{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:13:40Z","timestamp":1761948820117,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000070","name":"NIBIB","doi-asserted-by":"crossref","award":["R01EB000194"],"award-info":[{"award-number":["R01EB000194"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Cancer Institute\/National Institutes of Health","award":["5K12-CA138464"],"award-info":[{"award-number":["5K12-CA138464"]}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["P41 EB032840"],"award-info":[{"award-number":["P41 EB032840"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted\/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%\/9%\/11% for training, validation and testing. Six configurations of correction\/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 0.16, 0.73 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 0.168, and 0.99 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 0.012, respectively, while for SL predictions were 0.80 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 0.184, 0.78 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 0.193, and 1.00 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6\u00a0mm, respectively.\u00a0The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.<\/jats:p>","DOI":"10.1007\/s10278-023-00785-1","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T17:07:14Z","timestamp":1677604034000},"page":"1049-1059","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data"],"prefix":"10.1007","volume":"36","author":[{"given":"Chih-Chieh","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yasser G.","family":"Abdelhafez","sequence":"additional","affiliation":[]},{"given":"S. Paran","family":"Yap","sequence":"additional","affiliation":[]},{"given":"Francesco","family":"Acquafredda","sequence":"additional","affiliation":[]},{"given":"Silvia","family":"Schir\u00f2","sequence":"additional","affiliation":[]},{"given":"Andrew L.","family":"Wong","sequence":"additional","affiliation":[]},{"given":"Dani","family":"Sarohia","sequence":"additional","affiliation":[]},{"given":"Cyrus","family":"Bateni","sequence":"additional","affiliation":[]},{"given":"Morgan A.","family":"Darrow","sequence":"additional","affiliation":[]},{"given":"Michele","family":"Guindani","sequence":"additional","affiliation":[]},{"given":"Sonia","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Michelle","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ahmed W.","family":"Moawad","sequence":"additional","affiliation":[]},{"given":"Quinn Kwan-Tai","family":"Ng","sequence":"additional","affiliation":[]},{"given":"Layla","family":"Shere","sequence":"additional","affiliation":[]},{"given":"Khaled M.","family":"Elsayes","sequence":"additional","affiliation":[]},{"given":"Roberto","family":"Maroldi","sequence":"additional","affiliation":[]},{"given":"Thomas M.","family":"Link","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Nardo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5428-0322","authenticated-orcid":false,"given":"Jinyi","family":"Qi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"issue":"3","key":"785_CR1","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1148\/rg.2016150133","volume":"36","author":"P Gupta","year":"2016","unstructured":"Gupta P, Potti TA, Wuertzer SD, Lenchik L, Pacholke DA. Spectrum of fat-containing soft-tissue masses at MR imaging: the common, the uncommon, the characteristic, and the sometimes confusing. Radiographics. 2016;36(3):753\u2013766.","journal-title":"Radiographics."},{"issue":"3","key":"785_CR2","doi-asserted-by":"publisher","first-page":"733","DOI":"10.2214\/ajr.182.3.1820733","volume":"182","author":"CM Gaskin","year":"2004","unstructured":"Gaskin CM, Helms CA. Lipomas, lipoma variants, and well-differentiated liposarcomas (atypical lipomas): results of MRI evaluations of 126 consecutive fatty masses. Am J Roentgenol. 2004;182(3):733\u2013739.","journal-title":"Am J Roentgenol."},{"key":"785_CR3","doi-asserted-by":"crossref","unstructured":"O\u2019Donnell PW, Griffin AM, Eward WC, et al. Can experienced observers differentiate between lipoma and well-differentiated liposarcoma using only MRI? Sarcoma. 2013;2013.","DOI":"10.1155\/2013\/982784"},{"key":"785_CR4","doi-asserted-by":"crossref","unstructured":"Brisson M, Kashima T, Delaney D, et al. MRI characteristics of lipoma and atypical lipomatous tumor\/well- differentiated liposarcoma: Retrospective comparison with histology and MDM2 gene amplification. Skeletal Radiol. 2012\/09\/19. 2013;42(5):635\u2013647.","DOI":"10.1007\/s00256-012-1517-z"},{"key":"785_CR5","doi-asserted-by":"crossref","unstructured":"Ryan S, Visgauss J, Kerr D, et al. The value of MRI in distinguishing subtypes of lipomatous extremity tumors needs reassessment in the Era of MDM2 and CDK4 testing. Sarcoma. 2018;2018.","DOI":"10.1155\/2018\/1901896"},{"issue":"2","key":"785_CR6","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.crad.2019.10.006","volume":"75","author":"AS Alshabibi","year":"2020","unstructured":"Alshabibi AS, Suleiman ME, Tapia KA, Brennan PC. Effects of time of day on radiological interpretation. Clin Radiol. 2020;75(2):148\u2013155.","journal-title":"Clin Radiol."