{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T18:18:47Z","timestamp":1790878727968,"version":"4.1.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-22-CE17-0019-01"],"award-info":[{"award-number":["ANR-22-CE17-0019-01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-10-IAHU-02"],"award-info":[{"award-number":["ANR-10-IAHU-02"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005743","name":"Universit\u00e0 Cattolica del Sacro Cuore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005743","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Updates Surg"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Adrenal abnormalities are common imaging findings requiring comprehensive workup. Invasive diagnostic and therapeutic procedures, including adrenal surgery, demand expertise to mitigate risks of severe complications. Recent applications of 3D image analyses range from enhanced diagnosis to therapeutic guidance with anatomical visualisation. This study assesses the efficacy and clinical utility of 3D image analyses in managing adrenal disease. This systematic review, registered with PROSPERO (CRD42024500783), adheres to PRISMA guidelines. Searches of PubMed, Google Scholar and ClinicalTrials.gov up to August 2024 identified eligible studies. Extracted data included imaging modalities, 3D applications for diagnosis or treatment, volumetric segmentation techniques, and differentiation between pathologic and normal adrenal glands. Technological maturity was assessed with the IDEAL framework. Of 1813 studies screened, 30 met inclusion criteria, encompassing 5304 imaging exams (2060F\/2106M, and 10 studies with 1115 cases), predominantly CT (27 CT, 90%; 2 MRI, 6.7%; 1 CT\/MRI 3.3%). Volumetric reconstructions employed manual, semiautomatic and automatic methods for digital visualisation, and subsequent 3D printing. 3D analyses of normal vs. neoplastic adrenal tissue supported diagnosis in 18 studies (60%), and treatment in 12 (40%), improving understanding of respective anatomical features and planning for operative and interventional procedures. These applications represented early IDEAL stages of surgical innovation (0\u2014preclinical, 1\u2014idea and 2\u2014development\/exploration). 3D image analyses show promise in optimising adrenal disease management by aiding adrenal lesion characterisation and treatment optimisation through pre- and intraprocedural guidance. Further clinical assessment and long-term studies are warranted to establish the broader clinical impact.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s13304-025-02386-9","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:43:04Z","timestamp":1760028184000},"page":"365-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Three-dimensional image guidance for diagnosis and treatment of adrenal disease: a systematic review"],"prefix":"10.1007","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0321-7980","authenticated-orcid":false,"given":"Sofia","family":"Di Lorenzo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2958-9616","authenticated-orcid":false,"given":"Farahdiba","family":"Zarin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9791-9464","authenticated-orcid":false,"given":"Matteo","family":"Pavone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7559-3328","authenticated-orcid":false,"given":"Didier","family":"Mutter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1259-2491","authenticated-orcid":false,"given":"Marco","family":"Raffaelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9303-2364","authenticated-orcid":false,"given":"Michel","family":"Vix","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7451-7545","authenticated-orcid":false,"given":"Barbara","family":"Seeliger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"issue":"2","key":"2386_CR1","doi-asserted-by":"publisher","first-page":"R51","DOI":"10.1530\/EJE-16-0461","volume":"175","author":"J Dinnes","year":"2016","unstructured":"Dinnes J, Bancos I, Ferrante di Ruffano L, Chortis V, Davenport C, Bayliss S, Arlt W (2016) MANAGEMENT OF ENDOCRINE DISEASE: Imaging for the diagnosis of malignancy in incidentally discovered adrenal masses: a systematic review and meta-analysis. Eur J Endocrinol. 