{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:21:05Z","timestamp":1741666865427,"version":"3.38.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2023NSFSC0711"],"award-info":[{"award-number":["2023NSFSC0711"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chengdu University of Technology 2022 Young and Middle-aged Backbone Teachers Development Funding Program","award":["10912-JXGG2022-02304"],"award-info":[{"award-number":["10912-JXGG2022-02304"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11517-024-03231-8","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T14:54:09Z","timestamp":1730300049000},"page":"733-747","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deformable dose prediction network based on hybrid 2D and 3D convolution for nasopharyngeal carcinoma radiotherapy"],"prefix":"10.1007","volume":"63","author":[{"given":"Yanhua","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangchen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"issue":"2","key":"3231_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/0167-8140(88)90167-3","volume":"12","author":"A Brahme","year":"1988","unstructured":"Brahme A (1988) Optimization of stationary and moving beam radiation therapy techniques. Radiother Oncol 12(2):129\u2013140. https:\/\/doi.org\/10.1016\/0167-8140(88)90167-3","journal-title":"Radiother Oncol"},{"issue":"10","key":"3231_CR2","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1088\/0031-9155\/35\/10\/007","volume":"35","author":"T Bortfeld","year":"1990","unstructured":"Bortfeld T, B\u00fcrkelbach J, Boesecke R, Schlegel W (1990) Methods of image reconstruction from projections applied to conformation radiotherapy. Phys Med Biol 35(10):1423. https:\/\/doi.org\/10.1088\/0031-9155\/35\/10\/007","journal-title":"Phys Med Biol"},{"issue":"10","key":"3231_CR3","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1088\/0031-9155\/34\/10\/002","volume":"34","author":"S Webb","year":"1989","unstructured":"Webb S (1989) Optimisation of conformal radiotherapy dose distributions by simulated annealing. Phys Med Biol 34(10):1349. https:\/\/doi.org\/10.1088\/0031-9155\/34\/10\/002","journal-title":"Phys Med Biol"},{"issue":"9","key":"3231_CR4","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1088\/0031-9155\/40\/9\/004","volume":"40","author":"CX Yu","year":"1995","unstructured":"Yu CX (1995) Intensity-modulated arc therapy with dynamic multileaf collimation: an alternative to tomotherapy. Phys Med Biol 40(9):1435. https:\/\/doi.org\/10.1088\/0031-9155\/40\/9\/004","journal-title":"Phys Med Biol"},{"issue":"1","key":"3231_CR5","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1118\/1.2818738","volume":"35","author":"K Otto","year":"2008","unstructured":"Otto K (2008) Volumetric modulated arc therapy: IMRT in a single gantry arc. Med Phys 35(1):310\u2013317. https:\/\/doi.org\/10.1118\/1.2818738","journal-title":"Med Phys"},{"issue":"6","key":"3231_CR6","doi-asserted-by":"publisher","first-page":"2328","DOI":"10.1118\/1.3132234","volume":"36","author":"K Bzdusek","year":"2009","unstructured":"Bzdusek K, Friberger H, Eriksson K, H\u00e5rdemark B, Robinson D, Kaus M (2009) Development and evaluation of an efficient approach to volumetric arc therapy planning. Med Phys 36(6):2328\u20132339. https:\/\/doi.org\/10.1118\/1.3132234","journal-title":"Med Phys"},{"key":"3231_CR7","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.radonc.2022.03.012","volume":"170","author":"M Yue","year":"2022","unstructured":"Yue M, Xue X, Wang Z, Lambo RL, Zhao W, Xie Y, Cai J, Qin W (2022) Dose prediction via distance-guided deep learning: initial development for nasopharyngeal carcinoma radiotherapy. Radiother Oncol 170:198\u2013204. https:\/\/doi.org\/10.1016\/j.radonc.2022.03.012","journal-title":"Radiother Oncol"},{"issue":"1","key":"3231_CR8","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1038\/s41598-018-37741-x","volume":"9","author":"D Nguyen","year":"2019","unstructured":"Nguyen D, Long T, Jia X, Lu W, Gu X, Iqbal Z, Jiang S (2019) A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci Rep 9(1):1076. https:\/\/doi.org\/10.1038\/s41598-018-37741-x","journal-title":"Sci Rep"},{"issue":"1","key":"3231_CR9","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1118\/1.4938583","volume":"43","author":"S