","year":"2020","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0015","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11604-018-0804-6","article-title":"Recent technical development of artificial intelligence for diagnostic medical imaging","volume":"37","author":"Nakata","year":"2019","journal-title":"Jpn J Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106874","article-title":"Medical deep learning-a systematic meta-review","volume":"221","author":"Egger","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.diii.2022.09.003_bib0017","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1148\/radiol.211597","article-title":"Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies","volume":"304","author":"Moskowitz","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2022.09.003_bib0018","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1148\/radiol.2019192515","article-title":"Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board","volume":"294","author":"Bluemke","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2022.09.003_bib0019","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s10278-012-9554-7","article-title":"Adrenal gland abnormality detection using random forest classification","volume":"26","author":"Saiprasad","year":"2013","journal-title":"J Digit Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0020","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.jss.2021.03.060","article-title":"Automatic detection of thyroid and adrenal incidentals using radiology reports and deep learning","volume":"266","author":"Canton","year":"2021","journal-title":"J Surg Res"},{"key":"10.1016\/j.diii.2022.09.003_bib0021","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2022.826402","article-title":"Developing a cancer digital twin: supervised metastases detection from consecutive structured radiology reports","volume":"5","author":"Batch","year":"2022","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.diii.2022.09.003_bib0022","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1055\/s-0040-1715892","article-title":"A web application for adrenal incidentaloma identification, tracking, and management using machine learning","volume":"11","author":"Bala","year":"2020","journal-title":"Appl Clin Inform"},{"key":"10.1016\/j.diii.2022.09.003_bib0023","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1007\/s11604-021-01098-5","article-title":"Artificial intelligence: a critical review of current applications in pancreatic imaging","volume":"39","author":"Barat","year":"2021","journal-title":"Jpn J Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0024","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.diii.2019.09.002","article-title":"Deep lessons learned: radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis","volume":"101","author":"Weisberg","year":"2020","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0025","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/j.diii.2020.03.006","article-title":"Three artificial intelligence data challenges based on CT and MRI","volume":"101","author":"Lassau","year":"2020","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0026","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cmpb.2018.07.009","article-title":"Adrenal tumor segmentation method for MR images","volume":"164","author":"Barstugan","year":"2018","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.diii.2022.09.003_bib0027","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2018.01.032","article-title":"A novel pipeline for adrenal tumour segmentation","volume":"159","author":"Koyuncu","year":"2018","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.diii.2022.09.003_bib0028","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.cmpb.2019.07.002","article-title":"Automated pancreas segmentation from computed tomography and magnetic resonance images: a systematic review","volume":"178","author":"Kumar","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.diii.2022.09.003_bib0029","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.neuroimage.2019.02.016","article-title":"Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility","volume":"191","author":"Li","year":"2019","journal-title":"Neuroimage"},{"key":"10.1016\/j.diii.2022.09.003_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104749","article-title":"An optimized two-stage cascaded deep neural network for adrenal segmentation on CT images","volume":"136","author":"Luo","year":"2021","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.diii.2022.09.003_bib0031","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.diii.2021.08.002","article-title":"Abdominal metastases from Merkel cell carcinoma: prevalence and presentation on CT examination in 111 patients","volume":"103","author":"Girard","year":"2022","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0032","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1007\/s00261-022-03409-4","article-title":"Differentiation between heterogeneous adrenal adenoma and non-adenoma adrenal lesion with CT and MRI","volume":"47","author":"Lanoix","year":"2022","journal-title":"Abdom Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0033","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s11604-021-01199-1","article-title":"Adrenal pheochromocytoma: is it all or the tip of the iceberg?","volume":"40","author":"Wang","year":"2022","journal-title":"Jpn J Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0034","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1148\/radiology.207.2.9577483","article-title":"Adrenal adenomas and nonadenomas: assessment of washout at delayed contrast-enhanced CT","volume":"207","author":"Szolar","year":"1998","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2022.09.003_bib0035","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s12020-017-1473-4","article-title":"Adrenal myelolipoma: a comprehensive review","volume":"59","author":"Decmann","year":"2018","journal-title":"Endocrine"},{"key":"10.1016\/j.diii.2022.09.003_bib0036","doi-asserted-by":"crossref","first-page":"390","DOI":"10.2214\/AJR.19.21851","article-title":"Macroscopic fat in adrenocortical carcinoma: a systematic review","volume":"214","author":"Ranathunga","year":"2020","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.diii.2022.09.003_bib0037","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1148\/rg.2020190080","article-title":"Technical and interpretive pitfalls in adrenal imaging","volume":"40","author":"Nandra","year":"2020","journal-title":"Radiographics"},{"key":"10.1016\/j.diii.2022.09.003_bib0038","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.diii.2022.02.006","article-title":"Right-sided adrenocortical carcinoma with inferior vena cava tumor thrombus","volume":"103","author":"Gaillard","year":"2022","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0039","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1097\/01.rct.0000250105.93423.a2","article-title":"Computed tomographic histogram analysis in the diagnosis of lipid-poor adenomas: comparison to adrenal washout computed tomography","volume":"31","author":"Jhaveri","year":"2007","journal-title":"J Comput Assist Tomogr"},{"key":"10.1016\/j.diii.2022.09.003_bib0040","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.2214\/AJR.17.19276","article-title":"Can adrenal adenomas be differentiated from adrenal metastases at single-phase contrast-enhanced