{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:21:29Z","timestamp":1777854089775,"version":"3.51.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-026-02285-4","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T13:02:53Z","timestamp":1774098173000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO\/ISUP grade of patients with clear cell renal cell carcinoma"],"prefix":"10.1186","volume":"26","author":[{"given":"Xiang","family":"Tao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"issue":"1","key":"2285_CR1","first-page":"17","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u201348. Epub 2023\/01\/13.","journal-title":"CA Cancer J Clin"},{"key":"2285_CR2","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.ejca.2018.07.005","volume":"103","author":"J Ferlay","year":"2018","unstructured":"Ferlay J, Colombet M, Soerjomataram I, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. Eur J cancer (Oxford England: 1990). 2018;103:356\u201387. Epub 2018\/08\/14.","journal-title":"Eur J cancer (Oxford England: 1990)"},{"issue":"2","key":"2285_CR3","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1111\/his.13737","volume":"74","author":"J Dagher","year":"2019","unstructured":"Dagher J, Delahunt B, Rioux-Leclercq N, et al. Assessment of tumour-associated necrosis provides prognostic information additional to World Health Organization\/International Society of Urological Pathology grading for clear cell renal cell carcinoma. Histopathology. 2019;74(2):284\u201390. Epub 2018\/08\/22.","journal-title":"Histopathology"},{"issue":"1","key":"2285_CR4","doi-asserted-by":"publisher","first-page":"71","DOI":"10.6004\/jnccn.2022.0001","volume":"20","author":"RJ Motzer","year":"2022","unstructured":"Motzer RJ, Jonasch E, Agarwal N, et al. Kidney Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Network: JNCCN. 2022;20(1):71\u201390. Epub 2022\/01\/07.","journal-title":"J Natl Compr Cancer Network: JNCCN"},{"key":"2285_CR5","doi-asserted-by":"publisher","first-page":"112300","DOI":"10.1016\/j.intimp.2024.112300","volume":"135","author":"Y Li","year":"2024","unstructured":"Li Y, Fan C, Hu Y, et al. Multi-cohort validation: A comprehensive exploration of prognostic marker in clear cell renal cell carcinoma. Int Immunopharmacol. 2024;135:112300. Epub 2024\/05\/23.","journal-title":"Int Immunopharmacol"},{"issue":"1","key":"2285_CR6","doi-asserted-by":"publisher","first-page":"4740","DOI":"10.1038\/s41598-024-54052-6","volume":"14","author":"J Choi","year":"2024","unstructured":"Choi J, Bang S, Suh J, et al. Survival pattern of metastatic renal cell carcinoma patients according to WHO\/ISUP grade: a long-term multi-institutional study. Sci Rep. 2024;14(1):4740. Epub 2024\/02\/28.","journal-title":"Sci Rep"},{"issue":"4","key":"2285_CR7","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.eururo.2015.07.072","volume":"69","author":"L Marconi","year":"2016","unstructured":"Marconi L, Dabestani S, Lam TB, et al. Systematic Review and Meta-analysis of Diagnostic Accuracy of Percutaneous Renal Tumour Biopsy. Eur Urol. 2016;69(4):660\u201373. Epub 2015\/09\/02.","journal-title":"Eur Urol"},{"issue":"4","key":"2285_CR8","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1016\/j.acra.2023.10.014","volume":"31","author":"S Luo","year":"2024","unstructured":"Luo S, Lin W, Wu J, et al. Quantitative Measurement on Contrast-Enhanced CT Distinguishes Small Clear Cell Renal Cell Carcinoma From Benign Renal Tumors: A Multicenter Study. Acad Radiol. 2024;31(4):1460\u201371. Epub 2023\/11\/10.","journal-title":"Acad Radiol"},{"key":"2285_CR9","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1007\/s00261-024-04199-7","volume":"49","author":"H Zhang","year":"2024","unstructured":"Zhang H, Li F, Jing M, Xi H, Zheng Y, Liu J. Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO\/ISUP pathological grading in clear cell renal cell carcinoma. Abdom Radiol. 2024;49:1185\u201393.","journal-title":"Abdom Radiol"},{"issue":"8","key":"2285_CR10","doi-asserted-by":"publisher","first-page":"627e23","DOI":"10.1016\/j.crad.2021.02.033","volume":"76","author":"D Han","year":"2021","unstructured":"Han D, Yu Y, He T, et al. Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO\/ISUP classification of clear cell renal cell carcinoma. Clin Radiol. 2021;76(8):627e23. -.e29. Epub 2021\/05\/15.","journal-title":"Clin Radiol"},{"issue":"12","key":"2285_CR11","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1007\/s11547-025-02111-x","volume":"130","author":"D Cozzi","year":"2025","unstructured":"Cozzi D, Lugli B, Paolucci S, et al. Thymomas under the radiomic lens: preliminary evidence of CT-radiomics signatures for histological grading and disease staging. Radiol Med. 2025;130(12):1949\u201358. Epub 2025 Oct 1.","journal-title":"Radiol Med"},{"key":"2285_CR12","doi-asserted-by":"publisher","first-page":"1665459","DOI":"10.3389\/fimmu.2025.1665459","volume":"16","author":"P Li","year":"2025","unstructured":"Li P, Liu Y, Liu R, et al. A dual-modality machine learning precision diagnostic model integrated radiomics and proteomics for breast cancer. Front Immunol. 2025;16:1665459.","journal-title":"Front Immunol"},{"issue":"8","key":"2285_CR13","doi-asserted-by":"publisher","first-page":"109373","DOI":"10.4329\/wjr.v17.i8.109373","volume":"17","author":"S Ren","year":"2025","unstructured":"Ren S, Qin B, Daniels MJ, et al. Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer. World J Radiol. 2025;17(8):109373.","journal-title":"World J Radiol"},{"issue":"6","key":"2285_CR14","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1007\/s00261-021-02954-8","volume":"46","author":"S Chen","year":"2021","unstructured":"Chen S, Ren S, Guo K, et al. Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram. Abdom Radiol (NY). 2021;46(6):2637\u201346. Epub 2021 Feb 8.","journal-title":"Abdom Radiol (NY)"},{"issue":"8","key":"2285_CR15","doi-asserted-by":"publisher","first-page":"e011569","DOI":"10.1136\/jitc-2025-011569","volume":"13","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Zhang X, Zhong X, et al. Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III-IV d-MMR\/MSI-H colorectal cancer. J Immunother Cancer. 2025;13(8):e011569.","journal-title":"J Immunother Cancer"},{"issue":"1","key":"2285_CR16","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41698-025-00884-y","volume":"9","author":"W Niu","year":"2025","unstructured":"Niu W, Yan J, Hao M, et al. MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas. NPJ precision Oncol. 2025;9(1):89. Epub 2025\/03\/28.","journal-title":"NPJ precision Oncol"},{"issue":"1","key":"2285_CR17","first-page":"277","volume":"27","author":"M Mahootiha","year":"2025","unstructured":"Mahootiha M, Tak D, Ye Z, et al. Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas. Neurooncology. 2025;27(1):277\u201390. Epub 2024\/08\/31.","journal-title":"Neurooncology"},{"key":"2285_CR18","doi-asserted-by":"publisher","first-page":"110401","DOI":"10.1016\/j.mri.2025.110401","volume":"121","author":"W Wang","year":"2025","unstructured":"Wang W, Wang Z, Wang L, et al. Study on predicting breast cancer Ki-67 expression using a combination of radiomics and deep learning based on multiparametric MRI. Magn Reson Imaging. 2025;121:110401. Epub 2025\/05\/14.","journal-title":"Magn Reson Imaging"},{"issue":"1","key":"2285_CR19","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1038\/s41523-024-00628-4","volume":"10","author":"H Liu","year":"2024","unstructured":"Liu H, Zou L, Xu N, et al. Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer. NPJ breast cancer. 2024;10(1):22. Epub 2024\/03\/13.","journal-title":"NPJ breast cancer"},{"issue":"1","key":"2285_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s40364-024-00561-5","volume":"12","author":"X Zhang","year":"2024","unstructured":"Zhang X, Zhang G, Qiu X, et al. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res. 2024;12(1):12. Epub 2024\/01\/26.","journal-title":"Biomark Res"},{"issue":"12","key":"2285_CR21","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1007\/s11604-024-01639-8","volume":"42","author":"Y Zhu","year":"2024","unstructured":"Zhu Y, Zheng D, Xu S, et al. Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma. Japanese J Radiol. 