175(2):R51-64","journal-title":"Eur J Endocrinol."},{"issue":"2","key":"2386_CR2","doi-asserted-by":"publisher","first-page":"G1","DOI":"10.1530\/EJE-16-0467","volume":"175","author":"M Fassnacht","year":"2016","unstructured":"Fassnacht M, Arlt W, Bancos I, Dralle H, Newell-Price J, Sahdev A, Tabarin A, Terzolo M, Tsagarakis S, Dekkers OM (2016) Management of adrenal incidentalomas: European Society of Endocrinology Clinical Practice Guideline in collaboration with the European Network for the Study of Adrenal Tumors. Eur J Endocrinol 175(2):G1\u2013G34","journal-title":"Eur J Endocrinol"},{"key":"2386_CR3","doi-asserted-by":"publisher","first-page":"1023220","DOI":"10.3389\/fendo.2022.1023220","volume":"13","author":"A Qdaisat","year":"2022","unstructured":"Qdaisat A, Bedrose S, Ezzeldin O, Moawad AW, Yeung SJ, Elsayes KM, Habra MA (2022) The prevalence and spectrum of reported incidental adrenal abnormalities in abdominal computed tomography of cancer patients: the experience of a comprehensive cancer center. Front Endocrinol (Lausanne) 13:1023220","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"2386_CR4","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1102\/1470-7330.2010.0012","volume":"10","author":"A Sahdev","year":"2010","unstructured":"Sahdev A, Willatt J, Francis IR, Reznek RH (2010) The indeterminate adrenal lesion. Cancer Imaging 10(1):102\u2013113","journal-title":"Cancer Imaging"},{"issue":"1","key":"2386_CR5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.rcl.2008.01.003","volume":"46","author":"MA Blake","year":"2008","unstructured":"Blake MA, Holalkere NS, Boland GW (2008) Imaging techniques for adrenal lesion characterization. Radiol Clin North Am 46(1):65\u201378","journal-title":"Radiol Clin North Am"},{"issue":"8","key":"2386_CR6","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.jacr.2017.05.001","volume":"14","author":"WW Mayo-Smith","year":"2017","unstructured":"Mayo-Smith WW, Song JH, Boland GL, Francis IR, Israel GM, Mazzaglia PJ, Berland LL, Pandharipande PV (2017) Management of incidental adrenal masses: a white paper of the ACR incidental findings committee. J Am Coll Radiol 14(8):1038\u20131044","journal-title":"J Am Coll Radiol"},{"issue":"22","key":"2386_CR7","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1056\/NEJM198906013202211","volume":"320","author":"JP Kassirer","year":"1989","unstructured":"Kassirer JP (1989) Our stubborn quest for diagnostic certainty a cause of excessive testing. N Engl J Med. 320(22):1489\u201391","journal-title":"N Engl J Med."},{"issue":"11","key":"2386_CR8","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1016\/j.acra.2014.06.008","volume":"21","author":"J Schneller","year":"2014","unstructured":"Schneller J, Reiser M, Beuschlein F, Osswald A, Pallauf A, Riester A, Tietze JK, Reincke M, Degenhart C (2014) Linear and volumetric evaluation of the adrenal gland\u2013MDCT-based measurements of the adrenals. Acad Radiol 21(11):1465\u20131474","journal-title":"Acad Radiol"},{"issue":"3","key":"2386_CR9","doi-asserted-by":"publisher","first-page":"554","DOI":"10.2214\/AJR.15.14467","volume":"205","author":"YR Shin","year":"2015","unstructured":"Shin YR, Kim KA (2015) Imaging features of various adrenal neoplastic lesions on radiologic and nuclear medicine imaging. AJR Am J Roentgenol 205(3):554\u2013563","journal-title":"AJR Am J Roentgenol"},{"issue":"4","key":"2386_CR10","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1530\/EJE-09-0234","volume":"161","author":"TJ Cawood","year":"2009","unstructured":"Cawood TJ, Hunt PJ, O\u2019Shea D, Cole D, Soule S (2009) Recommended evaluation of adrenal incidentalomas is costly, has high false-positive rates and confers a risk of fatal cancer that is similar to the risk of the adrenal lesion becoming malignant; time for