{"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":1790696766243,"version":"4.1.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T00:00:00Z","timestamp":1567641600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T00:00:00Z","timestamp":1567641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cardiovasc Intervent Radiol"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s00270-019-02336-0","type":"journal-article","created":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T16:49:58Z","timestamp":1567788598000},"page":"1771-1776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept"],"prefix":"10.1007","volume":"42","author":[{"given":"Dania","family":"Daye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro V.","family":"Staziaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa Fiorini","family":"Furtado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azadeh","family":"Tabari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian J.","family":"Fintelmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan Elie","family":"Frenk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Shyn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kemal","family":"Tuncali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stuart","family":"Silverman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald","family":"Arellano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael S.","family":"Gee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raul Nirmal","family":"Uppot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,5]]},"reference":[{"issue":"1","key":"2336_CR1","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1002\/1097-0142(1950)3:1<74::AID-CNCR2820030111>3.0.CO;2-7","volume":"3","author":"HL Abrams","year":"1950","unstructured":"Abrams HL, Spiro R, Goldstein N. Metastases in carcinoma; analysis of 1000 autopsied cases. Cancer. 1950;3(1):74\u201385.","journal-title":"Cancer"},{"issue":"1","key":"2336_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1046\/j.0300-0664.2001.01435.x","volume":"56","author":"KY Lam","year":"2002","unstructured":"Lam KY, Lo CY. Metastatic tumours of the adrenal glands: a 30-year experience in a teaching hospital. Clin Endocrinol. 2002;56(1):95\u2013101.","journal-title":"Clin Endocrinol"},{"issue":"3","key":"2336_CR3","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.jvir.2015.11.034","volume":"27","author":"FJ Fintelmann","year":"2016","unstructured":"Fintelmann FJ, Tuncali K, Puchner S, Gervais DA, Thabet A, Shyn PB, et al. Catecholamine surge during image-guided ablation of adrenal gland metastases: predictors, consequences, and recommendations for management. J Vasc Interv Radiol. 2016;27(3):395\u2013402. \n                    https:\/\/doi.org\/10.1016\/j.jvir.2015.11.034\n                    \n                  .","journal-title":"J Vasc Interv Radiol"},{"issue":"6","key":"2336_CR4","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1080\/02656730903066608","volume":"25","author":"Y Wang","year":"2009","unstructured":"Wang Y, Liang P, Yu X, Cheng Z, Yu J, Dong J. Ultrasound-guided percutaneous microwave ablation of adrenal metastasis: preliminary results. Int J Hyperthermia. 2009;25(6):455\u201361. \n                    https:\/\/doi.org\/10.1080\/02656730903066608\n                    \n                  .","journal-title":"Int J Hyperthermia"},{"issue":"22","key":"2336_CR5","doi-asserted-by":"publisher","first-page":"5182","DOI":"10.1002\/cncr.26128","volume":"117","author":"X Li","year":"2011","unstructured":"Li X, Fan W, Zhang L, Zhao M, Huang Z, Li W, et al. CT-guided percutaneous microwave ablation of adrenal malignant carcinoma: preliminary results. Cancer. 2011;117(22):5182\u20138. \n                    https:\/\/doi.org\/10.1002\/cncr.26128\n                    \n                  .","journal-title":"Cancer"},{"issue":"2","key":"2336_CR6","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1148\/radiol.2015142029","volume":"277","author":"T Hasegawa","year":"2015","unstructured":"Hasegawa T, Yamakado K, Nakatsuka A, Uraki J, Yamanaka T, Fujimori M, et al. Unresectable adrenal metastases: clinical outcomes of radiofrequency ablation. Radiology. 