Shiraishi","year":"2016","unstructured":"Shiraishi S, Moore KL (2016) Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy. Med Phys 43(1):378\u2013387. https:\/\/doi.org\/10.1118\/1.4938583","journal-title":"Med Phys"},{"key":"3231_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102339","volume":"77","author":"B Zhan","year":"2022","unstructured":"Zhan B, Xiao J, Cao C, Peng X, Zu C, Zhou J, Wang Y (2022) Multi-constraint generative adversarial network for dose prediction in radiotherapy. Med Image Anal 77:102339. https:\/\/doi.org\/10.1016\/j.media.2021.102339","journal-title":"Med Image Anal"},{"key":"3231_CR11","doi-asserted-by":"publisher","unstructured":"Jiao Z, Peng X, Wang Y, Xiao J, Nie D, Wu X, Wang X, Zhou J, Shen D (2023) TransDose: transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification. Med Image Anal 89:102902. https:\/\/doi.org\/10.1016\/j.media.2023.102902","DOI":"10.1016\/j.media.2023.102902"},{"key":"3231_CR12","doi-asserted-by":"publisher","unstructured":"Hu C, Wang H, Zhang W, Xie Y, Jiao L, Cui S (2023) TrDosePred: a deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy. J Appl Clin Med Phys 24(7):e13942. https:\/\/doi.org\/10.1002\/acm2.13942","DOI":"10.1002\/acm2.13942"},{"issue":"1","key":"3231_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa9a30","volume":"63","author":"PDH Wall","year":"2018","unstructured":"Wall PDH, Carver RL, Fontenot JD (2018) An improved distance-to-dose correlation for predicting bladder and rectum dose-volumes in knowledge-based VMAT planning for prostate cancer. Phys Med Biol 63(1):015035. https:\/\/doi.org\/10.1088\/1361-6560\/aa9a30","journal-title":"Phys Med Biol"},{"issue":"2","key":"3231_CR14","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1002\/mp.13334","volume":"46","author":"M Ma","year":"2019","unstructured":"Ma M, Kovalchuk N, Buyyounouski MK, Xing L, Yang Y (2019) Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning. Med Phys 46(2):857\u2013867. https:\/\/doi.org\/10.1002\/mp.13334","journal-title":"Med Phys"},{"issue":"1","key":"3231_CR15","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1002\/mp.13271","volume":"46","author":"J Fan","year":"2019","unstructured":"Fan J, Wang J, Chen Z, Hu C, Zhang Z, Hu W (2019) Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique. Med Phys 46(1):370\u2013381. https:\/\/doi.org\/10.1002\/mp.13271","journal-title":"Med Phys"},{"key":"3231_CR16","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.neucom.2021.11.061","volume":"488","author":"J Wang","year":"2022","unstructured":"Wang J, Hu J, Song Y, Wang Q, Zhang X, Bai S, Yi Z (2022) VMAT dose prediction in radiotherapy by using progressive refinement UNet. Neurocomputing 488:528\u2013539. https:\/\/doi.org\/10.1016\/j.neucom.2021.11.061","journal-title":"Neurocomputing"},{"issue":"23","key":"3231_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaef74","volume":"63","author":"V Kearney","year":"2018","unstructured":"Kearney V, Chan JW, Haaf S, Descovich M, Solberg TD (2018) DoseNet: a volumetric dose prediction algorithm using 3D fully-convolutional neural networks. Phys Med Biol 63(23):235022. https:\/\/doi.org\/10.1088\/1361-6560\/aaef74","journal-title":"Phys Med Biol"},{"issue":"9","key":"3231_CR18","doi-asserted-by":"publisher","first-page":"5574","DOI":"10.1002\/mp.15034","volume":"48","author":"S Liu","year":"2021","unstructured":"Liu S, Zhang J, Li T, Yan H, Liu J (2021) Technical note: a cascade 3D U-net for dose prediction in radiotherapy. Med Phys 48(9):5574\u20135582. https:\/\/doi.org\/10.1002\/mp.15034","journal-title":"Med Phys"},{"key":"3231_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neucom.2020.06.146","volume":"438","author":"H Mei","year":"2021","unstructured":"Mei H, Lei W, Gu R, Ye S, Sun Z, Zhang S, Wang G (2021) Automatic segmentation of gross target volume of nasopharynx cancer using ensemble of multiscale deep neural networks with spatial attention. Neurocomputing 438:211\u2013222. https:\/\/doi.org\/10.1016\/j.neucom.2020.06.146","journal-title":"Neurocomputing"},{"key":"3231_CR20","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2021.01.135","volume":"442","author":"W Lei","year":"2021","unstructured":"Lei W, Mei H, Sun Z, Ye S, Gu R, Wang H, Huang R, Zhang S, Zhang S, Wang G (2021) Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss. Neurocomputing 442:184\u2013199. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.135","journal-title":"Neurocomputing"},{"key":"3231_CR21","doi-asserted-by":"publisher","unstructured":"Zhang Y, Liao Q, Ding L, Zhang J (2022) Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: an empirical study of 2.5D solutions. Comput Med Imaging Graph 99:102088. https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102088","DOI":"10.1016\/j.compmedimag.2022.102088"},{"key":"3231_CR22","first-page":"764","volume":"2017","author":"J Dai","year":"2017","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2017:764\u2013773","journal-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)"},{"issue":"12","key":"3231_CR23","doi-asserted-by":"publisher","first-page":"6216","DOI":"10.1002\/mp.14391","volume":"47","author":"MH Vu","year":"2020","unstructured":"Vu MH, Grimbergen G, Nyholm T, L\u00f6fstedt T (2020) Evaluation of multislice inputs to convolutional neural networks for medical image segmentation. Med Phys 47(12):6216\u20136231. https:\/\/doi.org\/10.1002\/mp.14391","journal-title":"Med Phys"},{"key":"3231_CR24","first-page":"7132","volume":"2018","author":"J Hu","year":"2018","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) 2018:7132\u20137141","journal-title":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"3231_CR25","doi-asserted-by":"publisher","unstructured":"Zhuang Y, Han J, Chen L, Liu X (2019) Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals. Phys Med Biol 64(23):23NT03. https:\/\/doi.org\/10.1088\/1361-6560\/ab5433","DOI":"10.1088\/1361-6560\/ab5433"},{"key":"3231_CR26","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, pp 424-432. Springer","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"3231_CR27","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 2016, pp 565\u2013571. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"issue":"1","key":"3231_CR28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13014-024-02401-0","volume":"19","author":"W Cai","year":"2024","unstructured":"Cai W, Ding S, Li H, Zhou X, Dou W, Zhou L, Song T, Li Y (2024) Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma. Radiat Oncol 19(1):39. https:\/\/doi.org\/10.1186\/s13014-024-02401-0","journal-title":"Radiat Oncol"},{"key":"3231_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctro.2024.100792","volume":"47","author":"C Jiang","year":"2024","unstructured":"Jiang C, Ji T, Qiao Q (2024) Application and progress of artificial intelligence in radiation therapy dose prediction. Clin Transl Radiat Oncol 47:100792. https:\/\/doi.org\/10.1016\/j.ctro.2024.100792","journal-title":"Clin Transl Radiat Oncol"},{"key":"3231_CR30","doi-asserted-by":"publisher","first-page":"153303382412565","DOI":"10.1177\/15330338241256594","volume":"23","author":"Y Wang","year":"2024","unstructured":"Wang Y, Piao Z, Gu H, Chen M, Zhang D, Zhu J (2024) Deep learning-based prediction of radiation therapy dose distributions in nasopharyngeal carcinomas: a preliminary study incorporating multiple features including images, structures, and dosimetry. Technol Cancer Res Treat 23:15330338241256594. https:\/\/doi.org\/10.1177\/15330338241256594","journal-title":"Technol Cancer Res Treat"},{"key":"3231_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01296-3","author":"X Li","year":"2024","unstructured":"Li X, Liu Y, Zhao F, Yang F, Luo W (2024) Transformer-integrated hybrid convolutional neural network for dose prediction in nasopharyngeal carcinoma radiotherapy. J Imaging Inform Med. https:\/\/doi.org\/10.1007\/s10278-024-01296-3","journal-title":"J Imaging Inform Med"},{"key":"3231_CR32","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.ejmp.2021.02.021","volume":"83","author":"N Hedden","year":"2021","unstructured":"Hedden N, Xu H (2021) Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models. Phys Med 83:101\u2013107. https:\/\/doi.org\/10.1016\/j.ejmp.2021.02.021","journal-title":"Phys Med"},{"issue":"1","key":"3231_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrras.2023.100757","volume":"17","author":"J Liu","year":"2024","unstructured":"Liu