a rethink? Eur J Endocrinol 161(4):513\u2013527","journal-title":"Eur J Endocrinol"},{"issue":"1","key":"2386_CR11","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/s12672-022-00577-z","volume":"13","author":"C Li","year":"2022","unstructured":"Li C, Fu Y, Yi X, Guan X, Liu L, Chen BT (2022) Application of radiomics in adrenal incidentaloma: a literature review. Discov Oncol 13(1):112","journal-title":"Discov Oncol"},{"key":"2386_CR12","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-031-00363-9_8","volume-title":"Robotic urologic surgery","author":"B Seeliger","year":"2022","unstructured":"Seeliger B, Collins JW, Porpiglia F, Marescaux J (2022) The role of virtual reality, telesurgery, and teleproctoring in robotic surgery. Robotic urologic surgery. Springer, pp 61\u201377"},{"key":"2386_CR13","first-page":"65","volume-title":"Mastery of robotic surgery","author":"B Seeliger","year":"2024","unstructured":"Seeliger B, Collins T, Marescaux J (2024) Implemented imaging and artificial intelligence. In: Giulianotti PC, Benedetti E (eds) Mastery of robotic surgery, Chap 6. McGraw Hill, New York, pp 65\u201387"},{"key":"2386_CR14","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/B978-0-12-816176-0.00023-5","volume-title":"Handbook of medical image computing and computer assisted intervention","author":"MP Starmans","year":"2020","unstructured":"Starmans MP, van der Voort SR, Castillo Tovar JM, Veenland JF, Klein S, Niessen WJ (2020) Radiomics: data mining using quantitative medical image features. Handbook of medical image computing and computer assisted intervention. Elsevier, pp 429\u2013456"},{"issue":"1","key":"2386_CR15","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10278-024-01158-y","volume":"38","author":"Y Li","year":"2025","unstructured":"Li Y, Zhao Y, Yang P, Li C, Liu L, Zhao X, Mao Y (2025) Adrenal volume quantitative visualization tool by multiple parameters and an nnU-net deep learning automatic segmentation model. J Imaging Inform Med 38(1):47\u201359","journal-title":"J Imaging Inform Med"},{"key":"2386_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_47","volume-title":"Semi-automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks","author":"Y Tang","year":"2018","unstructured":"Tang Y, Harrison AP, Bagheri M, Xiao J, Summers RM (2018) Semi-automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks. Springer International Publishing, Cham"},{"issue":"2","key":"2386_CR17","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563\u2013577","journal-title":"Radiology"},{"issue":"18","key":"2386_CR18","first-page":"2214","volume":"292","author":"J Marescaux","year":"2004","unstructured":"Marescaux J, Rubino F, Arenas M, Mutter D, Soler L (2004) Augmented-reality-assisted laparoscopic adrenalectomy. JAMA 292(18):2214\u20132215","journal-title":"JAMA"},{"issue":"7","key":"2386_CR19","doi-asserted-by":"publisher","first-page":"e1000097","DOI":"10.1371\/journal.pmed.1000097","volume":"6","author":"D Moher","year":"2009","unstructured":"Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097","journal-title":"PLoS Med"},{"key":"2386_CR20","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1472-6947-7-16","volume":"7","author":"C Schardt","year":"2007","unstructured":"Schardt C, Adams MB, Owens T, Keitz S, Fontelo P (2007) Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak 7:16","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"2386_CR21","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","volume":"5","author":"M Ouzzani","year":"2016","unstructured":"Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan-a web and mobile app for systematic reviews. Syst Rev 5(1):210","journal-title":"Syst Rev"},{"issue":"9695","key":"2386_CR22","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1016\/S0140-6736(09)61116-8","volume":"374","author":"P McCulloch","year":"2009","unstructured":"McCulloch P, Altman DG, Campbell WB, Flum DR, Glasziou P, Marshall JC, Nicholl J, Aronson JK, Barkun JS, Blazeby JM, Boutron IC, Clavien