{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T19:10:20Z","timestamp":1778872220902,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Project in Science and Technology of Henan Province","award":["222102220060"],"award-info":[{"award-number":["222102220060"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s11760-026-05307-4","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:24:28Z","timestamp":1777058668000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LCDNet: a learnable clustering-driven network for RGB-D salient object detection"],"prefix":"10.1007","volume":"20","author":[{"given":"Zhiqiang","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luwang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaixin","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoqiang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"issue":"12","key":"5307_CR1","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji, A., Cheng, M.M., Jiang, H., et al.: Salient object detection: A benchmark. IEEE Trans. Image Process. 24(12), 5706\u20135722 (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2487833","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"5307_CR2","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/TCSVT.2022.3215979","volume":"33","author":"G Chen","year":"2023","unstructured":"Chen, G., Shao, F., Chai, X., et al.: Modality-induced transfer-fusion network for RGB-D and RGB-T salient object detection. IEEE Trans. Circuits Syst. Video Technol. 33(4), 1787\u20131801 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2022.3215979","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5307_CR3","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TIP.2024.3364022","volume":"33","author":"H Chen","year":"2024","unstructured":"Chen, H., Shen, F., Ding, D., et al.: Disentangled cross-modal transformer for rgb-d salient object detection and beyond. IEEE Trans. Image Process. 33, 1699\u20131709 (2024)","journal-title":"IEEE Trans. Image Process."},{"key":"5307_CR4","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TIP.2024.3364022","volume":"33","author":"H Chen","year":"2024","unstructured":"Chen, H., Shen, F., Ding, D., et al.: Disentangled cross-modal transformer for RGB-D salient object detection and beyond. IEEE Trans. Image Process. 33, 1699\u20131709 (2024). https:\/\/doi.org\/10.1109\/TIP.2024.3364022","journal-title":"IEEE Trans. Image Process."},{"key":"5307_CR5","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2022.11.055","volume":"522","author":"T Chen","year":"2023","unstructured":"Chen, T., Xiao, J., Hu, X., et al.: Adaptive fusion network for RGB-D salient object detection. Neurocomputing 522, 152\u2013164 (2023). https:\/\/doi.org\/10.1016\/j.neucom.2022.11.055","journal-title":"Neurocomputing"},{"key":"5307_CR6","doi-asserted-by":"crossref","unstructured":"Chen,X., Kang, B., Geng, W., et\u00a0al.: Sutrack: Towards simple and unified single object tracking. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2239\u20132247 (2025)","DOI":"10.1609\/aaai.v39i2.32223"},{"key":"5307_CR7","doi-asserted-by":"publisher","first-page":"4253","DOI":"10.1109\/TMM.2022.3172852","volume":"25","author":"X Cheng","year":"2023","unstructured":"Cheng, X., Zheng, X., Pei, J., et al.: Depth-induced gap-reducing network for RGB-D salient object detection: An interaction, guidance and refinement approach. IEEE Trans. Multimedia 25, 4253\u20134266 (2023). https:\/\/doi.org\/10.1109\/TMM.2022.3172852","journal-title":"IEEE Trans. Multimedia"},{"key":"5307_CR8","doi-asserted-by":"publisher","unstructured":"Cheng,Y., Fu, H., Wei, X., et\u00a0al.:Depth enhanced saliency detection method. In: Proceedings of International Conference on Internet Multimedia Computing and Service (ICIMCS). ACM, Xiamen, China, pp 23\u201327, https:\/\/doi.org\/10.1145\/2641530.2641539(2014)","DOI":"10.1145\/2641530.2641539"},{"key":"5307_CR9","doi-asserted-by":"publisher","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., et\u00a0al.: Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp 4548\u20134557, https:\/\/doi.org\/10.1109\/ICCV.2017.487(2017)","DOI":"10.1109\/ICCV.2017.487"},{"key":"5307_CR10","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., et\u00a0al.:Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421(2018)","DOI":"10.24963\/ijcai.2018\/97"},{"issue":"5","key":"5307_CR11","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1109\/TNNLS.2020.2996406","volume":"32","author":"DP Fan","year":"2021","unstructured":"Fan, D.P., Lin, Z., Zhang, Z., et al.: Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks. IEEE Transactions on Neural Networks and Learning Systems 32(5), 2075\u20132089 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.2996406","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"5307_CR12","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s00371-023-02870-6","volume":"40","author":"L Gao","year":"2024","unstructured":"Gao, L., Fu, P., Xu, M., et al.: Uminet: A unified multi-modality interaction network for rgb-d and rgb-t salient object detection. Vis. Comput. 