{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T22:49:54Z","timestamp":1787870994526,"version":"build-2784847793"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Foundation of Zhongshan Hospital, Fudan University","award":["KYYJ202014"],"award-info":[{"award-number":["KYYJ202014"]}]},{"name":"the Natural Science Foundation of Xiamen City","award":["3502Z202373086"],"award-info":[{"award-number":["3502Z202373086"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiat Oncol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines\u2019 model showed the best predictive ability (AUC\u2009=\u20090.794, sensitivity\u2009=\u20090.641, specificity\u2009=\u20090.8, PPV\u2009=\u20090.676, NPV\u2009=\u20090.774 and accuracy\u2009=\u20090.737). An online calculator was established for clinicians to predict patients\u2019 risk of ALN metastasis (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/wuqian.shinyapps.io\/shinybsgi\/\">https:\/\/wuqian.shinyapps.io\/shinybsgi\/<\/jats:ext-link>\n                      ). The result in ROC showed the model could benefit from incorporating BSGI feature.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13014-024-02453-2","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T15:01:44Z","timestamp":1716822104000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features"],"prefix":"10.1186","volume":"19","author":[{"given":"Ranze","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"2453_CR1","doi-asserted-by":"publisher","unstructured":"Sung H, Ferlay J, Siegel RL, Global Cancer S. 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. (2021);71(3):209\u201349. https:\/\/doi.org\/10.3322\/caac.21660","DOI":"10.3322\/caac.21660"},{"key":"2453_CR2","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/1477-7819-12-67","volume":"12","author":"EW Soares","year":"2014","unstructured":"Soares EW, Nagai HM, Bredt LC. Morbidity after conventional dissection of axillary lymph nodes in breast cancer patients. World J Surg Oncol. 2014;12:67. https:\/\/doi.org\/10.1186\/1477-7819-12-67","journal-title":"WORLD J SURG ONCOL"},{"issue":"6","key":"2453_CR3","doi-asserted-by":"publisher","first-page":"100470","DOI":"10.1016\/j.currproblcancer.2019.02.002","volume":"43","author":"K Dinas","year":"2019","unstructured":"Dinas K, Kalder M, Zepiridis L. Axillary web syndrome: incidence, pathogenesis, and management. Curr Probl Cancer. 2019;43(6):100470. https:\/\/doi.org\/10.1016\/j.currproblcancer.2019.02.002","journal-title":"Curr Probl Cancer"},{"issue":"10","key":"2453_CR4","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/S1470-2045(10)70207-2","volume":"11","author":"DN Krag","year":"2010","unstructured":"Krag DN, Anderson SJ, Julian TB. Sentinel-lymph-node resection compared with conventional axillary-lymph-node dissection in clinically node-negative patients with breast cancer: overall survival findings from the NSABP B-32 randomised phase 3 trial. Lancet Oncol. 2010;11(10):927\u201333. https:\/\/doi.org\/10.1016\/S1470-2045(10)70207-2","journal-title":"LANCET ONCOL"},{"issue":"12","key":"2453_CR5","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1016\/S1470-2045(14)70460-7","volume":"15","author":"M Donker","year":"2014","unstructured":"Donker M, van Tienhoven G, Straver ME. Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer (EORTC 10981\u201322023 AMAROS): a randomised, multicentre, open-label, phase 3 non-inferiority trial. Lancet Oncol. 2014;15(12):1303\u201310. https:\/\/doi.org\/10.1016\/S1470-2045(14)70460-7","journal-title":"LANCET ONCOL"},{"issue":"10","key":"2453_CR6","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1001\/jama.2017.11470","volume":"318","author":"AE Giuliano","year":"2017","unstructured":"Giuliano AE, Ballman KV, McCall L. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (Alliance) randomized clinical trial. Jama. 2017;318(10):918\u201326. https:\/\/doi.org\/10.1001\/jama.2017.11470","journal-title":"JAMA"},{"issue":"24","key":"2453_CR7","doi-asserted-by":"publisher","first-page":"3670","DOI":"10.1200\/JCO.2006.08.8013","volume":"25","author":"JL Bevilacqua","year":"2007","unstructured":"Bevilacqua JL, Kattan MW, Fey JV. Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation. J Clin Oncol. 2007;25(24):3670\u20139. https:\/\/doi.org\/10.1200\/JCO.2006.08.8013","journal-title":"J CLIN ONCOL"},{"issue":"2","key":"2453_CR8","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1007\/s00330-020-07181-1","volume":"31","author":"Y Gao","year":"2021","unstructured":"Gao Y, Luo Y, Zhao C. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients. Eur Radiol. 2021;31(2):928\u201337. https:\/\/doi.org\/10.1007\/s00330-020-07181-1","journal-title":"EUR RADIOL"},{"issue":"1","key":"2453_CR9","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1053\/j.sult.2017.10.001","volume":"39","author":"AI Huppe","year":"2018","unstructured":"Huppe AI, Mehta AK, Brem RF. Molecular breast imaging: a comprehensive review. Semin Ultrasound CT MR. 2018;39(1):60\u20139. https:\/\/doi.org\/10.1053\/j.sult.2017.10.001","journal-title":"Semin Ultrasound CT MR"},{"issue":"1","key":"2453_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1148\/radiol.2251011667","volume":"225","author":"TM Kolb","year":"2002","unstructured":"Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002;225(1):165\u201375. https:\/\/doi.org\/10.1148\/radiol.2251011667","journal-title":"Radiology"},{"issue":"2","key":"2453_CR11","doi-asserted-by":"publisher","first-page":"241","DOI":"10.2214\/AJR.14.13357","volume":"204","author":"DJ Rhodes","year":"2015","unstructured":"Rhodes DJ, Hruska CB, Conners AL. Journal club: molecular breast imaging at reduced radiation dose for supplemental screening in mammographically dense breasts. AJR Am J Roentgenol. 2015;204(2):241\u201351. https:\/\/doi.org\/10.2214\/AJR.14.13357","journal-title":"AJR Am J Roentgenol"},{"issue":"2","key":"2453_CR12","doi-asserted-by":"publisher","first-page":"293","DOI":"10.2214\/AJR.13.11585","volume":"202","author":"LR Rechtman","year":"2014","unstructured":"Rechtman LR, Lenihan MJ, Lieberman JH. Breast-specific gamma imaging for the detection of breast cancer in dense versus nondense breasts. AJR Am J Roentgenol. 2014;202(2):293\u20138. https:\/\/doi.org\/10.2214\/AJR.13.11585","journal-title":"AJR Am J Roentgenol"},{"issue":"6","key":"2453_CR13","first-page":"926","volume":"37","author":"J Villanueva-Meyer","year":"1996","unstructured":"Villanueva-Meyer J, Leonard MJ, Briscoe E. Mammoscintigraphy with technetium-99m-sestamibi in suspected breast cancer. J Nucl Med. 1996;37(6):926\u201330.","journal-title":"J NUCL MED"},{"issue":"6","key":"2453_CR14","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1111\/j.1524-4741.2009.00834.x","volume":"15","author":"J Werner","year":"2009","unstructured":"Werner J, Rapelyea JA, Yost KG. Quantification of radio-tracer uptake in axillary lymph nodes using breast specific gamma imaging (BSGI): benign radio-tracer extravasation versus uptake secondary to breast cancer. Breast J. 2009;15(6):579\u201382. https:\/\/doi.org\/10.1111\/j.1524-4741.2009.00834.x","journal-title":"BREAST J"},{"issue":"2","key":"2453_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1177\/000313481107700225","volume":"77","author":"A Stojadinovic","year":"2011","unstructured":"Stojadinovic A, Nissan A, Eberhardt J. Development of a bayesian belief network model for personalized prognostic risk assessment in colon carcinomatosis. Am Surg. 2011;77(2):221\u201330.","journal-title":"Am Surg"},{"issue":"6","key":"2453_CR16","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1007\/s00330-019-06652-4","volume":"30","author":"JH Lee","year":"2020","unstructured":"Lee JH, Ha EJ, Kim D. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training. Eur Radiol. 2020;30(6):3066\u201372. https:\/\/doi.org\/10.1007\/s00330-019-06652-4","journal-title":"EUR RADIOL"},{"issue":"4","key":"2453_CR17","doi-asserted-by":"publisher","first-page":"219","DOI":"10.2967\/jnmt.110.082271","volume":"38","author":"SJ Goldsmith","year":"2010","unstructured":"Goldsmith SJ, Parsons W, Guiberteau MJ. SNM practice guideline for breast scintigraphy with breast-specific gamma-cameras 1.0. J Nucl Med Technol. 