},{"issue":"1","key":"785_CR7","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1148\/radiol.2017170213","volume":"286","author":"B Baessler","year":"2018","unstructured":"Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images. Radiology. 2018;286(1):103\u2013112.","journal-title":"Radiology."},{"key":"785_CR8","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3389\/fneur.2017.00327","volume":"8","author":"MDC Vald\u00e9s Hern\u00e1ndez","year":"2017","unstructured":"Vald\u00e9s Hern\u00e1ndez MDC, Gonz\u00e1lez-Castro V, Chappell FM, et al. Application of Texture analysis to study small Vessel Disease and Blood\u2013Brain Barrier integrity. Front Neurol. 2017;8:327.","journal-title":"Front Neurol."},{"issue":"1","key":"785_CR9","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.mri.2013.04.006","volume":"32","author":"B Barry","year":"2014","unstructured":"Barry B, Buch K, Soto JA, Jara H, Nakhmani A, Anderson SW. Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging. Magn Reson Imaging. 2014;32(1):84\u201390.","journal-title":"Magn Reson Imaging."},{"key":"785_CR10","doi-asserted-by":"crossref","unstructured":"Juntu J, Sijbers J, Backer D, S., Rajan J, Van Dyck D. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft\u2010tissue tumors in T1\u2010MRI images. J Magn Reson Imaging An Off J Int Soc Magn Reson Med. 2010;31(3):680\u2013689.","DOI":"10.1002\/jmri.22095"},{"issue":"4","key":"785_CR11","doi-asserted-by":"publisher","first-page":"963","DOI":"10.2214\/AJR.19.22147","volume":"215","author":"W Xu","year":"2020","unstructured":"Xu W, Hao D, Hou F, Zhang D, Wang H. Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade. Am J Roentgenol. 2020;215(4):963\u2013969.","journal-title":"Am J Roentgenol."},{"key":"785_CR12","unstructured":"Acquafredda F, Abdelhafez YG, Zhang M, et al. Predictive value of MRI radiomic features in differentiating lipoma from atypical lipomatous tumour. Eur. Soc. Radiol. Vienna, Austria; 2019. https:\/\/ecronline.myesr.org\/ecr2019\/index.php?p=recorddetail%7B%5C&%7Drid=c8e8039e-3fc7-4594-97da-f0ffb8a3e25f%7B%5C&%7Dt=browsesessions%7B%5C#%7Dipp-record-1ae14120-374b-4a52-902d-0769e415f1ee."},{"issue":"11","key":"785_CR13","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1007\/s00256-020-03454-4","volume":"49","author":"I Pressney","year":"2020","unstructured":"Pressney I, Khoo M, Endozo R, Ganeshan B, O\u2019Donnell P. Pilot study to differentiate lipoma from atypical lipomatous tumour\/well-differentiated liposarcoma using MR radiomics-based texture analysis. Skelet Radiol. 2020;49(11):1719\u20131729.","journal-title":"Skelet Radiol."},{"key":"785_CR14","doi-asserted-by":"crossref","unstructured":"Malinauskaite I, Hofmeister J, Burgermeister S, et al. Radiomics and machine learning differentiate soft-tissue lipoma and liposarcoma better than musculoskeletal radiologists. Sarcoma. 2020;","DOI":"10.1155\/2020\/7163453"},{"issue":"13","key":"785_CR15","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1002\/bjs.11410","volume":"106","author":"M Vos","year":"2019","unstructured":"Vos M, Starmans MPA, Timbergen MJM, et al. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. Br J Surg. 2019;106(13):1800.","journal-title":"Br J Surg."},{"key":"785_CR16","doi-asserted-by":"crossref","unstructured":"Wang H, Zhang J, Bao S, et al. Preoperative MRI-based radiomic machine-learning nomogram may accurately distinguish between benign and malignant soft-tissue lesions: a two-center study. J Magn Reson Imaging. John Wiley and Sons Inc.; 2020;52(3):873\u2013882.","DOI":"10.1002\/jmri.27111"},{"issue":"9","key":"785_CR17","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1007\/s11604-022-01278-x","volume":"40","author":"N Cay","year":"2022","unstructured":"Cay N, Mendi BAR, Batur H, Erdogan F. Discrimination of lipoma from atypical lipomatous tumor\/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol. 2022;40(9):951\u2013960.","journal-title":"Jpn J Radiol."},{"issue":"6","key":"785_CR18","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1002\/jmri.28167","volume":"56","author":"Y Tang","year":"2022","unstructured":"Tang Y, Cui J, Zhu J, Fan G. Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics. J Magn Reson Imaging. 2022;56(6):1746\u20131754.","journal-title":"J Magn Reson Imaging."},{"key":"785_CR19","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhou Y, Zhou C, Ma X. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods. Orphanet J Rare Dis. 2022;17(1).","DOI":"10.1186\/s13023-022-02304-x"},{"key":"785_CR20","doi-asserted-by":"crossref","unstructured":"Haidey J, Low G, Wilson MP. Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review. Skeletal Radiol. 2022.","DOI":"10.1007\/s00256-022-04232-0"},{"key":"785_CR21","doi-asserted-by":"crossref","unstructured":"Asano Y, Miwa S, Yamamoto N, et al. A scoring system combining clinical, radiological, and histopathological examinations for differential diagnosis between lipoma and atypical lipomatous tumor\/well-differentiated liposarcoma. Sci Rep. 2022;12(1).","DOI":"10.1038\/s41598-021-04004-1"},{"key":"785_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693","volume":"63","author":"N Tajbakhsh","year":"2020","unstructured":"Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Med Image Anal. 2020;63:101693.","journal-title":"Med Image Anal."