40(3), 1565\u20131582 (2024)","journal-title":"Vis. Comput."},{"issue":"8","key":"5307_CR13","doi-asserted-by":"publisher","first-page":"3067","DOI":"10.1007\/s11263-024-01884-1","volume":"132","author":"X Hu","year":"2024","unstructured":"Hu, X., Sun, F., Sun, J., et al.: Cross-modal fusion and progressive decoding network for RGB-D salient object detection. Int. J. Comput. Vision 132(8), 3067\u20133085 (2024). https:\/\/doi.org\/10.1007\/s11263-024-01884-1","journal-title":"Int. J. Comput. Vision"},{"issue":"23","key":"5307_CR14","doi-asserted-by":"publisher","first-page":"14361","DOI":"10.1007\/s00521-024-09692-0","volume":"36","author":"X Jia","year":"2024","unstructured":"Jia, X., Zhao, W., Wang, Y., et al.: Cmdcf: an effective cross-modal dense cooperative fusion network for rgb-d sod. Neural Comput. Appl. 36(23), 14361\u201314378 (2024)","journal-title":"Neural Comput. Appl."},{"key":"5307_CR15","doi-asserted-by":"crossref","unstructured":"Ju, R., Ge, L., Geng, W., et\u00a0al.:Depth saliency based on anisotropic center-surround difference. In: 2014 IEEE international conference on image processing (ICIP), IEEE, 1115\u20131119 (2014)","DOI":"10.1109\/ICIP.2014.7025222"},{"key":"5307_CR16","doi-asserted-by":"publisher","unstructured":"Lee, M., Park, C., Cho, S., et\u00a0al.: SPSN: Superpixel prototype sampling network for RGB-D salient object detection. In: European Conference on Computer Vision (ECCV). Springer, Cham, Tel Aviv, Israel, 630\u2013647, https:\/\/doi.org\/10.1007\/978-3-031-20071-7_37 (2022)","DOI":"10.1007\/978-3-031-20071-7_37"},{"issue":"1","key":"5307_CR17","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/TCYB.2020.2969255","volume":"51","author":"C Li","year":"2020","unstructured":"Li, C., Cong, R., Kwong, S., et al.: Asif-net: Attention steered interweave fusion network for rgb-d salient object detection. IEEE transactions on cybernetics 51(1), 88\u2013100 (2020)","journal-title":"IEEE transactions on cybernetics"},{"key":"5307_CR18","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, X., Li, W., et\u00a0al.: U-kan makes strong backbone for medical image segmentation and generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, 4652\u20134660 (2025a)","DOI":"10.1609\/aaai.v39i5.32491"},{"issue":"4","key":"5307_CR19","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s11263-023-01761-7","volume":"131","author":"J Li","year":"2023","unstructured":"Li, J., Ji, W., Zhang, M., et al.: Delving into calibrated depth for accurate RGB-D salient object detection. Int. J. Comput. Vision 131(4), 855\u2013876 (2023). https:\/\/doi.org\/10.1007\/s11263-023-01761-7","journal-title":"Int. J. Comput. Vision"},{"key":"5307_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2024.104268","volume":"104","author":"J Li","year":"2024","unstructured":"Li, J., Pan, C., Zheng, Y., et al.: Progressive cross-level fusion network for rgb-d salient object detection. J. Vis. Commun. Image Represent. 104, 104268 (2024)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"5307_CR21","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.jmapro.2025.03.113","volume":"143","author":"R Li","year":"2025","unstructured":"Li, R., Ma, H., Wang, R., et al.: Application of unsupervised learning methods based on video data for real-time anomaly detection in wire arc additive manufacturing. J. Manuf. Process. 143, 37\u201355 (2025)","journal-title":"J. Manuf. Process."},{"key":"5307_CR22","doi-asserted-by":"publisher","unstructured":"Lin, W., Wu, Z., Chen, J., et\u00a0al.: Scale-aware modulation meet transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, 6015\u20136026, https:\/\/doi.org\/10.1109\/ICCV.2023.00461(2023)","DOI":"10.1109\/ICCV.2023.00461"},{"issue":"4","key":"5307_CR23","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1049\/cvi2.12167","volume":"17","author":"C Liu","year":"2023","unstructured":"Liu, C., Yang, G., Wang, S., et al.: TANet: Transformer-based asymmetric network for RGB-D salient object detection. IET Comput. Vision 17(4), 415\u2013430 (2023). https:\/\/doi.org\/10.1049\/cvi2.12167","journal-title":"IET