PA, Cook JA, Ergina PL, Feldman LS, Maddern GJ, Reeves BC, Seiler CM, Strasberg SM, Meakins JL, Ashby D, Black N, Bunker J, Burton M, Campbell M, Chalkidou K, Chalmers I, de Leval M, Deeks J, Grant A, Gray M, Greenhalgh R, Jenicek M, Kehoe S, Lilford R, Littlejohns P, Loke Y, Madhock R, McPherson K, Meakins J, Rothwell P, Summerskill B, Taggart D, Tekkis P, Thompson M, Treasure T, Trohler U, Vandenbroucke J (2009) No surgical innovation without evaluation: the IDEAL recommendations. Lancet 374(9695):1105\u20131112","journal-title":"Lancet"},{"issue":"1","key":"2386_CR23","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1097\/SLA.0000000000004907","volume":"275","author":"HJ Marcus","year":"2022","unstructured":"Marcus HJ, Bennett A, Chari A, Day T, Hirst A, Hughes-Hallett A, Kolias A, Kwasnicki RM, Martin J, Rovers M, Squire SE, McCulloch P (2022) IDEAL-D framework for device innovation: a consensus statement on the preclinical stage. Ann Surg 275(1):73\u201379","journal-title":"Ann Surg"},{"issue":"8","key":"2386_CR24","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Rutjes AWS, Leeflang MMG, Sterne JAC, Bossuyt PMM, Q.-. Group (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529\u2013536","journal-title":"Ann Intern Med"},{"key":"2386_CR25","unstructured":"OCEBM Levels of Evidence Working Group* (2011) The Oxford Levels of Evidence 2. https:\/\/www.cebm.ox.ac.uk\/resources\/levels-of-evidence\/ocebm-levels-of-evidence. Accessed 7 Aug 2024"},{"issue":"12","key":"2386_CR26","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749\u2013762","journal-title":"Nat Rev Clin Oncol"},{"issue":"6","key":"2386_CR27","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1097\/SLE.0000000000001105","volume":"32","author":"G Palomba","year":"2022","unstructured":"Palomba G, Dinuzzi VP, Pegoraro F, Troisi RI, Montalti R, De Palma GD, Aprea G (2022) The role of intraoperative indocyanine green (ICG) and preoperative 3-dimensional (3D) reconstruction in laparoscopic adrenalectomy: a propensity score-matched analysis. Surg Laparosc Endosc Percutan Tech 32(6):643\u2013649","journal-title":"Surg Laparosc Endosc Percutan Tech"},{"key":"2386_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.1265790","volume":"14","author":"S Sun","year":"2023","unstructured":"Sun S, Yao W, Wang Y, Yue P, Guo F, Deng X, Zhang Y (2023) Development and validation of machine-learning models for the difficulty of retroperitoneal laparoscopic adrenalectomy based on radiomics. Front Endocrinol (Lausanne) 14:1265790","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"2386_CR29","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1080\/02656736.2022.2125590","volume":"39","author":"J Sebek","year":"2022","unstructured":"Sebek J, Cappiello G, Rahmani G, Zeinali N, Keating M, Fayemiwo M, Harkin J, McDaid L, Gardiner B, Sheppard D, Senanayake R, Gurnell M, O\u2019Halloran M, Dennedy MC, Prakash P (2022) Image-based computer modeling assessment of microwave ablation for treatment of adrenal tumors. Int J Hyperthermia 39(1):1264\u20131275","journal-title":"Int J Hyperthermia"},{"key":"2386_CR30","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.mri.2021.03.009","volume":"79","author":"A Stanzione","year":"2021","unstructured":"Stanzione A, Cuocolo R, Verde F, Galatola R, Romeo V, Mainenti PP, Aprea G, Guadagno E, Del Basso De Caro M, Maurea S (2021) Handcrafted MRI radiomics and machine learning: classification of indeterminate solid adrenal lesions. Magn Reson Imaging 79:52\u201358","journal-title":"Magn Reson Imaging"},{"issue":"1","key":"2386_CR31","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1002\/jmri.25954","volume":"48","author":"V Romeo","year":"2018","unstructured":"Romeo V, Maurea S, Cuocolo R, Petretta M, Mainenti PP, Verde F, Coppola M, Dell\u2019Aversana S, Brunetti A (2018) Characterization of adrenal lesions on unenhanced MRI using texture analysis: a machine-learning approach. J Magn Reson Imaging 