},{"issue":"1","key":"785_CR23","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"SA Taghanaki","year":"2021","unstructured":"Taghanaki SA, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G. Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev. 2021;54(1):137\u2013178.","journal-title":"Artif Intell Rev."},{"key":"785_CR24","doi-asserted-by":"crossref","unstructured":"Nyul LG, Udupa JK. Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning. Image Disp Vis. SPIE; 2000. p. 496\u2013504.","DOI":"10.1117\/12.383076"},{"key":"785_CR25","doi-asserted-by":"crossref","unstructured":"Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. Journal of Digital Imaging; 2017;30(4):449\u2013459.","DOI":"10.1007\/s10278-017-9983-4"},{"key":"785_CR26","doi-asserted-by":"crossref","unstructured":"Nardo L, Abdelhafez YG, Acquafredda F, et al. Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study. Skelet Radiol. Skeletal Radiology; 2020;49(6):1\u201310.","DOI":"10.1007\/s00256-020-03372-5"},{"issue":"6","key":"785_CR27","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310\u20131320.","journal-title":"IEEE Trans Med Imaging."},{"issue":"6","key":"785_CR28","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1002\/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M","volume":"42","author":"LG Ny\u00fal","year":"1999","unstructured":"Ny\u00fal LG, Udupa JK. On Standardizing the MR Image Intensity Scale. Magn Reson Med An Off J Int Soc Magn Reson Med. 1999;42(6):1072\u20131081.","journal-title":"Magn Reson Med An Off J Int Soc Magn Reson Med."},{"key":"785_CR29","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2015. p. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"785_CR30","unstructured":"Oktay O, Schlemper J, Folgoc L Le, et al. Attention u-net: learning where to look for the pancreas. arXiv:180403999v3. 2018;(Midl). http:\/\/arxiv.org\/abs\/1804.03999."},{"key":"785_CR31","unstructured":"Ioffe S, Szegedy C. Batch normalization\u202f: accelerating deep network training by reducing internal covariate shift. arXiv:150203167v3. 2015;"},{"key":"785_CR32","first-page":"1","volume":"2015","author":"DP Kingma","year":"2015","unstructured":"Kingma DP, Ba JL. Adam: a Method for stochastic optimization. Int Conf Learn Represent 2015. 2015;1\u201315.","journal-title":"Int Conf Learn Represent"},{"issue":"15","key":"785_CR33","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1080\/02664763.2018.1441383","volume":"45","author":"C Ju","year":"2018","unstructured":"Ju C, Bibaut A, van der Laan M. The relative performance of ensemble methods with deep convolutional neural networks for image classification. J Appl Stat. 2018;45(15):2800\u20132818.","journal-title":"J Appl Stat."},{"key":"785_CR34","doi-asserted-by":"crossref","unstructured":"Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med Imaging. BMC Medical Imaging; 2015;15(1).","DOI":"10.1186\/s12880-015-0068-x"},{"issue":"01","key":"785_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.5.1.015006","volume":"5","author":"V Yeghiazaryan","year":"2018","unstructured":"Yeghiazaryan V, Voiculescu I. Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imaging. 2018;5(01):1.","journal-title":"J Med Imaging."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00785-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00785-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00785-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T10:09:32Z","timestamp":1687514972000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00785-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,28]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["785"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00785-1","relation":{},"ISSN":["1618-727X"],"issn-type":[{"type":"electronic","value":"1618-727X"}],"subject":[],"published":{"date-parts":[[2023,2,28]]},"assertion":[{"value":"8 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research protocol was approved by our institutional review board (IRB). IRB waived the requirement for informed consent because of the retrospective chart review nature of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The study was approved by the Institutional Review Board at the host university and several data transfer agreements were in effect between host university and other universities. All participating sites fulfilled their institutional regulatory requirements, and where appropriate, obtained IRB approval or relied on UC Davis IRB approval (within Universities of California). Informed consent was waived for this retrospective chart review study at all sites.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The study was approved by the Institutional Review Board at the host university and several data sharing agreements were in effect between host university and other universities. Informed consent was waived for this retrospective chart review study at all sites.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}