2024;42(12):1413\u201324. Epub 2024\/08\/20.","journal-title":"Japanese J Radiol"},{"issue":"2","key":"2285_CR22","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s00330-022-09055-0","volume":"33","author":"J Li","year":"2023","unstructured":"Li J, Qiu Z, Zhang C, et al. ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features. Eur Radiol. 2023;33(2):893\u2013903. Epub 2022\/08\/25.","journal-title":"Eur Radiol"},{"issue":"5","key":"2285_CR23","doi-asserted-by":"publisher","first-page":"3215","DOI":"10.1007\/s00330-023-10339-2","volume":"34","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Chen J, Yang C, Dai Y, Zeng M. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging. Eur Radiol. 2024;34(5):3215\u201325. Epub 2023\/10\/19.","journal-title":"Eur Radiol"},{"issue":"1","key":"2285_CR24","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s12885-025-13445-0","volume":"25","author":"Z Zuo","year":"2025","unstructured":"Zuo Z, Deng J, Ge W, et al. Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma. BMC Cancer. 2025;25(1):51. Epub 2025\/01\/10.","journal-title":"BMC Cancer"},{"issue":"2","key":"2285_CR25","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328\u201338. Epub 2020\/03\/11.","journal-title":"Radiology"},{"issue":"3","key":"2285_CR26","doi-asserted-by":"publisher","first-page":"100320","DOI":"10.1016\/j.labinv.2023.100320","volume":"104","author":"F Deng","year":"2024","unstructured":"Deng F, Zhao L, Yu N, Lin Y, Zhang L. Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer. Lab Invest. 2024;104(3):100320.","journal-title":"Lab Invest"},{"issue":"9","key":"2285_CR27","doi-asserted-by":"publisher","first-page":"4289","DOI":"10.1007\/s00261-021-03090-z","volume":"46","author":"X Wang","year":"2021","unstructured":"Wang X, Song G, Jiang H, et al. Can texture analysis based on single unenhanced CT accurately predict the WHO\/ISUP grading of localized clear cell renal cell carcinoma? Abdom Radiol (New York). 2021;46(9):4289\u2013300. Epub 2021\/04\/29.","journal-title":"Abdom Radiol (New York)"},{"issue":"1","key":"2285_CR28","doi-asserted-by":"publisher","first-page":"12043","DOI":"10.1038\/s41598-024-60921-x","volume":"14","author":"C Lu","year":"2024","unstructured":"Lu C, Xia Y, Han J, et al. Multiphase comparative study for WHO\/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma. Sci Rep. 2024;14(1):12043. Epub 2024\/05\/28.","journal-title":"Sci Rep"},{"key":"2285_CR29","doi-asserted-by":"publisher","first-page":"1467775","DOI":"10.3389\/fonc.2024.1467775","volume":"14","author":"Y Yang","year":"2024","unstructured":"Yang Y, Zhang Z, Zhang H, et al. Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO\/ISUP nuclear grading of clear cell renal cell carcinoma. Front Oncol. 2024;14:1467775. Epub 2024\/11\/22.","journal-title":"Front Oncol"},{"issue":"1","key":"2285_CR30","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s13244-024-01739-z","volume":"15","author":"X Li","year":"2024","unstructured":"Li X, Lin J, Qi H, et al. Radiomics predict the WHO\/ISUP nuclear grade and survival in clear cell renal cell carcinoma. Insights into imaging. 2024;15(1):175. Epub 2024\/07\/12.","journal-title":"Insights into imaging"},{"issue":"10","key":"2285_CR31","doi-asserted-by":"publisher","first-page":"7031","DOI":"10.21037\/qims-24-35","volume":"14","author":"Y Xv","year":"2024","unstructured":"Xv Y, Wei Z, Lv F, et al. Multiparameter computed tomography (CT) radiomics signature fusion-based model for the preoperative prediction of clear cell renal cell carcinoma nuclear grade: a multicenter development and external validation study. Quant imaging Med Surg. 2024;14(10):7031\u201345. Epub 2024\/10\/21.","journal-title":"Quant imaging Med Surg"},{"key":"2285_CR32","doi-asserted-by":"publisher","first-page":"831112","DOI":"10.3389\/fonc.2022.831112","volume":"12","author":"Y Ma","year":"2022","unstructured":"Ma Y, Guan Z, Liang H, Cao H. Predicting the WHO\/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics. Front Oncol. 