J, Zhang X, Cheng X, Sun L (2024) A deep learning-based dose prediction method for evaluation of radiotherapy treatment planning. Journal of Radiation Research and Applied Sciences 17(1):100757. https:\/\/doi.org\/10.1016\/j.jrras.2023.100757","journal-title":"Journal of Radiation Research and Applied Sciences"},{"key":"3231_CR34","doi-asserted-by":"publisher","unstructured":"Liu Y, Chen Z, Wang J, Wang X, Qu B, Ma L, Zhao W, Zhang G, Xu S (2021) Dose prediction using a three-dimensional convolutional neural network for nasopharyngeal carcinoma with tomotherapy. Front Oncol 11:752007. https:\/\/doi.org\/10.3389\/fonc.2021.752007","DOI":"10.3389\/fonc.2021.752007"},{"key":"3231_CR35","doi-asserted-by":"publisher","unstructured":"Mashayekhi M, Tapia IR, Balagopal A, Zhong X, Barkousaraie AS, McBeth R, Lin MH, Jiang S, Nguyen D (2022) Site-agnostic\u00a03D dose distribution prediction with deep learning neural networks. Med Phys 49(3):1391\u20131406. https:\/\/doi.org\/10.1002\/mp.15461","DOI":"10.1002\/mp.15461"},{"issue":"1","key":"3231_CR36","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s13014-023-02287-4","volume":"18","author":"Y Li","year":"2023","unstructured":"Li Y, Cai W, Xiao F, Zhou X, Cai J, Zhou L, Dou W, Song T (2023) Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT. Radiat Oncol 18(1):110. https:\/\/doi.org\/10.1186\/s13014-023-02287-4","journal-title":"Radiat Oncol"},{"issue":"8","key":"3231_CR37","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1007\/s00432-022-03974-7","volume":"148","author":"DH Xie","year":"2022","unstructured":"Xie DH, Wu Z, Li WZ, Cheng WQ, Tao YL, Wang L, Lv SW, Lin FF, Cui NJ, Zhao C, Ma J, Huang SM, Lu TX, Han YQ, Su Y (2022) Individualized clinical target volume delineation and efficacy analysis in unilateral nasopharyngeal carcinoma treated with intensity-modulated radiotherapy (IMRT): 10-year summary. J Cancer Res Clin Oncol 148(8):1931\u20131942. https:\/\/doi.org\/10.1007\/s00432-022-03974-7","journal-title":"J Cancer Res Clin Oncol"},{"key":"3231_CR38","doi-asserted-by":"publisher","unstructured":"Wang X, Huang N, Yip PL, Wang J, Huang R, Sun Z, Kang D, He Q, Deng X, Zhao C, Chua MLK, Han F (2024) The individualized delineation of clinical target volume for primary nasopharyngeal carcinoma based on invasion risk of substructures: a prospective, real-world study with a large population. Radiother Oncol 194:11054. https:\/\/doi.org\/10.1016\/j.radonc.2024.110154","DOI":"10.1016\/j.radonc.2024.110154"},{"key":"3231_CR39","doi-asserted-by":"publisher","unstructured":"Liu Y, Yuan X, Jiang X, Wang P, Kou J, Wang H, Liu M (2021) Dilated adversarial U-net network for automatic gross tumor volume segmentation of nasopharyngeal carcinoma. Appl Soft Comput 111:107722. https:\/\/doi.org\/10.1016\/j.asoc.2021.107722","DOI":"10.1016\/j.asoc.2021.107722"},{"issue":"7","key":"3231_CR40","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1007\/s11517-024-03064-5","volume":"62","author":"C Ozdemir","year":"2024","unstructured":"Ozdemir C, Dogan Y (2024) Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics. Med Biol Eng Comput 62(7):2165\u20132176. https:\/\/doi.org\/10.1007\/s11517-024-03064-5","journal-title":"Med Biol Eng Comput"},{"key":"3231_CR41","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: Computer vision-ECCV 2016: 14th european conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14, pp 694-711. Springer","DOI":"10.1007\/978-3-319-46475-6_43"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03231-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03231-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03231-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T15:20:41Z","timestamp":1741620041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03231-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3231"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03231-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"30 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 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":"Approval was obtained from the Ethics Committee of Sichuan Cancer Hospital, China (Ethical Approval Number: SCCHE-02-2022-003).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}