{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:31:52Z","timestamp":1774423912506,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Innovation Project of GUET Graduate Education","award":["2025YCXS046"],"award-info":[{"award-number":["2025YCXS046"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s44443-025-00202-3","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T15:09:08Z","timestamp":1755529748000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8634-0713","authenticated-orcid":false,"given":"Chaofeng","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1059-088X","authenticated-orcid":false,"given":"Yongjie","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1183-2223","authenticated-orcid":false,"given":"Fan","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2456-2093","authenticated-orcid":false,"given":"Yulong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3742-672X","authenticated-orcid":false,"given":"Peiyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4138-3417","authenticated-orcid":false,"given":"Jiaying","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1523-1125","authenticated-orcid":false,"given":"Bizhong","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"issue":"7","key":"202_CR1","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-020-0900-x","volume":"26","author":"K AbdulJabbar","year":"2020","unstructured":"AbdulJabbar K, Raza SEA, Rosenthal R et al (2020) Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat Med 26(7):1054\u20131062. https:\/\/doi.org\/10.1038\/s41591-020-0900-x","journal-title":"Nat Med"},{"issue":"1","key":"202_CR2","doi-asserted-by":"publisher","first-page":"67","DOI":"10.5409\/wjcp.v5.i1.67","volume":"5","author":"CE Ahearne","year":"2016","unstructured":"Ahearne CE, Boylan GB, Murray DM (2016) Short and long term prognosis in perinatal asphyxia: an update. World J Clin Pediatr 5(1):67. https:\/\/doi.org\/10.5409\/wjcp.v5.i1.67","journal-title":"World J Clin Pediatr"},{"issue":"23","key":"202_CR3","doi-asserted-by":"publisher","first-page":"5591","DOI":"10.1242\/jcs.116392","volume":"125","author":"FR Balkwill","year":"2012","unstructured":"Balkwill FR, Capasso M, Hagemann T (2012) The tumor microenvironment at a glance. J Cell Sci 125(23):5591\u20135596. https:\/\/doi.org\/10.1242\/jcs.116392","journal-title":"J Cell Sci"},{"issue":"8","key":"202_CR4","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella G, Hanna MG, Geneslaw L et al (2019) Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 25(8):1301\u20131309. https:\/\/doi.org\/10.1038\/s41591-019-0508-1","journal-title":"Nat Med"},{"issue":"14","key":"202_CR5","doi-asserted-by":"publisher","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","volume":"35","author":"A Cheerla","year":"2019","unstructured":"Cheerla A, Gevaert O (2019) Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14):i446\u2013i454. https:\/\/doi.org\/10.1093\/bioinformatics\/btz342","journal-title":"Bioinformatics"},{"issue":"4","key":"202_CR6","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen RJ, Lu MY, Wang J et al (2020) Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans Med Imaging 41(4):757\u2013770. https:\/\/doi.org\/10.1109\/TMI.2020.3021387","journal-title":"IEEE Trans Med Imaging"},{"key":"202_CR7","doi-asserted-by":"publisher","unstructured":"Chen RJ, Lu MY, Weng WH et\u00a0al (2021) Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4015\u20134025. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00398","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"202_CR8","doi-asserted-by":"publisher","unstructured":"Chen RJ, Chen C, Li Y et\u00a0al (2022a) Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16144\u201316155. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01567","DOI":"10.1109\/CVPR52688.2022.01567"},{"issue":"8","key":"202_CR9","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","volume":"40","author":"RJ Chen","year":"2022","unstructured":"Chen RJ, Lu MY, Williamson DF et al (2022b) Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40(8):865\u2013878. https:\/\/doi.org\/10.1016\/j.ccell.2022.07.004","journal-title":"Cancer Cell"},{"issue":"2","key":"202_CR10","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox DR (1972) Regression models and life-tables. J Roy Stat Soc Ser B (Methodol) 34(2):187\u2013202. https:\/\/doi.org\/10.1111\/j.2517-6161.1972.tb00899.x","journal-title":"J Roy Stat Soc Ser B (Methodol)"},{"key":"202_CR11","doi-asserted-by":"publisher","unstructured":"Feng Y, You H, Zhang Z et\u00a0al (2019) Hypergraph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 3558\u20133565. https:\/\/doi.org\/10.1609\/aaai.v33i01.33013558","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"8","key":"202_CR12","doi-asserted-by":"publisher","first-page":"2462","DOI":"10.1109\/TMI.2023.3253760","volume":"42","author":"W Hou","year":"2023","unstructured":"Hou W, Lin C, Yu L et al (2023) Hybrid graph convolutional network with online masked autoencoder for robust multimodal cancer survival prediction. IEEE Trans Med Imaging 42(8):2462\u20132473. https:\/\/doi.org\/10.1109\/TMI.2023.3253760","journal-title":"IEEE Trans Med Imaging"},{"key":"202_CR13","unstructured":"Ilse M, Tomczak J, Welling M (2018) Attention-based deep multiple instance learning. In: International conference on machine learning. PMLR, pp 2127\u20132136"},{"key":"202_CR14","doi-asserted-by":"publisher","unstructured":"Klambauer G, Unterthiner T, Mayr A et al (2017) Self-normalizing neural networks. Adv Neural Inf Process Syst 30. https:\/\/doi.org\/10.5555\/3294771.3294864","DOI":"10.5555\/3294771.3294864"},{"key":"202_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-021-08009-x","volume":"21","author":"H Kuroda","year":"2021","unstructured":"Kuroda H, Jamiyan T, Yamaguchi R et al (2021) Tumor-infiltrating b cells and t cells correlate with postoperative prognosis in triple-negative carcinoma of the breast. BMC Cancer 21:1\u201310. https:\/\/doi.org\/10.1186\/s12885-021-08009-x","journal-title":"BMC Cancer"},{"key":"202_CR16","doi-asserted-by":"publisher","unstructured":"Li B, Li Y, Eliceiri KW (2021) Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14318\u201314328. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01409","DOI":"10.1109\/CVPR46437.2021.01409"},{"issue":"9","key":"202_CR17","doi-asserted-by":"publisher","first-page":"2587","DOI":"10.1093\/bioinformatics\/btac113","volume":"38","author":"R Li","year":"2022","unstructured":"Li R, Wu X, Li A et al (2022) Hfbsurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction. Bioinformatics 38(9):2587\u20132594. https:\/\/doi.org\/10.1093\/bioinformatics\/btac113","journal-title":"Bioinformatics"},{"issue":"6","key":"202_CR18","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","volume":"1","author":"A Liberzon","year":"2015","unstructured":"Liberzon A, Birger C, Thorvaldsd\u00f3ttir H et al (2015) The molecular signatures database hallmark gene set collection. Cell Syst 1(6):417\u2013425. https:\/\/doi.org\/10.1016\/j.cels.2015.12.004","journal-title":"Cell Syst"},{"issue":"6","key":"202_CR19","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu MY, Williamson DF, Chen TY et al (2021) Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 5(6):555\u2013570. https:\/\/doi.org\/10.1038\/s41551-020-00682-w","journal-title":"Nat Biomed Eng"},{"issue":"13","key":"202_CR20","doi-asserted-by":"publisher","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","volume":"115","author":"P Mobadersany","year":"2018","unstructured":"Mobadersany P, Yousefi S, Amgad M et al (2018) Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci 115(13):E2970\u2013E2979. https:\/\/doi.org\/10.1073\/pnas.1717139115","journal-title":"Proc Natl Acad Sci"},{"issue":"8","key":"202_CR21","doi-asserted-by":"publisher","first-page":"2696","DOI":"10.1111\/cas.14521","volume":"111","author":"Y Oya","year":"2020","unstructured":"Oya Y, Hayakawa Y, Koike K (2020) Tumor microenvironment in gastric cancers. Cancer Sci 111(8):2696\u20132707. https:\/\/doi.org\/10.1111\/cas.14521","journal-title":"Cancer Sci"},{"issue":"5","key":"202_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44443-025-00106-2","volume":"37","author":"F Qin","year":"2025","unstructured":"Qin F, Liang Y, Yang C et al (2025) Medical image segmentation network based on multi-scale cross-attention and wavelet transform. J King Saud Univ Comput Inf Sci 37(5):1\u201314. https:\/\/doi.org\/10.1007\/s44443-025-00106-2","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"12","key":"202_CR23","doi-asserted-by":"publisher","first-page":"3777","DOI":"10.3390\/s24123777","volume":"24","author":"JL