Comput. Vision"},{"key":"5307_CR24","doi-asserted-by":"publisher","unstructured":"Liu, N., Zhang, N., Wan, K., et\u00a0al.: Visual saliency transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, Canada, 4722\u20134732, https:\/\/doi.org\/10.1109\/ICCV.2021.00470(2021)","DOI":"10.1109\/ICCV.2021.00470"},{"key":"5307_CR25","doi-asserted-by":"publisher","unstructured":"Lyu, P., Yeung, P.H., Yu, X., et al.: Efficient fourier filtering network with contrastive learning for AAV-based unaligned bimodal salient object detection. IEEE Trans. Geosci. Remote Sens. 63,(2025). https:\/\/doi.org\/10.1109\/TGRS.2025.3562562","DOI":"10.1109\/TGRS.2025.3562562"},{"key":"5307_CR26","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1109\/TIP.2025.3564821","volume":"34","author":"P Lyu","year":"2025","unstructured":"Lyu, P., Yu, X., Chi, J., et al.: Twinstnet: Broad-view twins transformer network for bi-modal salient object detection. IEEE Trans. Image Process. 34, 2796\u20132810 (2025). https:\/\/doi.org\/10.1109\/TIP.2025.3564821","journal-title":"IEEE Trans. Image Process."},{"key":"5307_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2025.3629727","author":"P Lyu","year":"2025","unstructured":"Lyu, P., Yu, X., Yeung, P.H., et al.: Deep fourier-embedded network for RGB and thermal salient object detection. IEEE Trans. Circuits Syst. Video Technol. (2025). https:\/\/doi.org\/10.1109\/TCSVT.2025.3629727","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5307_CR28","doi-asserted-by":"publisher","unstructured":"Niu, Y., Geng, Y., Li, X., et\u00a0al.: Leveraging stereopsis for saliency analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 454\u2013461, https:\/\/doi.org\/10.1109\/CVPR.2012.6247684(2012)","DOI":"10.1109\/CVPR.2012.6247684"},{"key":"5307_CR29","unstructured":"Panda, G., Kundu, S., Bhattacharya, S., et\u00a0al.:SSNet: Saliency prior and state space model-based network for salient object detection in RGB-D images. arXiv preprint arXiv:2503.02270(2025)"},{"key":"5307_CR30","doi-asserted-by":"publisher","unstructured":"Peng, H., Li, B., Xiong, W., et\u00a0al.:RGBD salient object detection: A benchmark and algorithms. In: European Conference on Computer Vision (ECCV). Springer, Cham, Zurich, Switzerland, pp 92\u2013109, https:\/\/doi.org\/10.1007\/978-3-319-10590-1_7(2014)","DOI":"10.1007\/978-3-319-10590-1_7"},{"key":"5307_CR31","doi-asserted-by":"publisher","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., et\u00a0al.:Saliency filters: Contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 733\u2013740, https:\/\/doi.org\/10.1109\/CVPR.2012.6247735(2012)","DOI":"10.1109\/CVPR.2012.6247735"},{"key":"5307_CR32","doi-asserted-by":"publisher","unstructured":"Piao ,Y., Ji, W., Li, J., et\u00a0al.:Depth-induced multi-scale recurrent attention network for saliency detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, pp 7254\u20137263, https:\/\/doi.org\/10.1109\/ICCV.2019.00735(2019)","DOI":"10.1109\/ICCV.2019.00735"},{"key":"5307_CR33","doi-asserted-by":"publisher","unstructured":"Song, D., Yuan, Y., Li, X.:Potential region attention network for RGB-D salient object detection. Neural Networks p Article 107620. https:\/\/doi.org\/10.1016\/j.neunet.2025.03.010 (2025)","DOI":"10.1016\/j.neunet.2025.03.010"},{"key":"5307_CR34","doi-asserted-by":"publisher","unstructured":"Su, Y., Gao, H., Wang, M., et\u00a0al.:Asymmetric cross-modality interaction network for RGB-D salient object detection. Expert Systems with Applications 275:Article 127004. https:\/\/doi.org\/10.1016\/j.eswa.2024.127004(2025)","DOI":"10.1016\/j.eswa.2024.127004"},{"key":"5307_CR35","doi-asserted-by":"publisher","unstructured":"Sun, F., Ren, P., Yin, B., et al.: CATNet: A cascaded and aggregated transformer network for RGB-D salient object detection. IEEE Trans. Multimedia 26, 2249\u20132262 (2024). https:\/\/doi.org\/10.1109\/TMM.2023.3294003","DOI":"10.1109\/TMM.2023.3294003"},{"issue":"11","key":"5307_CR36","doi-asserted-by":"publisher","first-page":"2822","DOI":"10.1007\/s11263-022-01646-0","volume":"130","author":"P Sun","year":"2022","unstructured":"Sun, P., Zhang, W., Li, S., et al.: Learnable depth-sensitive attention for deep rgb-d saliency detection with multi-modal fusion architecture search. Int. J. Comput. Vision 130(11), 2822\u20132841 (2022)","journal-title":"Int. J. Comput. Vision"},{"key":"5307_CR37","doi-asserted-by":"publisher","unstructured":"Tang, Y., Li, M.: (2024) DMGNet: Depth mask guiding network for RGB-D salient object detection. Neural Networks 180:Article 106751. https:\/\/doi.org\/10.1016\/j.neunet.2023.11.026","DOI":"10.1016\/j.neunet.2023.11.026"},{"issue":"7","key":"5307_CR38","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.1007\/s00371-024-03712-9","volume":"41","author":"K Wang","year":"2025","unstructured":"Wang, K., Liu, C., Zhang, R.: Cma-sod: cross-modal attention fusion network for rgb-d salient object detection. Vis. Comput. 