2010;38(4):219\u201324. https:\/\/doi.org\/10.2967\/jnmt.110.082271","journal-title":"J Nucl Med Technol"},{"issue":"6","key":"2453_CR18","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/0301-5629(90)90020-d","volume":"16","author":"DD Adler","year":"1990","unstructured":"Adler DD, Carson PL, Rubin JM. Doppler ultrasound color flow imaging in the study of breast cancer: preliminary findings. Ultrasound Med Biol. 1990;16(6):553\u20139. https:\/\/doi.org\/10.1016\/0301-5629(90)90020-d","journal-title":"ULTRASOUND MED BIOL"},{"issue":"24","key":"2453_CR19","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1093\/jnci\/djs455","volume":"104","author":"TJ Meretoja","year":"2012","unstructured":"Meretoja TJ, Leidenius MH, Heikkil\u00e4 PS. International Multicenter tool to predict the risk of nonsentinel node metastases in breast cancer. J Natl Cancer Inst. 2012;104(24):1888\u201396. https:\/\/doi.org\/10.1093\/jnci\/djs455","journal-title":"J Natl Cancer Inst"},{"issue":"6","key":"2453_CR20","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1111\/tbj.12497","volume":"21","author":"Z Al-Hilli","year":"2015","unstructured":"Al-Hilli Z, Hieken TJ, Boughey JC. Axillary Ultrasound in the management of the newly diagnosed breast Cancer patient. Breast J. 2015;21(6):634\u201341. https:\/\/doi.org\/10.1111\/tbj.12497","journal-title":"BREAST J"},{"issue":"4","key":"2453_CR21","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","volume":"15","author":"FJ Harrell","year":"1996","unstructured":"Harrell FJ, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361\u201387. https:\/\/doi.org\/10.1002\/(SICI)1097-0258(19960229)15:4%3C361::AID-SIM168%3E3.0.CO;2-4","journal-title":"STAT MED"},{"issue":"5","key":"2453_CR22","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1111\/cobi.13119","volume":"32","author":"RT Buxton","year":"2018","unstructured":"Buxton RT, McKenna MF, Clapp M. Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity. Conserv Biol. 2018;32(5):1174\u201384. https:\/\/doi.org\/10.1111\/cobi.13119","journal-title":"CONSERV BIOL"},{"key":"2453_CR23","doi-asserted-by":"publisher","unstructured":"Huang ML, Hung YH, Lee WM. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. Scientific World Journal. (2014);2014:795624. https:\/\/doi.org\/10.1155\/2014\/795624","DOI":"10.1155\/2014\/795624"},{"issue":"5","key":"2453_CR24","doi-asserted-by":"publisher","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","volume":"20","author":"KY Ngiam","year":"2019","unstructured":"Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262\u201373. https:\/\/doi.org\/10.1016\/S1470-2045(19)30149-4","journal-title":"LANCET ONCOL"},{"issue":"6","key":"2453_CR25","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.eururo.2018.08.038","volume":"74","author":"B Van Calster","year":"2018","unstructured":"Van Calster B, Wynants L, Verbeek J. Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol. 2018;74(6):796\u2013804. https:\/\/doi.org\/10.1016\/j.eururo.2018.08.038","journal-title":"EUR UROL"},{"issue":"9","key":"2453_CR26","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1093\/jnci\/djj158","volume":"98","author":"RE Mansel","year":"2006","unstructured":"Mansel RE, Fallowfield L, Kissin M. Randomized multicenter trial of sentinel node biopsy versus standard axillary treatment in operable breast cancer: the ALMANAC Trial. J Natl Cancer Inst. 2006;98(9):599\u2013609. https:\/\/doi.org\/10.1093\/jnci\/djj158","journal-title":"J Natl Cancer Inst"},{"key":"2453_CR27","doi-asserted-by":"publisher","first-page":"635771","DOI":"10.3389\/fmed.2021.635771","volume":"8","author":"J Zhu","year":"2021","unstructured":"Zhu J, Zheng J, Li L. Application of machine learning algorithms to predict central lymph node metastasis in T1-T2, non-invasive, and clinically node negative papillary thyroid carcinoma. Front Med (Lausanne). 2021;8:635771.","journal-title":"Front Med (Lausanne)"},{"key":"2453_CR28","doi-asserted-by":"publisher","first-page":"577537","DOI":"10.3389\/fendo.2020.577537","volume":"11","author":"Y Wu","year":"2020","unstructured":"Wu Y, Rao K, Liu J. Machine learning algorithms for the prediction of central lymph node metastasis in patients with papillary thyroid cancer. Front Endocrinol (Lausanne). 2020;11:577537.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"2453_CR29","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/s12880-020-00497-w","volume":"20","author":"H Liu","year":"2020","unstructured":"Liu H, Zhan H, Sun D. Comparison of BSGI, MRI, mammography, and ultrasound for the diagnosis of breast lesions and their correlations with specific molecular subtypes in Chinese women. BMC Med Imaging. 