Ruiz-Casado","year":"2024","unstructured":"Ruiz-Casado JL, Molina-Cabello MA, Luque-Baena RM (2024) Enhancing histopathological image classification performance through synthetic data generation with generative adversarial networks. Sensors 24(12):3777. https:\/\/doi.org\/10.3390\/s24123777","journal-title":"Sensors"},{"issue":"1","key":"202_CR24","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.celrep.2018.03.086","volume":"23","author":"J Saltz","year":"2018","unstructured":"Saltz J, Gupta R, Hou L et al (2018) Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 23(1):181\u2013193. https:\/\/doi.org\/10.1016\/j.celrep.2018.03.086","journal-title":"Cell Rep"},{"key":"202_CR25","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.5555\/3540261.3540425","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao Z, Bian H, Chen Y et al (2021) Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv Neural Inf Process Syst 34:2136\u20132147. https:\/\/doi.org\/10.5555\/3540261.3540425","journal-title":"Adv Neural Inf Process Syst"},{"key":"202_CR26","doi-asserted-by":"publisher","unstructured":"Xiong C, Chen H, Zheng H et\u00a0al (2024) Mome: Mixture of multimodal experts for cancer survival prediction. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 318\u2013328. https:\/\/doi.org\/10.1007\/978-3-031-72083-3_30","DOI":"10.1007\/978-3-031-72083-3_30"},{"key":"202_CR27","doi-asserted-by":"publisher","unstructured":"Xu Y, Chen H (2023) Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 21241\u201321251. https:\/\/doi.org\/10.1109\/ICCV51070.2023.01942","DOI":"10.1109\/ICCV51070.2023.01942"},{"key":"202_CR28","doi-asserted-by":"publisher","unstructured":"Yang S, Wang Y, Chen H (2024) Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 296\u2013306. https:\/\/doi.org\/10.1007\/978-3-031-72083-3_28","DOI":"10.1007\/978-3-031-72083-3_28"},{"issue":"1","key":"202_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-11817-6","volume":"7","author":"S Yousefi","year":"2017","unstructured":"Yousefi S, Amrollahi F, Amgad M et al (2017) Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci Rep 7(1):1\u201311. https:\/\/doi.org\/10.1038\/s41598-017-11817-6","journal-title":"Sci Rep"},{"key":"202_CR30","doi-asserted-by":"publisher","unstructured":"Zhang H, Meng Y, Zhao Y et\u00a0al (2022) Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 18802\u201318812. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01824","DOI":"10.1109\/CVPR52688.2022.01824"},{"key":"202_CR31","doi-asserted-by":"publisher","unstructured":"Zhang W, Li R, Zeng T et\u00a0al (2015) Deep model based transfer and multi-task learning for biological image analysis. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1475\u20131484. https:\/\/doi.org\/10.1109\/TBDATA.2016.2573280","DOI":"10.1109\/TBDATA.2016.2573280"},{"key":"202_CR32","doi-asserted-by":"publisher","unstructured":"Zhou F, Chen H (2023) Cross-modal translation and alignment for survival analysis. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 21485\u201321494. https:\/\/doi.org\/10.1109\/ICCV51070.2023.01964","DOI":"10.1109\/ICCV51070.2023.01964"},{"key":"202_CR33","doi-asserted-by":"publisher","unstructured":"Zhu X, Yao J, Zhu F et\u00a0al (2017) Wsisa: Making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7234\u20137242. https:\/\/doi.org\/10.1109\/CVPR.2017.725","DOI":"10.1109\/CVPR.2017.725"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00202-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00202-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00202-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T12:42:23Z","timestamp":1758112943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00202-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":33,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["202"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00202-3","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,18]]},"assertion":[{"value":"6 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"TCGA belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues and other conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"177"}}