48(1):198\u2013204","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"2386_CR32","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.4103\/jcrt.jcrt_2315_23","volume":"20","author":"Q Du","year":"2024","unstructured":"Du Q, Li X, Lin Z, Dong L, Liu F, Liang P (2024) Value of the three-dimensional visualization ablation planning system in ultrasound-guided percutaneous microwave ablation for malignant adrenal tumors: a clinical comparative study. J Cancer Res Ther 20(4):1232\u20131240","journal-title":"J Cancer Res Ther"},{"key":"2386_CR33","doi-asserted-by":"crossref","unstructured":"Tang S, Guo Y, Wang Y, Cao W, Sun F. Segmentation and 3D visualization of pheochromocytoma in contrast-enhanced CT images. In: 2014 International Conference on Audio, Language and Image Processing. 2014. IEEE.","DOI":"10.1109\/ICALIP.2014.7009753"},{"issue":"5","key":"2386_CR34","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10278-012-9554-7","volume":"26","author":"G Saiprasad","year":"2013","unstructured":"Saiprasad G, Chang CI, Safdar N, Saenz N, Siegel E (2013) Adrenal gland abnormality detection using random forest classification. J Digit Imaging 26(5):891\u2013897","journal-title":"J Digit Imaging"},{"issue":"5","key":"2386_CR35","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1111\/j.1464-410X.2006.06489.x","volume":"98","author":"M Mitterberger","year":"2006","unstructured":"Mitterberger M, Pinggera GM, Peschel R, Bartsch G, Pallwein L, Frauscher F (2006) The use of three-dimensional computed tomography for assessing patients before laparoscopic adrenal-sparing surgery. BJU Int 98(5):1068\u20131073","journal-title":"BJU Int"},{"issue":"7","key":"2386_CR36","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.4103\/jcrt.JCRT_39_19","volume":"15","author":"S Wu","year":"2019","unstructured":"Wu S, Li X, Yu J, Yu X, Cheng Z, Liu F, Liang P (2019) Ultrasound-guided percutaneous microwave ablation assisted by a three-dimensional visualization preoperative treatment planning system for larger adrenal metastasis (D >\/= 4 cm): Preliminary results. J Cancer Res Ther 15(7):1477\u20131483","journal-title":"J Cancer Res Ther"},{"issue":"12","key":"2386_CR37","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1016\/j.jpedsurg.2015.08.037","volume":"50","author":"R Souzaki","year":"2015","unstructured":"Souzaki R, Kinoshita Y, Ieiri S, Kawakubo N, Obata S, Jimbo T, Koga Y, Hashizume M, Taguchi T (2015) Preoperative surgical simulation of laparoscopic adrenalectomy for neuroblastoma using a three-dimensional printed model based on preoperative CT images. J Pediatr Surg 50(12):2112\u20132115","journal-title":"J Pediatr Surg"},{"issue":"8","key":"2386_CR38","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1007\/s00464-003-9190-x","volume":"18","author":"R Marvik","year":"2004","unstructured":"Marvik R, Lango T, Tangen GA, Andersen JO, Kaspersen JH, Ystgaard B, Nagelhus Hernes TA (2004) Laparoscopic navigation pointer for three-dimensional image-guided surgery. Surg Endosc 18(8):1242\u20138","journal-title":"Surg Endosc"},{"issue":"4","key":"2386_CR39","first-page":"1602","volume":"13","author":"Y Wang","year":"2023","unstructured":"Wang Y, Liu L, Pang Y, Chen D, Xu X, Liu J, Guan X (2023) Nomogram for predicting hemodynamic instability and the association between preoperative 3D printing and hemodynamic instability during laparoscopic adrenalectomy for pheochromocytoma. Am J Cancer Res 13(4):1602\u20131610","journal-title":"Am J Cancer Res"},{"key":"2386_CR40","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.urology.2022.04.033","volume":"165","author":"XW Yao","year":"2022","unstructured":"Yao XW, Ai K, Li Y (2022) Presurgical planning of 3D printing for a large abdominal pheochromocytoma and paraganglioma. Urology 165:356\u2013358","journal-title":"Urology"},{"issue":"3","key":"2386_CR41","doi-asserted-by":"publisher","first-page":"210","DOI":"10.24920\/003968","volume":"36","author":"LY Xu","year":"2021","unstructured":"Xu LY, Yan K, Lu L, Zhang WH, Chen X, Huo XF, Lu JJ (2021) External and internal validation of a