2020;20(1):98. https:\/\/doi.org\/10.1186\/s12880-020-00497-w","journal-title":"BMC MED IMAGING"},{"issue":"3B","key":"2453_CR30","first-page":"2299","volume":"19","author":"JB Cwikla","year":"1999","unstructured":"Cwikla JB, Buscombe JR, Kolasinska AD. Correlation between uptake of Tc-99m sestaMIBI and prognostic factors of breast cancer. Anticancer Res. 1999;19(3B):2299\u2013304.","journal-title":"ANTICANCER RES"},{"issue":"1","key":"2453_CR31","first-page":"91","volume":"20","author":"G Bonazzi","year":"2001","unstructured":"Bonazzi G, Cistaro A, Bell\u00f2 M. Breast cancer cellular proliferation indexes and 99mTc-sesta Mibi capture: what correlation? J Exp Clin Cancer Res. 2001;20(1):91\u20134.","journal-title":"J Exp Clin Cancer Res"},{"issue":"3","key":"2453_CR32","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1053\/ejso.2000.1096","volume":"27","author":"F Lumachi","year":"2001","unstructured":"Lumachi F, Ferretti G, Povolato M. Usefulness of 99m-Tc-sestamibi scintimammography in suspected breast cancer and in axillary lymph node metastases detection. Eur J Surg Oncol. 2001;27(3):256\u20139. https:\/\/doi.org\/10.1053\/ejso.2000.1096","journal-title":"Eur J Surg Oncol"},{"key":"2453_CR33","doi-asserted-by":"publisher","first-page":"21196","DOI":"10.1038\/srep21196","volume":"6","author":"SQ Qiu","year":"2016","unstructured":"Qiu SQ, Zeng HC, Zhang F. A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound. Sci Rep. 2016;6:21196. https:\/\/doi.org\/10.1038\/srep21196","journal-title":"Sci Rep"},{"issue":"2","key":"2453_CR34","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10911-006-9018-0","volume":"11","author":"CM Sehgal","year":"2006","unstructured":"Sehgal CM, Weinstein SP, Arger PH. A review of breast ultrasound. J Mammary Gland Biol Neoplasia. 2006;11(2):113\u201323. https:\/\/doi.org\/10.1007\/s10911-006-9018-0","journal-title":"J Mammary Gland Biol Neoplasia"},{"issue":"3","key":"2453_CR35","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/s12094-009-0329-7","volume":"11","author":"SP Khosravi","year":"2009","unstructured":"Khosravi SP, Soria LA, P\u00e9rez MG. Tumoral angiogenesis and breast cancer. Clin Transl Oncol. 2009;11(3):138\u201342. https:\/\/doi.org\/10.1007\/s12094-009-0329-7","journal-title":"CLIN TRANSL ONCOL"},{"issue":"2","key":"2453_CR36","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1159\/000337766","volume":"7","author":"ZY Shen","year":"2012","unstructured":"Shen ZY, Hu B, Wu MF. Correlation between blood flow signal of color flow imaging and nottingham prognostic index in patients with breast carcinoma. Breast Care (Basel). 2012;7(2):126\u201330. https:\/\/doi.org\/10.1159\/000337766","journal-title":"Breast Care (Basel)"},{"issue":"3","key":"2453_CR37","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/s0301-5629(00)00345-8","volume":"27","author":"TC Chao","year":"2001","unstructured":"Chao TC, Luo YF, Chen SC. Color Doppler ultrasound in breast carcinomas: relationship with hormone receptors, DNA ploidy, S-phase fraction, and histopathology. Ultrasound Med Biol. 2001;27(3):351\u20135. https:\/\/doi.org\/10.1016\/s0301-5629(00)00345-8","journal-title":"ULTRASOUND MED BIOL"},{"issue":"1","key":"2453_CR38","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1148\/radiology.196.1.7784555","volume":"196","author":"AT Stavros","year":"1995","unstructured":"Stavros AT, Thickman D, Rapp CL. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology. 1995;196(1):123\u201334. https:\/\/doi.org\/10.1148\/radiology.196.1.7784555","journal-title":"Radiology"}],"container-title":["Radiation Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13014-024-02453-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13014-024-02453-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13014-024-02453-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T15:02:47Z","timestamp":1716822167000},"score":1,"resource":{"primary":{"URL":"https:\/\/ro-journal.biomedcentral.com\/articles\/10.1186\/s13014-024-02453-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2453"],"URL":"https:\/\/doi.org\/10.1186\/s13014-024-02453-2","relation":{},"ISSN":["1748-717X"],"issn-type":[{"value":"1748-717X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"31 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","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 ethical approval of this study was granted by ethics committee of Zhongshan Hospital. The need of informed consent was waived by ethics committee of Zhongshan Hospital due to retrospective nature of the study. All experiments were performed with the Declaration of Helsinki.","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":"63"}}