{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T04:31:12Z","timestamp":1789792272392,"version":"4.0.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100014717","name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82203197"],"award-info":[{"award-number":["82203197"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Sichuan Provincial Cadre Health Research Project","award":["2023-803"],"award-info":[{"award-number":["2023-803"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-84804-3","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T15:04:21Z","timestamp":1738681461000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning"],"prefix":"10.1038","volume":"15","author":[{"given":"Wenjun","family":"Liao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangde","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,4]]},"reference":[{"issue":"10318","key":"84804_CR1","doi-asserted-by":"publisher","first-page":"2289","DOI":"10.1016\/S0140-6736(21)01550-6","volume":"398","author":"MD Mody","year":"2021","unstructured":"Mody, M. D., Rocco, J. W., Yom, S. S. & Haddad, R. I. Saba NFJTL: Head and neck cancer. Lancet 398(10318), 2289\u20132299 (2021).","journal-title":"Lancet"},{"issue":"12","key":"84804_CR2","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1002\/hed.23814","volume":"37","author":"R De Bree","year":"2015","unstructured":"De Bree, R. et al. Advances in diagnostic modalities to detect occult lymph node metastases in head and neck squamous cell carcinoma. Head Neck. 37(12), 1829\u20131839 (2015).","journal-title":"Head Neck."},{"issue":"3","key":"84804_CR3","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2214\/ajr.174.3.1740837","volume":"174","author":"PM Som","year":"2000","unstructured":"Som, P. M. & Curtin, H. D. Mancuso AAJAJoR: Imaging-based nodal classification for evaluation of neck metastatic adenopathy. AJR Am. J. Roentgenol. 174(3), 837\u2013844 (2000).","journal-title":"AJR Am. J. Roentgenol."},{"issue":"1","key":"84804_CR4","first-page":"163","volume":"22","author":"K Yonetsu","year":"2001","unstructured":"Yonetsu, K., Sumi, M., Izumi, M., Ohki, M. & Eida, S. Nakamura TJAjon: Contribution of Doppler sonography blood flow information to the diagnosis of metastatic cervical nodes in patients with head and neck cancer: Assessment in relation to anatomic levels of the neck. AJNR Am. J. Neuroradiol. 22(1), 163\u2013169 (2001).","journal-title":"AJNR Am. J. Neuroradiol."},{"issue":"1","key":"84804_CR5","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1148\/radiol.13131470","volume":"271","author":"J-L Roh","year":"2014","unstructured":"Roh, J.-L. et al. 18F fluorodeoxyglucose PET\/CT in head and neck squamous cell carcinoma with negative neck palpation findings: A prospective study. Radiology 271(1), 153\u2013161 (2014).","journal-title":"Radiology"},{"issue":"2","key":"84804_CR6","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.ejca.2008.10.028","volume":"45","author":"L Schwartz","year":"2009","unstructured":"Schwartz, L. et al. Eisenhauer EJEjoc: Evaluation of lymph nodes with RECIST 1.1. Eur. J. Cancer 45(2), 261\u2013267 (2009).","journal-title":"Eur. J. Cancer"},{"issue":"12","key":"84804_CR7","doi-asserted-by":"publisher","first-page":"6338","DOI":"10.1118\/1.3515459","volume":"37","author":"A Chen","year":"2010","unstructured":"Chen, A., Deeley, M. A., Niermann, K. J. & Moretti, L. Dawant BMJMp: Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images. Med. Phys. 37(12), 6338\u20136346 (2010).","journal-title":"Med. Phys."},{"key":"84804_CR8","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s11282-020-00449-8","volume":"37","author":"Y Ariji","year":"2021","unstructured":"Ariji, Y. et al. Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: A preliminary study. Oral Radiol. 37, 290\u2013296 (2021).","journal-title":"Oral Radiol."},{"issue":"1","key":"84804_CR9","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1148\/radiol.2018180237","volume":"290","author":"JG Nam","year":"2019","unstructured":"Nam, J. G. et al. Development and validation of deep learning\u2013based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology 290(1), 218\u2013228 (2019).","journal-title":"Radiology"},{"issue":"1","key":"84804_CR10","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1038\/s41467-024-45325-9","volume":"15","author":"H Ying","year":"2024","unstructured":"Ying, H. et al. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat. Commun. 15(1), 1131 (2024).","journal-title":"Nat. Commun."},{"key":"84804_CR11","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.radonc.2021.04.019","volume":"160","author":"X Chen","year":"2021","unstructured":"Chen, X. et al. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother. Oncol. 160, 175\u2013184 (2021).","journal-title":"Radiother. Oncol."},{"key":"84804_CR12","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1016\/j.ijrobp.2023.05.034","volume":"15","author":"W Liao","year":"2023","unstructured":"Liao, W. et al. Comprehensive evaluation of a deep learning model for automatic organs at risk segmentation on heterogeneous computed tomography images for abdominal radiation therapy. Int. J. Radiat. Oncol. Biol. Phy. 15, 994\u20131006 (2023).","journal-title":"Int. J. Radiat. Oncol. Biol. Phy."},{"issue":"10","key":"84804_CR13","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","volume":"1","author":"H Tang","year":"2019","unstructured":"Tang, H., Chen, X., Liu, Y. & Lu, Z. Xie XJNMI: Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat. Mach Intell. 