41(7), 5135\u20135151 (2025)","journal-title":"Vis. Comput."},{"issue":"11","key":"5307_CR39","doi-asserted-by":"publisher","first-page":"11041","DOI":"10.1109\/TCSVT.2025.3578340","volume":"35","author":"Y Wang","year":"2025","unstructured":"Wang, Y., Wei, S., Xu, S., et al.: Confidence-driven unimodal interference removal for enhanced multimodal object detection. IEEE Trans. Circuits Syst. Video Technol. 35(11), 11041\u201311053 (2025). https:\/\/doi.org\/10.1109\/TCSVT.2025.3578340","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5307_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2025.112135","volume":"171","author":"Y Yang","year":"2026","unstructured":"Yang, Y., Huang, N., Zhang, Q., et al.: Mitigating fusion bias for RGB-D salient object detection. Pattern Recognit 171, 112135 (2026). https:\/\/doi.org\/10.1016\/J.PATCOG.2025.112135","journal-title":"Pattern Recognit"},{"key":"5307_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129718","volume":"631","author":"Y Zhan","year":"2025","unstructured":"Zhan, Y., Zeng, Z., Liu, H., et al.: Mambasod: Dual mamba-driven cross-modal fusion network for rgb-d salient object detection. Neurocomputing 631, 129718 (2025)","journal-title":"Neurocomputing"},{"key":"5307_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cong, R., Lin, Q., et\u00a0al.:Cross-modality discrepant interaction network for rgb-d salient object detection. In: Proceedings of the 29th ACM international conference on multimedia, 2094\u20132102 (2021)","DOI":"10.1145\/3474085.3475364"},{"key":"5307_CR43","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.1109\/TMM.2024.3521699","volume":"27","author":"J Zhang","year":"2025","unstructured":"Zhang, J., Zhang, R., Xu, L., et al.: Fastersal: Robust and real-time single-stream architecture for RGB-D salient object detection. IEEE Trans Multim 27, 2477\u20132488 (2025). https:\/\/doi.org\/10.1109\/TMM.2024.3521699","journal-title":"IEEE Trans Multim"},{"issue":"10","key":"5307_CR44","doi-asserted-by":"publisher","first-page":"9209","DOI":"10.1109\/TCSVT.2023.3268217","volume":"34","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Xu, Y., Wang, T., et al.: Multi-prior driven network for RGB-D salient object detection. IEEE Trans. Circuits Syst. Video Technol. 34(10), 9209\u20139222 (2024). https:\/\/doi.org\/10.1109\/TCSVT.2023.3268217","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5307_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2025.112103","volume":"171","author":"M Zhong","year":"2026","unstructured":"Zhong, M., Sun, J., Wang, F., et al.: LESOD: lightweight and efficient network for RGB-D salient object detection. Pattern Recognit 171, 112103 (2026). https:\/\/doi.org\/10.1016\/J.PATCOG.2025.112103","journal-title":"Pattern Recognit"},{"issue":"4","key":"5307_CR46","doi-asserted-by":"publisher","first-page":"5523","DOI":"10.1109\/TASE.2023.3313122","volume":"21","author":"W Zhou","year":"2024","unstructured":"Zhou, W., Xiao, Y., Yan, W., et al.: CMPFFNet: Cross-modal and progressive feature fusion network for RGB-D indoor scene semantic segmentation. IEEE Trans. Autom. Sci. Eng. 21(4), 5523\u20135533 (2024). https:\/\/doi.org\/10.1109\/TASE.2023.3313122","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"5307_CR47","doi-asserted-by":"publisher","first-page":"4341","DOI":"10.1109\/TASE.2024.3410182","volume":"22","author":"W Zhou","year":"2025","unstructured":"Zhou, W., Sun, F., Qiu, W.: MSNet: Multiple strategy network with bidirectional fusion for detecting salient objects in RGB-D images. IEEE Trans. Autom. Sci. Eng. 22, 4341\u20134353 (2025). https:\/\/doi.org\/10.1109\/TASE.2024.3410182","journal-title":"IEEE Trans. Autom. Sci. Eng."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05307-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05307-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05307-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T18:39:36Z","timestamp":1778870376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05307-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":47,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["5307"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05307-4","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"16 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"280"}}