computer assisted diagnostic model for detecting multi-organ mass lesions in CT images. Chin Med Sci J 36(3):210\u2013217","journal-title":"Chin Med Sci J"},{"issue":"8","key":"2386_CR42","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1089\/lap.2017.0704","volume":"28","author":"B Zhang","year":"2018","unstructured":"Zhang B, Chen Z, Hu X, He Y, Li M, Liu P, Du Y, Liu Y, Chen X (2018) Application of three-dimensional visualization technology in laparoscopic surgery for Pheochromocytoma\/Paraganglioma: a single-center experience. J Laparoendosc Adv Surg Tech A 28(8):997\u20131002","journal-title":"J Laparoendosc Adv Surg Tech A"},{"key":"2386_CR43","first-page":"1006","volume":"2019","author":"W Zhao","year":"2019","unstructured":"Zhao W, He H, Zhao J, Sun J (2019) Adrenal tumor vessels segmentation using convolutional neural network in computed tomography angiography. Annu Int Conf IEEE Eng Med Biol Soc 2019:1006\u20131009","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"2386_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104749","volume":"136","author":"G Luo","year":"2021","unstructured":"Luo G, Yang Q, Chen T, Zheng T, Xie W, Sun H (2021) An optimized two-stage cascaded deep neural network for adrenal segmentation on CT images. Comput Biol Med 136:104749","journal-title":"Comput Biol Med"},{"issue":"6","key":"2386_CR45","doi-asserted-by":"publisher","first-page":"4292","DOI":"10.1007\/s00330-022-09347-5","volume":"33","author":"TM Kim","year":"2023","unstructured":"Kim TM, Choi SJ, Ko JY, Kim S, Jeong CW, Cho JY, Kim YG (2023) Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia. Eur Radiol 33(6):4292\u20134302","journal-title":"Eur Radiol"},{"issue":"2","key":"2386_CR46","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220101","volume":"306","author":"C Robinson-Weiss","year":"2023","unstructured":"Robinson-Weiss C, Patel J, Bizzo BC, Glazer DI, Bridge CP, Andriole KP, Dabiri B, Chin JK, Dreyer K, Kalpathy-Cramer J, Mayo-Smith WW (2023) Machine learning for adrenal gland segmentation and classification of normal and adrenal masses at CT. Radiology 306(2):e220101","journal-title":"Radiology"},{"issue":"1","key":"2386_CR47","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s12938-023-01160-5","volume":"22","author":"L Wang","year":"2023","unstructured":"Wang L, Ye M, Lu Y, Qiu Q, Niu Z, Shi H, Wang J (2023) A combined encoder-transformer-decoder network for volumetric segmentation of adrenal tumors. Biomed Eng Online 22(1):106","journal-title":"Biomed Eng Online"},{"issue":"1","key":"2386_CR48","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-023-01106-2","volume":"23","author":"DX Xiao","year":"2023","unstructured":"Xiao DX, Zhong JP, Peng JD, Fan CG, Wang XC, Wen XL, Liao W-W, Wang J, Yin XF (2023) Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics. BMC Med Imaging 23(1):159","journal-title":"BMC Med Imaging"},{"issue":"10","key":"2386_CR49","doi-asserted-by":"publisher","first-page":"4853","DOI":"10.1007\/s00261-021-03136-2","volume":"46","author":"AW Moawad","year":"2021","unstructured":"Moawad AW, Ahmed A, Fuentes DT, Hazle JD, Habra MA, Elsayes KM (2021) Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans. Abdom Radiol 46(10):4853\u20134863","journal-title":"Abdom Radiol"},{"issue":"10","key":"2386_CR50","doi-asserted-by":"publisher","first-page":"3189","DOI":"10.1007\/s00261-023-03988-w","volume":"48","author":"Y Singh","year":"2023","unstructured":"Singh Y, Kelm ZS, Faghani S, Erickson D, Yalon T, Bancos I, Erickson BJ (2023) Deep learning approach for differentiating indeterminate adrenal masses using CT imaging. Abdom Radiol (NY) 48(10):3189\u20133194","journal-title":"Abdom Radiol (NY)"},{"issue":"9","key":"2386_CR51","first-page":"1018","volume":"19","author":"BAR Mendi","year":"2023","unstructured":"Mendi BAR, Gulbay M (2023) A CT radiomics analysis of the adrenal masses: can we discriminate lipid-poor adenomas from the pheochromocytoma and malignant masses? Curr Med Imaging 19(9):1018\u20131030","journal-title":"Curr