1(10), 480\u2013491 (2019).","journal-title":"Nat. Mach Intell."},{"issue":"1","key":"84804_CR14","doi-asserted-by":"publisher","first-page":"9186","DOI":"10.1038\/s41467-024-53387-y","volume":"15","author":"Y Oh","year":"2024","unstructured":"Oh, Y. et al. Ye JCJNC: LLM-driven multimodal target volume contouring in radiation oncology. Nat. Commun. 15(1), 9186 (2024).","journal-title":"Nat. Commun."},{"key":"84804_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2024.105311","volume":"107","author":"Z Li","year":"2024","unstructured":"Li, Z. et al. Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: Multicentre study. EBioMedicine 107, 105311 (2024).","journal-title":"EBioMedicine"},{"issue":"3","key":"84804_CR16","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.240575","volume":"312","author":"T Ramtohul","year":"2024","unstructured":"Ramtohul, T. et al. Use of pretreatment perfusion MRI\u2013based intratumoral heterogeneity to predict pathologic response of triple-negative breast cancer to neoadjuvant chemoimmunotherapy. Radiology 312(3), e240575 (2024).","journal-title":"Radiology"},{"key":"84804_CR17","doi-asserted-by":"crossref","unstructured":"Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J. & Turkbey, E. Summers RM: A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14\u201318, 2014, Proceedings, Part I 17: 2014: Springer; 2014: 520\u2013527.","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"84804_CR18","doi-asserted-by":"crossref","unstructured":"Kitasaka, T., Tsujimura, Y., Nakamura, Y., Mori, K., Suenaga, Y., Ito, M. & Nawano S: Automated extraction of lymph nodes from 3-D abdominal CT images using 3-D minimum directional difference filter. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29-November 2, 2007, Proceedings, Part II 10: 2007: Springer; 2007: 336\u2013343.","DOI":"10.1007\/978-3-540-75759-7_41"},{"issue":"2","key":"84804_CR19","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/TMI.2011.2168234","volume":"31","author":"A Barbu","year":"2011","unstructured":"Barbu, A., Suehling, M., Xu, X., Liu, D. & Zhou, S. K. Comaniciu DJIToMI: Automatic detection and segmentation of lymph nodes from CT data. IEEE Trans. Med. Imaging 31(2), 240\u2013250 (2011).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"84804_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102312","volume":"111","author":"A Mehrtash","year":"2024","unstructured":"Mehrtash, A. et al. Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision. Comput. Med. Imaging Graph. 111, 102312 (2024).","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"84804_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s41747-023-00360-x","volume":"7","author":"M Rinneburger","year":"2023","unstructured":"Rinneburger, M. et al. Laqua FCJERE: Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network. Eur. Radiol. Exp. 7(1), 45 (2023).","journal-title":"Eur. Radiol. Exp."},{"key":"84804_CR22","doi-asserted-by":"publisher","first-page":"102780","DOI":"10.1016\/j.ebiom.2020.102780","volume":"56","author":"X Zhao","year":"2020","unstructured":"Zhao, X. et al. Deep learning\u2013based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study. EBioMedicine 56, 102780 (2020).","journal-title":"EBioMedicine"},{"issue":"2","key":"84804_CR23","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s11547-023-01747-x","volume":"129","author":"Y Cao","year":"2024","unstructured":"Cao, Y. et al. Mao LJLrm: LNAS: A clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images. Radiol. Med. 129(2), 229\u2013238 (2024).","journal-title":"Radiol. Med."},{"issue":"6","key":"84804_CR24","doi-asserted-by":"publisher","first-page":"4303","DOI":"10.1007\/s00330-022-09355-5","volume":"33","author":"X Xu","year":"2023","unstructured":"Xu, X. et al. Deep learning assisted contrast-enhanced CT-based diagnosis of cervical lymph node metastasis of oral cancer: A retrospective study of 1466 cases. Eur. Radiol. 33(6), 4303\u20134312 (2023).","journal-title":"Eur. Radiol."},{"key":"84804_CR25","first-page":"47","volume":"36","author":"N Taku","year":"2022","unstructured":"Taku, N. et al. Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network. Clin. Transl. Radiat. Oncol. 36, 47\u201355 (2022).","journal-title":"Clin. Transl. Radiat. Oncol."},{"key":"84804_CR26","doi-asserted-by":"crossref","unstructured":"Dornheim, J., Seim, H., Preim, B., Hertel, I. & Strauss, G. Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1\u20136, 2006 Proceedings, Part II 9: 2006: Springer; 2006: 904\u2013911.","DOI":"10.1007\/11866763_111"},{"key":"84804_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1748-717X-8-229","volume":"8","author":"C Sj\u00f6berg","year":"2013","unstructured":"Sj\u00f6berg, C. et al. Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients. Radiat. Oncol. 8, 1\u20137 (2013).","journal-title":"Radiat. Oncol."},{"issue":"1","key":"84804_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.radonc.2015.02.013","volume":"115","author":"J Zhang","year":"2015","unstructured":"Zhang, J. et al. Level IIb CTV delineation based on cervical fascia anatomy in nasopharyngeal cancer. Radiother. Oncol. 115(1), 46\u201349 (2015).","journal-title":"Radiother. Oncol."},{"key":"84804_CR29","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.radonc.2022.04.026","volume":"172","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y. et al. Level Ib CTV delineation in nasopharyngeal carcinoma based on lymph node distribution and topographic anatomy. Radiother. Oncol. 