2015;277(2):584\u201393. \n                    https:\/\/doi.org\/10.1148\/radiol.2015142029\n                    \n                  .","journal-title":"Radiology"},{"issue":"6","key":"2336_CR7","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1007\/s00270-008-9377-6","volume":"31","author":"H Kuehl","year":"2008","unstructured":"Kuehl H, Stattaus J, Forsting M, Antoch G. Transhepatic CT-guided radiofrequency ablation of adrenal metastases from hepatocellular carcinoma. Cardiovasc Interv Radiol. 2008;31(6):1210\u20134. \n                    https:\/\/doi.org\/10.1007\/s00270-008-9377-6\n                    \n                  .","journal-title":"Cardiovasc Interv Radiol"},{"issue":"6","key":"2336_CR8","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1016\/j.urology.2009.06.058","volume":"74","author":"P Mouracade","year":"2009","unstructured":"Mouracade P, Dettloff H, Schneider M, Debras B, Jung JL. Radio-frequency ablation of solitary adrenal gland metastasis from renal cell carcinoma. Urology. 2009;74(6):1341\u20133. \n                    https:\/\/doi.org\/10.1016\/j.urology.2009.06.058\n                    \n                  .","journal-title":"Urology"},{"issue":"4","key":"2336_CR9","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.jvir.2013.12.013","volume":"25","author":"BT Welch","year":"2014","unstructured":"Welch BT, Callstrom MR, Carpenter PC, Wass CT, Welch TL, Boorjian SA, et al. A single-institution experience in image-guided thermal ablation of adrenal gland metastases. J Vasc Interv Radiol. 2014;25(4):593\u20138. \n                    https:\/\/doi.org\/10.1016\/j.jvir.2013.12.013\n                    \n                  .","journal-title":"J Vasc Interv Radiol"},{"issue":"6","key":"2336_CR10","doi-asserted-by":"publisher","first-page":"W300","DOI":"10.2214\/ajr.08.1752","volume":"192","author":"K Yamakado","year":"2009","unstructured":"Yamakado K, Anai H, Takaki H, Sakaguchi H, Tanaka T, Kichikawa K, et al. Adrenal metastasis from hepatocellular carcinoma: radiofrequency ablation combined with adrenal arterial chemoembolization in six patients. AJR Am J Roentgenol. 2009;192(6):W300\u20135. \n                    https:\/\/doi.org\/10.2214\/ajr.08.1752\n                    \n                  .","journal-title":"AJR Am J Roentgenol"},{"issue":"8","key":"2336_CR11","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1016\/j.ejrad.2011.04.054","volume":"81","author":"FJ Wolf","year":"2012","unstructured":"Wolf FJ, Dupuy DE, Machan JT, Mayo-Smith WW. Adrenal neoplasms: effectiveness and safety of CT-guided ablation of 23 tumors in 22 patients. Eur J Radiol. 2012;81(8):1717\u201323. \n                    https:\/\/doi.org\/10.1016\/j.ejrad.2011.04.054\n                    \n                  .","journal-title":"Eur J Radiol"},{"issue":"4","key":"2336_CR12","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1007\/s00270-008-9337-1","volume":"31","author":"G Carrafiello","year":"2008","unstructured":"Carrafiello G, Lagana D, Recaldini C, Giorgianni A, Ianniello A, Lumia D, et al. Imaging-guided percutaneous radiofrequency ablation of adrenal metastases: preliminary results at a single institution with a single device. Cardiovasc Interv Radiol. 2008;31(4):762\u20137. \n                    https:\/\/doi.org\/10.1007\/s00270-008-9337-1\n                    \n                  .","journal-title":"Cardiovasc Interv Radiol"},{"issue":"5","key":"2336_CR13","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3109\/02656736.2016.1164905","volume":"32","author":"C Ren","year":"2016","unstructured":"Ren C, Liang P, Yu XL, Cheng ZG, Han ZY, Yu J. Percutaneous microwave ablation of adrenal tumours under ultrasound guidance in 33 patients with 35 tumours: a single-centre experience. Int J Hyperthermia. 2016;32(5):517\u201323. \n                    https:\/\/doi.org\/10.3109\/02656736.2016.1164905\n                    \n                  .","journal-title":"Int J Hyperthermia"},{"issue":"2","key":"2336_CR14","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.jvir.2017.07.026","volume":"29","author":"NE Frenk","year":"2018","unstructured":"Frenk NE, Daye D, Tuncali K, Arellano RS, Shyn PB, Silverman SG, et al. Local control and survival after image-guided percutaneous ablation of adrenal metastases. J Vasc Interv Radiol. 