Med Imaging"},{"issue":"10","key":"2386_CR52","doi-asserted-by":"publisher","first-page":"e711","DOI":"10.1016\/j.crad.2022.06.015","volume":"77","author":"A O'Shea","year":"2022","unstructured":"O\u2019Shea A, Kilcoyne A, McDermott E, O\u2019Grady M, McDermott S (2022) Can radiomic feature analysis differentiate adrenal metastases from lipid-poor adenomas on single-phase contrast-enhanced CT abdomen? Clin Radiol 77(10):e711\u2013e718","journal-title":"Clin Radiol"},{"issue":"5","key":"2386_CR53","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1016\/j.acra.2023.10.015","volume":"31","author":"PT Chen","year":"2024","unstructured":"Chen PT, Li PY, Liu KL, Wu VC, Lin YH, Chueh JS, Chen C-M, Chang C-C, T.S. Group (2024) Machine learning model with computed tomography radiomics and clinicobiochemical characteristics predict the subtypes of patients with primary aldosteronism. Acad Radiol 31(5):1818\u20131827","journal-title":"Acad Radiol"},{"issue":"10","key":"2386_CR54","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1007\/s40618-022-01826-2","volume":"45","author":"A De Leo","year":"2022","unstructured":"De Leo A, Vara G, Paccapelo A, Balacchi C, Vicennati V, Tucci L, Pagotto U, Selva S, Ricci C, Alberici L, Minni F, Nanni C, Ambrosi F, Santini D, Golfieri R, Di Dalmazi G, Mosconi C (2022) Computerized tomography texture analysis of pheochromocytoma: relationship with hormonal and histopathological data. J Endocrinol Invest 45(10):1935\u20131944","journal-title":"J Endocrinol Invest"},{"issue":"10","key":"2386_CR55","doi-asserted-by":"publisher","first-page":"818 e1","DOI":"10.1016\/j.crad.2019.06.021","volume":"74","author":"MM Elmohr","year":"2019","unstructured":"Elmohr MM, Fuentes D, Habra MA, Bhosale PR, Qayyum AA, Gates E, Elsayes KM (2019) Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Clin Radiol 74(10):818 e1-818 e7","journal-title":"Clin Radiol"},{"issue":"1","key":"2386_CR56","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s00330-019-06360-z","volume":"30","author":"JE Park","year":"2020","unstructured":"Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, Shin JH, Kim JH (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30(1):523\u2013536","journal-title":"Eur Radiol"},{"issue":"3","key":"2386_CR57","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.radonc.2018.03.033","volume":"127","author":"S Sanduleanu","year":"2018","unstructured":"Sanduleanu S, Woodruff HC, de Jong EEC, van Timmeren JE, Jochems A, Dubois L, Lambin P (2018) Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 127(3):349\u2013360","journal-title":"Radiother Oncol"},{"issue":"Suppl 1","key":"2386_CR58","doi-asserted-by":"publisher","first-page":"S41","DOI":"10.21037\/gs.2019.06.07","volume":"8","author":"M Raffaelli","year":"2019","unstructured":"Raffaelli M, De Crea C, Bellantone R (2019) Laparoscopic adrenalectomy. Gland Surg 8(Suppl 1):S41\u2013S52","journal-title":"Gland Surg"},{"issue":"4","key":"2386_CR59","doi-asserted-by":"publisher","first-page":"G1","DOI":"10.1530\/EJE-18-0608","volume":"179","author":"M Fassnacht","year":"2018","unstructured":"Fassnacht M, Dekkers OM, Else T, Baudin E, Berruti A, de Krijger R, Terzolo M (2018) European Society of Endocrinology Clinical Practice Guidelines on the management of adrenocortical carcinoma in adults, in collaboration with the European Network for the Study of Adrenal Tumors. Eur J Endocrinol 179(4):G1\u2013G46","journal-title":"Eur J Endocrinol"},{"issue":"1","key":"2386_CR60","doi-asserted-by":"publisher","first-page":"G1","DOI":"10.1093\/ejendo\/lvad066","volume":"189","author":"M Fassnacht","year":"2023","unstructured":"Fassnacht M, Tsagarakis S, Terzolo M, Tabarin A, Sahdev A, Newell-Price J, Pelsma I, Marina L, Lorenz K, Bancos I, Arlt W, Dekkers OM, European Society of Endocrinology (2023) European society of endocrinology clinical practice guidelines on the management of adrenal incidentalomas, in collaboration with the European Network for the Study of Adrenal Tumors. Eur J