172, 10\u201317 (2022).","journal-title":"Radiother. Oncol."},{"key":"84804_CR30","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.radonc.2022.10.036","volume":"177","author":"Z Zhong","year":"2022","unstructured":"Zhong, Z. et al. Lymph drainage and cervical fascia anatomy-oriented differential nodal CTV delineation at the supraclavicular region for esophageal cancer and nasopharyngeal cancer. Radiother. Oncol. 177, 113\u2013120 (2022).","journal-title":"Radiother. Oncol."},{"issue":"3","key":"84804_CR31","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.meddos.2019.12.004","volume":"45","author":"Y Nakajima","year":"2020","unstructured":"Nakajima, Y. et al. Evaluation of the effect of user-guided deformable image registration of thoracic images on registration accuracy among users. Med. Dosim. 45(3), 206\u2013212 (2020).","journal-title":"Med. Dosim."},{"issue":"3","key":"84804_CR32","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1120\/jacmp.v17i3.5735","volume":"17","author":"J Pukala","year":"2016","unstructured":"Pukala, J. et al. Meeks SLJJoacmp: Benchmarking of five commercial deformable image registration algorithms for head and neck patients. Appl. Clin. Med. Phys. 17(3), 25\u201340 (2016).","journal-title":"Appl. Clin. Med. Phys."},{"issue":"1","key":"84804_CR33","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41746-023-00811-0","volume":"6","author":"S-C Huang","year":"2023","unstructured":"Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P. & Yeung, S. Chaudhari ASJNDM: Self-supervised learning for medical image classification: A systematic review and implementation guidelines. NPJ. Digit. Med. 6(1), 74 (2023).","journal-title":"NPJ. Digit. Med."},{"issue":"6","key":"84804_CR34","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1038\/s41551-023-01049-7","volume":"7","author":"S Azizi","year":"2023","unstructured":"Azizi, S. et al. Strachan PJNBE: Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nat. Biomed. Eng. 7(6), 756\u2013779 (2023).","journal-title":"Nat. Biomed. Eng."},{"key":"84804_CR35","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J. et al. Yang SJRAI: Totalsegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. https:\/\/doi.org\/10.1148\/ryai.230024 (2023).","journal-title":"Radiol. Artif. Intell."},{"key":"84804_CR36","unstructured":"Luo, X., Fu, J., Zhong, Y., Liu, S., Han, B., Astaraki, M., Bendazzoli, S., Toma-Dasu, I., Ye, Y. & Chen, Z. J. Segrap2023: A benchmark of organs-at-risk and gross tumor volume segmentation for radiotherapy planning of nasopharyngeal carcinoma (2023)."},{"issue":"2","key":"84804_CR37","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P. F., Kohl, S. A. & Petersen, J. Maier-Hein KHJNm: nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021).","journal-title":"Nat. Methods"},{"issue":"3","key":"84804_CR38","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.ijrobp.2009.09.023","volume":"77","author":"LJ Stapleford","year":"2010","unstructured":"Stapleford, L. J. et al. Fox TJIJoROBP: Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. Int. J. Radiat. Oncol. Biol. Phys. 77(3), 959\u2013966 (2010).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"84804_CR39","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.radonc.2021.05.003","volume":"160","author":"MV Sherer","year":"2021","unstructured":"Sherer, M. V. et al. Oncology: Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother. Oncol. 160, 185\u2013191 (2021).","journal-title":"Radiother. Oncol."},{"issue":"9","key":"84804_CR40","doi-asserted-by":"publisher","first-page":"2513","DOI":"10.1109\/TMI.2023.3262680","volume":"42","author":"Q Zhao","year":"2023","unstructured":"Zhao, Q. et al. Wang GJIToMI: Efficient multi-organ segmentation from 3D abdominal CT images with lightweight network and knowledge distillation. IEEE Trans. Med. Imaging 42(9), 2513\u20132523 (2023).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"84804_CR41","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L. et al. Xu DJItomi: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531\u20132540 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"84804_CR42","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1016\/j.ijrobp.2020.10.005","volume":"109","author":"CE Cardenas","year":"2021","unstructured":"Cardenas, C. E. et al. Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based approach. Int. J. Radiat. Oncol. Biol. Phys. 109(3), 801\u2013812 (2021).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84804-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84804-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84804-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T15:04:29Z","timestamp":1738681469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84804-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,4]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["84804"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-84804-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,4]]},"assertion":[{"value":"2 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Due to the retrospective nature of the study, the Ethics Committee for Medical Research of Sichuan Cancer Hospital waived the need of obtaining informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"4250"}}