2018;29(2):276\u201384. \n                    https:\/\/doi.org\/10.1016\/j.jvir.2017.07.026\n                    \n                  .","journal-title":"J Vasc Interv Radiol"},{"issue":"1","key":"2336_CR15","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1148\/radiol.11110264","volume":"261","author":"V Goh","year":"2011","unstructured":"Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology. 2011;261(1):165\u201371. \n                    https:\/\/doi.org\/10.1148\/radiol.11110264\n                    \n                  .","journal-title":"Radiology"},{"issue":"2","key":"2336_CR16","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1148\/radiol.2502071879","volume":"250","author":"KA Miles","year":"2009","unstructured":"Miles KA, Ganeshan B, Griffiths MR, Young RC, Chatwin CR. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology. 2009;250(2):444\u201352. \n                    https:\/\/doi.org\/10.1148\/radiol.2502071879\n                    \n                  .","journal-title":"Radiology"},{"issue":"1","key":"2336_CR17","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1148\/radiol.12112428","volume":"266","author":"B Ganeshan","year":"2013","unstructured":"Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology. 2013;266(1):326\u201336. \n                    https:\/\/doi.org\/10.1148\/radiol.12112428\n                    \n                  .","journal-title":"Radiology"},{"issue":"11","key":"2336_CR18","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1109\/83.725367","volume":"7","author":"X Tang","year":"1998","unstructured":"Tang X. Texture information in run-length matrices. IEEE Trans Image Process. 1998;7(11):1602\u20139. \n                    https:\/\/doi.org\/10.1109\/83.725367\n                    \n                  .","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"2336_CR19","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/tsmc.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3(6):610\u201321. \n                    https:\/\/doi.org\/10.1109\/tsmc.1973.4309314\n                    \n                  .","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"2336_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.clinimag.2017.11.007","volume":"49","author":"RC Mayo","year":"2018","unstructured":"Mayo RC, Leung J. Artificial intelligence and deep learning\u2014radiology\u2019s next frontier? Clin Imaging. 2018;49:87\u20138. \n                    https:\/\/doi.org\/10.1016\/j.clinimag.2017.11.007\n                    \n                  .","journal-title":"Clin Imaging"},{"issue":"2","key":"2336_CR21","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","volume":"37","author":"BJ Erickson","year":"2017","unstructured":"Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505\u201315. \n                    https:\/\/doi.org\/10.1148\/rg.2017160130\n                    \n                  .","journal-title":"Radiographics"},{"issue":"4\u20135","key":"2336_CR22","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.compmedimag.2007.02.011","volume":"31","author":"H Yoshida","year":"2007","unstructured":"Yoshida H, Nappi J. CAD in CT colonography without and with oral contrast agents: progress and challenges. Comput Med Imaging Graph.\u00a02007;31(4\u20135):267\u201384. \n                    https:\/\/doi.org\/10.1016\/j.compmedimag.2007.02.011\n                    \n                  .","journal-title":"Comput Med Imaging Graph"},{"issue":"10","key":"2336_CR23","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1118\/1.597428","volume":"22","author":"HP Chan","year":"1995","unstructured":"Chan HP, Lo SC, Sahiner B, Lam KL, Helvie MA. Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. Med Phys. 1995;22(10):1555\u201367. \n                    https:\/\/doi.org\/10.1118\/1.597428\n                    \n                  .","journal-title":"Med Phys"},{"issue":"4","key":"2336_CR24","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s10278-017-9988-z","volume":"30","author":"H Lee","year":"2017","unstructured":"Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, et al. Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging. 2017;30(4):487\u201398. \n                    https:\/\/doi.org\/10.1007\/s10278-017-9988-z\n                    \n                  .","journal-title":"J Digit Imaging"},{"issue":"12","key":"2336_CR25","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1016\/j.crad.2004.07.008","volume":"59","author":"G Castellano","year":"2004","unstructured":"Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59(12):1061\u20139. \n                    https:\/\/doi.org\/10.1016\/j.crad.2004.07.008\n                    \n                  .","journal-title":"Clin Radiol"},{"key":"2336_CR26","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"6","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;6:610\u201321.","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"2336_CR27","volume-title":"Handbook of pattern recognition and computer vision","author":"CC Hau","year":"2015","unstructured":"Hau CC. Handbook of pattern recognition and computer vision. Singapore: World Scientific; 2015."},{"key":"2336_CR28","doi-asserted-by":"crossref","unstructured":"Laws KI. Textured image segmentation: University of Southern California Los Angeles Image Processing INST; 1980.","DOI":"10.21236\/ADA083283"},{"issue":"3","key":"2336_CR29","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837\u201345.","journal-title":"Biometrics"},{"issue":"2","key":"2336_CR30","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. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563\u201377. \n                    https:\/\/doi.org\/10.1148\/radiol.2015151169\n                    \n                  .","journal-title":"Radiology"},{"issue":"5","key":"2336_CR31","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.2214\/ajr.16.16193","volume":"207","author":"K Sasaguri","year":"2016","unstructured":"Sasaguri K, Takahashi N, Takeuchi M, Carter RE, Leibovich BC, Kawashima A. Differentiation of benign from metastatic adrenal masses in patients with renal cell carcinoma on contrast-enhanced CT. AJR Am J Roentgenol. 2016;207(5):1031\u20138. \n                    https:\/\/doi.org\/10.2214\/ajr.16.16193\n                    \n                  .","journal-title":"AJR Am J Roentgenol"},{"issue":"5","key":"2336_CR32","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.2214\/ajr.17.19276","volume":"211","author":"W Tu","year":"2018","unstructured":"Tu W, Verma R, Krishna S, McInnes MDF, Flood TA, Schieda N. Can adrenal adenomas be differentiated from adrenal metastases at single-phase contrast-enhanced CT? AJR Am J Roentgenol. 2018;211(5):1044\u201350. \n                    https:\/\/doi.org\/10.2214\/ajr.17.19276\n                    \n                  .","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"2336_CR33","doi-asserted-by":"publisher","first-page":"W152","DOI":"10.2214\/ajr.16.17649","volume":"209","author":"N Schieda","year":"2017","unstructured":"Schieda N, Krishna S, McInnes MDF, Moosavi B, Alrashed A, Moreland R, et al. Utility of MRI to differentiate clear cell renal cell carcinoma adrenal metastases from adrenal adenomas. AJR Am J Roentgenol. 2017;209(3):W152\u20139. \n                    https:\/\/doi.org\/10.2214\/ajr.16.17649\n                    \n                  .","journal-title":"AJR Am J Roentgenol"},{"issue":"19","key":"2336_CR34","doi-asserted-by":"publisher","first-page":"3577","DOI":"10.7150\/jca.26356","volume":"9","author":"X Yi","year":"2018","unstructured":"Yi X, Guan X, Chen C, Zhang Y, Zhang Z, Li M, et al. Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma. J Cancer. 2018;9(19):3577\u201382. \n                    https:\/\/doi.org\/10.7150\/jca.26356\n                    \n                  .","journal-title":"J Cancer"}],"container-title":["CardioVascular and Interventional Radiology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00270-019-02336-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00270-019-02336-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00270-019-02336-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T23:30:17Z","timestamp":1599175817000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00270-019-02336-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,5]]},"references-count":34,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["2336"],"URL":"https:\/\/doi.org\/10.1007\/s00270-019-02336-0","relation":{},"ISSN":["0174-1551","1432-086X"],"issn-type":[{"value":"0174-1551","type":"print"},{"value":"1432-086X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,5]]},"assertion":[{"value":"23 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}