Endocrinol 189(1):G1\u2013G42","journal-title":"Eur J Endocrinol"},{"issue":"9","key":"2386_CR61","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1093\/bjs\/znab142","volume":"108","author":"WK Gray","year":"2021","unstructured":"Gray WK, Day J, Briggs TWR, Wass JAH, Lansdown M (2021) Volume-outcome relationship for adrenalectomy: analysis of an administrative dataset for the getting it right first time programme. Br J Surg 108(9):1112\u20131119","journal-title":"Br J Surg"},{"issue":"8","key":"2386_CR62","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1007\/s00423-019-01828-4","volume":"404","author":"O Gimm","year":"2019","unstructured":"Gimm O, Barczynski M, Mihai R, Raffaelli M (2019) Training in endocrine surgery. Langenbecks Arch Surg 404(8):929\u2013944","journal-title":"Langenbecks Arch Surg"},{"issue":"7","key":"2386_CR63","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/s00423-019-01827-5","volume":"404","author":"R Mihai","year":"2019","unstructured":"Mihai R, Donatini G, Vidal O, Brunaud L (2019) Volume-outcome correlation in adrenal surgery-an ESES consensus statement. Langenbecks Arch Surg 404(7):795\u2013806","journal-title":"Langenbecks Arch Surg"},{"key":"2386_CR64","doi-asserted-by":"publisher","DOI":"10.3390\/biology10070644","author":"G Laimer","year":"2021","unstructured":"Laimer G, Schullian P, Bale R (2021) Stereotactic thermal ablation of liver tumors: 3D planning, multiple needle approach, and intraprocedural image fusion are the key to success-a narrative review. Biology. https:\/\/doi.org\/10.3390\/biology10070644","journal-title":"Biology"}],"container-title":["Updates in Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13304-025-02386-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13304-025-02386-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13304-025-02386-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T21:05:02Z","timestamp":1771275902000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13304-025-02386-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,9]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2386"],"URL":"https:\/\/doi.org\/10.1007\/s13304-025-02386-9","relation":{},"ISSN":["2038-131X","2038-3312"],"issn-type":[{"value":"2038-131X","type":"print"},{"value":"2038-3312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,9]]},"assertion":[{"value":"16 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Barbara Seeliger declares that she is the recipient of a grant from the French National Agency for Research (Agence Nationale de la Recherche (ANR), 86 rue Regnault, 75013 Paris, France) within the framework of the project AI-DIAL\u2014Diagnostic Imaging of Adrenal Lesions (ANR-22-CE17-0019-01 and ANR-23-IACL-0004) and has a consultant agreement with Intuitive Surgical unrelated to the present study. Marco Raffaelli has a consultant agreement with Medtronic, AB Medica, and Intuitive Surgical. Sofia Di Lorenzo, Farahdiba Zarin, Matteo Pavone, Didier Mutter, and Michel Vix have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"An exemplary volumetric segmentation is illustrated (Fig.\u00a0\n                      \n                      ) with a representative case from a patient who underwent adrenalectomy at the University Hospitals of Strasbourg\/IHU Strasbourg. The retrospective clinical studies \u201cDiagnosis of Adrenal Lesions in Imaging (DALI)\u201d and \u201cArtificial Intelligence \u2013Diagnostic Imaging of Adrenal Lesions (AI-DIAL)\u201d were approved by the ethical committee (CE-2020-019, CE-2024-36), complied with the national regulations (MR004, #MR 4017210619) and institutional policies, and were performed in accordance with the tenets of the Helsinki Declaration. In accordance with French regulations, patients were informed individually of the purpose of the study, the data collected and the data processing. None of them made use of their right to object to the processing of their data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals, and informed consent"}}]}}