2022;12:831112. Epub 2022\/03\/04.","journal-title":"Front Oncol"},{"issue":"6","key":"2285_CR33","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1007\/s00330-022-09312-2","volume":"33","author":"S Li","year":"2023","unstructured":"Li S, He K, Yuan G, et al. WHO\/ISUP grade and pathological T stage of clear cell renal cell carcinoma: value of ZOOMit diffusion kurtosis imaging and chemical exchange saturation transfer imaging. Eur Radiol. 2023;33(6):4429\u201339. Epub 2022\/12\/07.","journal-title":"Eur Radiol"},{"issue":"8","key":"2285_CR34","doi-asserted-by":"publisher","first-page":"6078","DOI":"10.1007\/s00330-020-07667-y","volume":"31","author":"Z Zheng","year":"2021","unstructured":"Zheng Z, Chen Z, Xie Y, Zhong Q, Xie W. Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades. Eur Radiol. 2021;31(8):6078\u201386. Epub 2021\/01\/31.","journal-title":"Eur Radiol"},{"key":"2285_CR35","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.ebiom.2019.05.023","volume":"44","author":"C Xie","year":"2019","unstructured":"Xie C, Yang P, Zhang X, et al. Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy. EBioMedicine. 2019;44:289\u201397. Epub 2019\/05\/28.","journal-title":"EBioMedicine"},{"key":"2285_CR36","doi-asserted-by":"publisher","first-page":"1252074","DOI":"10.3389\/fonc.2023.1252074","volume":"13","author":"S Wang","year":"2023","unstructured":"Wang S, Liu X, Wu Y, et al. Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study. Front Oncol. 2023;13:1252074. Epub 2023\/11\/13.","journal-title":"Front Oncol"},{"issue":"8","key":"2285_CR37","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s00262-024-03724-3","volume":"73","author":"W Caii","year":"2024","unstructured":"Caii W, Wu X, Guo K, et al. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. Cancer Immunol immunotherapy: CII. 2024;73(8):153. Epub 2024\/06\/04.","journal-title":"Cancer Immunol immunotherapy: CII"},{"issue":"5","key":"2285_CR38","doi-asserted-by":"publisher","first-page":"356","DOI":"10.3390\/jpm11050356","volume":"11","author":"YH Kim","year":"2021","unstructured":"Kim YH, Park JB, Chang MS, et al. Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery. J Pers Med. 2021;11(5):356.","journal-title":"J Pers Med"},{"issue":"10","key":"2285_CR39","doi-asserted-by":"publisher","first-page":"5907","DOI":"10.1016\/j.acra.2025.06.056","volume":"32","author":"Z Yang","year":"2025","unstructured":"Yang Z, Jiang H, Shan S, et al. 2.5D Deep Learning-Based Prediction of Pathological Grading of Clear Cell Renal Cell Carcinoma Using Contrast-Enhanced CT: A Multicenter Study. Acad Radiol. 2025;32(10):5907\u201316. Epub 2025 Jul 19.","journal-title":"Acad Radiol"},{"issue":"1","key":"2285_CR40","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s12672-025-02170-6","volume":"16","author":"H Niu","year":"2025","unstructured":"Niu H, Li L, Wang X, et al. The value of predicting breast cancer with a DBT 2.5D deep learning model. Discov Oncol. 2025;16(1):420.","journal-title":"Discov Oncol"},{"key":"2285_CR41","doi-asserted-by":"crossref","unstructured":"Vermijs S, Vangeneugden J, Visschere P, Backer P, Decaestecker K, Praet C, Debbaut C. Measurement of renal tumor size: the impact of 2D versus 3D methods on the T-stage. Abdom Radiol. 2025 Nov 22.","DOI":"10.1007\/s00261-025-05292-1"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-026-02285-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02285-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02285-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:01:52Z","timestamp":1777503712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-026-02285-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,21]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2285"],"URL":"https:\/\/doi.org\/10.1186\/s12880-026-02285-4","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,21]]},"assertion":[{"value":"14 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The study was conducted in accordance with the Declaration of Helsinki (as amended in 2013). The study was approved by the Medical Ethics Committee of Suzhou Yongding Hospital(202452). Due to the nature of the retrospective analysis, it is not necessary to obtain the written informed consent of the participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"221"}}