{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:33:26Z","timestamp":1772120006913,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473033"],"award-info":[{"award-number":["62473033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L231012"],"award-info":[{"award-number":["L231012"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s11760-025-04156-x","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T01:56:09Z","timestamp":1747014969000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inter-patch spatio-temporal relation prediction for video anomaly detection"],"prefix":"10.1007","volume":"19","author":[{"given":"Hao","family":"Shen","sequence":"first","affiliation":[]},{"given":"Lu","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Linna","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wanru","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yigang","family":"Cen","sequence":"additional","affiliation":[]},{"given":"Gaoyun","family":"An","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"4156_CR1","doi-asserted-by":"publisher","unstructured":"Kim, D., Cho, D., Kweon, I.S.: Self-supervised video representation learning with space-time cubic puzzles. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8545\u20138552 (2019) https:\/\/doi.org\/10.1609\/aaai.v33i01.33018545","DOI":"10.1609\/aaai.v33i01.33018545"},{"key":"4156_CR2","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"4156_CR3","doi-asserted-by":"publisher","unstructured":"Cai, R., Zhang, H., Liu, W., Gao, S., Hao, Z.: Appearance-motion memory consistency network for video anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 938\u2013946 (2022) https:\/\/doi.org\/10.1609\/aaai.v35i2.16177","DOI":"10.1609\/aaai.v35i2.16177"},{"key":"4156_CR4","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/tifs.2019.2900907","volume":"14","author":"JT Zhou","year":"2019","unstructured":"Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y., Goh, R.S.M.: AnomalyNet: an anomaly detection network for video surveillance. IEEE Trans Inf Forens Secur 14, 2537\u20132550 (2019). https:\/\/doi.org\/10.1109\/tifs.2019.2900907","journal-title":"IEEE Trans Inf Forens Secur"},{"key":"4156_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2019.2933554","author":"P Wu","year":"2019","unstructured":"Wu, P., Liu, J., Shen, F.: A deep one-class neural network for anomalous event detection in complex scenes. IEEE Trans. Neural Netw. Learn. Syst. (2019). https:\/\/doi.org\/10.1109\/tnnls.2019.2933554","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"4156_CR6","doi-asserted-by":"publisher","unstructured":"Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: Learning regularity in skeleton trajectories for anomaly detection in videos. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019). https:\/\/doi.org\/10.1109\/cvpr.2019.01227","DOI":"10.1109\/cvpr.2019.01227"},{"key":"4156_CR7","doi-asserted-by":"publisher","unstructured":"Sun, C., Jia, Y., Hu, Y., Wu, Y.: Scene-aware context reasoning for unsupervised abnormal event detection in videos. In: Proceedings of the 28th ACM International Conference on Multimedia (2020). https:\/\/doi.org\/10.1145\/3394171.3413887","DOI":"10.1145\/3394171.3413887"},{"key":"4156_CR8","doi-asserted-by":"publisher","unstructured":"Nguyen, T.N., Meunier, J.: Anomaly detection in video sequence with appearance-motion correspondence. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) (2019). https:\/\/doi.org\/10.1109\/iccv.2019.00136","DOI":"10.1109\/iccv.2019.00136"},{"key":"4156_CR9","doi-asserted-by":"publisher","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Van Den\u00a0Hengel, A.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) (2019). https:\/\/doi.org\/10.1109\/iccv.2019.00179","DOI":"10.1109\/iccv.2019.00179"},{"key":"4156_CR10","doi-asserted-by":"crossref","unstructured":"Chang, Y., Tu, Z., Xie, W., Yuan, J.: Clustering driven deep autoencoder for video anomaly detection. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV 16, pp. 329\u2013345. Springer (2020)","DOI":"10.1007\/978-3-030-58555-6_20"},{"key":"4156_CR11","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14372\u201314381 (2020)","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"4156_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108336","author":"Y Zhong","year":"2022","unstructured":"Zhong, Y., Chen, X., Jiang, J., Ren, F.: A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos. Pattern Recogn. (2022). https:\/\/doi.org\/10.1016\/j.patcog.2021.108336","journal-title":"Pattern Recogn."},{"key":"4156_CR13","doi-asserted-by":"publisher","unstructured":"Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.01438","DOI":"10.1109\/cvpr42600.2020.01438"},{"key":"4156_CR14","doi-asserted-by":"publisher","unstructured":"Ye, M., Peng, X., Gan, W., Wu, W., Qiao, Y.: Anopcn. In: Proceedings of the 27th ACM International Conference on Multimedia (2019). https:\/\/doi.org\/10.1145\/3343031.3350899","DOI":"10.1145\/3343031.3350899"},{"key":"4156_CR15","volume-title":"Robust Unsupervised Video Anomaly Detection by Multi-path Frame Prediction","author":"X Wang","year":"2020","unstructured":"Wang, X., Che, Z., Jiang, B., Xiao, N., Yang, K., Tang, J., Ye, J., Wang, J., Qi, Q.: Robust Unsupervised Video Anomaly Detection by Multi-path Frame Prediction. Cornell University-arXiv (2020)"},{"key":"4156_CR16","doi-asserted-by":"publisher","unstructured":"Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., Yang, J.: Learning normal dynamics in videos with meta prototype network. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01517","DOI":"10.1109\/cvpr46437.2021.01517"},{"key":"4156_CR17","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wu, P., Liu, J., Liu, X.: Dynamic local aggregation network with adaptive clusterer for anomaly detection. In: European Conference on Computer Vision, pp. 404\u2013421. Springer (2022)","DOI":"10.1007\/978-3-031-19772-7_24"},{"key":"4156_CR18","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.patrec.2019.11.024","volume":"129","author":"Y Tang","year":"2020","unstructured":"Tang, Y., Zhao, L., Zhang, S., Gong, C., Li, G., Yang, J.: Integrating prediction and reconstruction for anomaly detection. Pattern Recogn. Lett. 129, 123\u2013130 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"4156_CR19","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, J., Wu, Z., Wu, P., Liu, X.: Video event restoration based on keyframes for video anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14592\u201314601 (2023)","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"4156_CR20","doi-asserted-by":"publisher","unstructured":"Yu, G., Wang, S., Cai, Z., Zhu, E., Xu, C., Yin, J., Kloft, M.: Cloze test helps: Effective video anomaly detection via learning to complete video events. In: Proceedings of the 28th ACM International Conference on Multimedia (2020). https:\/\/doi.org\/10.1145\/3394171.3413973 . http:\/\/dx.doi.org\/10.1145\/3394171.3413973","DOI":"10.1145\/3394171.3413973"},{"key":"4156_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zou, Y., Zhang, Z.: Cluster attention contrast for video anomaly detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2463\u20132471 (2020)","DOI":"10.1145\/3394171.3413529"},{"key":"4156_CR22","doi-asserted-by":"publisher","unstructured":"Georgescu, M.-I., Barbalau, A., Ionescu, R.T., Shahbaz\u00a0Khan, F., Popescu, M., Shah, M.: Anomaly detection in video via self-supervised and multi-task learning. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01255 . http:\/\/dx.doi.org\/10.1109\/cvpr46437.2021.01255","DOI":"10.1109\/cvpr46437.2021.01255"},{"key":"4156_CR23","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, Y., Qin, J., Zhang, D., Bao, X., Huang, D.: Video anomaly detection by solving decoupled spatio-temporal jigsaw puzzles. In: European Conference on Computer Vision, pp. 494\u2013511. Springer (2022)","DOI":"10.1007\/978-3-031-20080-9_29"},{"key":"4156_CR24","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)"},{"key":"4156_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"4156_CR26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4156_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4156_CR28","doi-asserted-by":"publisher","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.86 . http:\/\/dx.doi.org\/10.1109\/cvpr.2016.86","DOI":"10.1109\/cvpr.2016.86"},{"key":"4156_CR29","doi-asserted-by":"crossref","unstructured":"Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: CVPR 2011, pp. 3449\u20133456. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"4156_CR30","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection\u2013a new baseline. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6536\u20136545 (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"4156_CR31","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhao, C., Wu, Z.: A video anomaly detection framework based on appearance-motion semantics representation consistency. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10097199"},{"key":"4156_CR32","unstructured":"Cao, C., Lu, Y., Zhang, Y.: Context recovery and knowledge retrieval: a novel two-stream framework for video anomaly detection. arXiv preprint arXiv:2209.02899 (2022)"},{"key":"4156_CR33","unstructured":"Georgescu, M.-I., Ionescu, R., Khan, F., Popescu, M., Shah, M.: A background-agnostic framework with adversarial training for abnormal event detection in video. IEEE Trans. Pattern Anal. Mach. Intell. (2023)"},{"key":"4156_CR34","unstructured":"Reiss, T., Hoshen, Y.: Attribute-based representations for accurate and interpretable video anomaly detection. arXiv preprint arXiv:2212.00789 (2022)"},{"key":"4156_CR35","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"4156_CR36","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740\u2013755. Springer (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"4156_CR37","doi-asserted-by":"publisher","unstructured":"Feng, X., Song, D., Chen, Y., Chen, Z., Ni, J., Chen, H.: Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection. In: Proceedings of the 29th ACM International Conference on Multimedia (2021). https:\/\/doi.org\/10.1145\/3474085.3475693 . http:\/\/dx.doi.org\/10.1145\/3474085.3475693","DOI":"10.1145\/3474085.3475693"},{"key":"4156_CR38","doi-asserted-by":"publisher","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975\u20131981 (2010). https:\/\/doi.org\/10.1109\/CVPR.2010.5539872","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"4156_CR39","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2720\u20132727 (2013)","DOI":"10.1109\/ICCV.2013.338"},{"key":"4156_CR40","doi-asserted-by":"crossref","unstructured":"Liu, W., W.