CT?","volume":"211","author":"Tu","year":"2018","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.diii.2022.09.003_bib0041","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1002\/jmri.25452","article-title":"ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma","volume":"45","author":"Umanodan","year":"2017","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0042","doi-asserted-by":"crossref","first-page":"2882","DOI":"10.1007\/s00261-017-1207-3","article-title":"Texture analysis of FDG PET\/CT for differentiating between FDG-avid benign and metastatic adrenal tumors: efficacy of combining SUV and texture parameters","volume":"42","author":"Nakajo","year":"2017","journal-title":"Abdom Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0043","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s12967-022-03233-w","article-title":"Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation","volume":"20","author":"Kong","year":"2022","journal-title":"J Transl Med"},{"key":"10.1016\/j.diii.2022.09.003_bib0044","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.3390\/cancers12092424","article-title":"Usefulness of FDG-PET\/CT-based radiomics for the characterization and genetic orientation of pheochromocytomas before surgery","volume":"12","author":"Ansquer","year":"2020","journal-title":"Cancers"},{"key":"10.1016\/j.diii.2022.09.003_bib0045","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s12020-016-0970-1","article-title":"Assessment of tumor heterogeneity in treatment-naive adrenocortical cancer patients using (18)F-FDG positron emission tomography","volume":"53","author":"Werner","year":"2016","journal-title":"Endocrine"},{"key":"10.1016\/j.diii.2022.09.003_bib0046","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20211066","article-title":"A deep learning-based approach for the diagnosis of adrenal adenoma: a new trial using CT","volume":"95","author":"Kusunoki","year":"2022","journal-title":"Br J Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.crad.2019.06.021","article-title":"Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT","volume":"74","author":"Elmohr","year":"2019","journal-title":"Clin Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0048","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.1007\/s00261-021-03136-2","article-title":"Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans","volume":"46","author":"Moawad","year":"2021","journal-title":"Abdom Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0049","article-title":"Computed tomography-based machine learning differentiates adrenal pheochromocytoma from lipid-poor adenoma","volume":"13","author":"Liu","year":"2022","journal-title":"Front Endocrinol"},{"key":"10.1016\/j.diii.2022.09.003_bib0050","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1177\/0846537120911736","article-title":"Combined diagnosis of whole-lesion histogram analysis of T1- and T2-weighted imaging for differentiating adrenal adenoma and pheochromocytoma: a support vector machine-based study","volume":"72","author":"Liu","year":"2021","journal-title":"Can Assoc Radiol J"},{"key":"10.1016\/j.diii.2022.09.003_bib0051","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/S0140-6736(05)67139-5","volume":"366","author":"Lenders","year":"2005","journal-title":"Lancet"},{"key":"10.1016\/j.diii.2022.09.003_bib0052","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.mri.2021.03.009","article-title":"Handcrafted MRI radiomics and machine learning: classification of indeterminate solid adrenal lesions","volume":"79","author":"Stanzione","year":"2021","journal-title":"Magn Reson Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0053","doi-asserted-by":"crossref","first-page":"dgaa379","DOI":"10.1210\/clinem\/dgaa379","article-title":"Development and validation of prediction models for subtype diagnosis of patients with primary aldosteronism","volume":"105","author":"Burrello","year":"2020","journal-title":"J Clin Endocrinol Metab"},{"key":"10.1016\/j.diii.2022.09.003_bib0054","doi-asserted-by":"crossref","first-page":"8892","DOI":"10.1038\/s41598-022-12835-9","article-title":"Radiomics utilization to differentiate nonfunctional adenoma in essential hypertension and functional adenoma in primary aldosteronism","volume":"12","author":"Chen","year":"2022","journal-title":"Sci Rep"},{"key":"10.1016\/j.diii.2022.09.003_bib0055","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1007\/s00270-019-02336-0","article-title":"CT texture analysis and machine learning improve post-ablation prognostication in patients with adrenal metastases: a proof of concept","volume":"42","author":"Daye","year":"2019","journal-title":"Cardiovasc Intervent Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0056","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1148\/radiol.2015142029","article-title":"Unresectable adrenal metastases: clinical outcomes of radiofrequency ablation","volume":"277","author":"Hasegawa","year":"2015","journal-title":"Radiology"},{"key":"10.1016\/j.diii.2022.09.003_bib0057","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.jvir.2017.07.026","article-title":"Local control and survival after image-guided percutaneous ablation of adrenal metastases","volume":"29","author":"Frenk","year":"2018","journal-title":"J Vasc Interv Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0058","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/2641627","article-title":"Prognostic value of functional parameters of (18)F-FDG-PET images in patients with primary renal\/adrenal lymphoma","volume":"2019","author":"Wang","year":"2019","journal-title":"Contrast Media Mol Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0059","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.diii.2020.10.007","article-title":"Artificial intelligence: from challenges to clinical implementation","volume":"101","author":"Chassagnon","year":"2020","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.diii.2022.09.003_bib0060","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.jacr.2017.05.001","article-title":"Management of incidental adrenal masses: a white paper of the ACR incidental findings committee","volume":"14","author":"Mayo-Smith","year":"2017","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.diii.2022.09.003_bib0061","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1097\/JU.0000000000001342","article-title":"Discrepancies in the recommended management of adrenal incidentalomas by various guidelines","volume":"205","author":"Maas","year":"2021","journal-title":"J Urol"}],"container-title":["Diagnostic and Interventional Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2211568422001693?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2211568422001693?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:37:49Z","timestamp":1761932269000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2211568422001693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["S2211568422001693"],"URL":"https:\/\/doi.org\/10.1016\/j.diii.2022.09.003","relation":{},"ISSN":["2211-5684"],"issn-type":[{"value":"2211-5684","type":"print"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in adrenal imaging: A critical review of current applications","name":"articletitle","label":"Article Title"},{"value":"Diagnostic and Interventional Imaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.diii.2022.09.003","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Published by Elsevier Masson SAS on behalf of Soci\u00e9t\u00e9 fran\u00e7aise de radiologie.","name":"copyright","label":"Copyright"}]}}