\u00a0Luo, D.L., Gao, S.: Future frame prediction for anomaly detection\u2014a new baseline. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"4156_CR41","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.neucom.2023.03.008","volume":"534","author":"M Astrid","year":"2023","unstructured":"Astrid, M., Zaheer, M.Z., Lee, S.-I.: PseudoBound: limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies. Neurocomputing 534, 147\u2013160 (2023)","journal-title":"Neurocomputing"},{"key":"4156_CR42","doi-asserted-by":"crossref","unstructured":"Wang, L., Tian, J., Zhou, S., Shi, H., Hua, G.: Memory-augmented appearance-motion network for video anomaly detection. Pattern Recogn. 138, 109335 (2023)","DOI":"10.1016\/j.patcog.2023.109335"},{"key":"4156_CR43","doi-asserted-by":"publisher","first-page":"127444","DOI":"10.1016\/j.neucom.2024.127444","volume":"579","author":"R Kommanduri","year":"2024","unstructured":"Kommanduri, R., Ghorai, M.: DAST-Net: dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection. Neurocomputing 579, 127444 (2024)","journal-title":"Neurocomputing"},{"key":"4156_CR44","doi-asserted-by":"publisher","first-page":"107830","DOI":"10.1016\/j.engappai.2023.107830","volume":"131","author":"R Singh","year":"2024","unstructured":"Singh, R., Sethi, A., Saini, K., Saurav, S., Tiwari, A., Singh, S.: Attention-guided generator with dual discriminator GAN for real-time video anomaly detection. Eng. Appl. Artif. Intell. 131, 107830 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4156_CR45","doi-asserted-by":"crossref","unstructured":"Cheng, K., Zeng, X., Liu, Y., Zhao, M., Pang, C., Hu, X.: Spatial-temporal graph convolutional network boosted flow-frame prediction for video anomaly detection. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10095170"},{"key":"4156_CR46","doi-asserted-by":"crossref","unstructured":"Yan, C., Zhang, S., Liu, Y., Pang, G., Wang, W.: Feature prediction diffusion model for video anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5527\u20135537 (2023)","DOI":"10.1109\/ICCV51070.2023.00509"},{"key":"4156_CR47","doi-asserted-by":"crossref","unstructured":"Huang, C., Wen, J., Liu, C., Liu, Y.: Long short-term dynamic prototype alignment learning for video anomaly detection (2024)","DOI":"10.24963\/ijcai.2024\/96"},{"issue":"2","key":"4156_CR48","doi-asserted-by":"publisher","first-page":"1980","DOI":"10.1007\/s10489-023-05252-6","volume":"54","author":"J Liang","year":"2024","unstructured":"Liang, J., Xiao, Y., Zhou, J.T., Yang, F., Li, T., Fang, Z.: C2Net: content-dependent and-independent cross-attention network for anomaly detection in videos. Appl. Intell. 54(2), 1980\u20131996 (2024)","journal-title":"Appl. Intell."},{"key":"4156_CR49","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X.-S.: Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1933\u20131941 (2017)","DOI":"10.1145\/3123266.3123451"},{"key":"4156_CR50","doi-asserted-by":"crossref","unstructured":"Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11996\u201312004 (2019)","DOI":"10.1109\/CVPR.2019.01227"},{"key":"4156_CR51","doi-asserted-by":"crossref","unstructured":"Ye, M., Peng, X., Gan, W., Wu, W., Qiao, Y.: Anopcn: Video anomaly detection via deep predictive coding network. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1805\u20131813 (2019)","DOI":"10.1145\/3343031.3350899"},{"key":"4156_CR52","doi-asserted-by":"crossref","unstructured":"Shi, C., Sun, C., Wu, Y., Jia, Y.: Video anomaly detection via sequentially learning multiple pretext tasks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10330\u201310340 (2023)","DOI":"10.1109\/ICCV51070.2023.00948"},{"key":"4156_CR53","doi-asserted-by":"publisher","unstructured":"Park, C., Cho, M., Lee, M., Lee, S.: Fastano: Fast anomaly detection via spatio-temporal patch transformation. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2022). https:\/\/doi.org\/10.1109\/wacv51458.2022.00197","DOI":"10.1109\/wacv51458.2022.00197"},{"key":"4156_CR54","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04156-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04156-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04156-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T02:38:56Z","timestamp":1748572736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04156-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,12]]},"references-count":54,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["4156"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04156-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5329540\/v1","asserted-by":"object"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,12]]},"assertion":[{"value":"25 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"545"}}