{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T07:07:53Z","timestamp":1778828873342,"version":"3.51.4"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008845","name":"Xinjiang University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008845","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110391","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:41:11Z","timestamp":1777592471000},"page":"110391","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CD-AHF: A causal-driven adaptive hierarchical fusion framework for multimodal cancer prognosis"],"prefix":"10.1016","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4052-2789","authenticated-orcid":false,"given":"Qing","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"QingYang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7999-9433","authenticated-orcid":false,"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110391_b1","doi-asserted-by":"crossref","unstructured":"R.J. Chen, C. Chen, Y. Li, T.Y. Chen, A.D. Trister, R.G. Krishnan, F. Mahmood, Scaling vision transformers to gigapixel images via hierarchical self-supervised learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16144\u201316155.","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"10.1016\/j.bspc.2026.110391_b2","series-title":"2023 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"1254","article-title":"Transvcox: bridging transformer encoder and pre-trained VAE for robust cancer multi-omics survival analysis","author":"Li","year":"2023"},{"issue":"1","key":"10.1016\/j.bspc.2026.110391_b3","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae699","article-title":"Multimodal deep learning approaches for precision oncology: a comprehensive review","volume":"26","author":"Yang","year":"2024","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.bspc.2026.110391_b4","series-title":"Pathology-and-genomics multimodal transformer for survival outcome prediction","author":"Ding","year":"2023"},{"key":"10.1016\/j.bspc.2026.110391_b5","series-title":"Deep orthogonal fusion: Multimodal prognostic biomarker discovery integrating radiology, pathology, genomic, and clinical data","author":"Braman","year":"2021"},{"key":"10.1016\/j.bspc.2026.110391_b6","series-title":"International Workshop on Multiscale Multimodal Medical Imaging","first-page":"1","article-title":"M 2 f: A multi-modal and multi-task fusion network for glioma diagnosis and prognosis","author":"Lu","year":"2022"},{"key":"10.1016\/j.bspc.2026.110391_b7","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2024.1408843","article-title":"Multimodal data integration for oncology in the era of deep neural networks: a review","volume":"7","author":"Waqas","year":"2024","journal-title":"Front. Artif. Intell."},{"issue":"9","key":"10.1016\/j.bspc.2026.110391_b8","doi-asserted-by":"crossref","DOI":"10.1007\/s00262-025-04102-3","article-title":"A pan-cancer comparative analysis of the cancer genome atlas transcriptomic TIL-immune signatures","volume":"74","author":"Hitscherich","year":"2025","journal-title":"Cancer Immunol. Immunother."},{"key":"10.1016\/j.bspc.2026.110391_b9","doi-asserted-by":"crossref","unstructured":"A. McLoughlin, H.Y. Ho, X. Zhao, A.K. Hakansson, A. Moradi, Q.J. Xu, Y. Liu, Comparison of Digital Histology AI Models with Low-Dimensional Genomic and Clinical Models in Survival Modeling for Prostate Cancer, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 881\u2013891.","DOI":"10.1109\/ICCVW69036.2025.00097"},{"issue":"8","key":"10.1016\/j.bspc.2026.110391_b10","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","volume":"40","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"issue":"000","key":"10.1016\/j.bspc.2026.110391_b11","article-title":"Interpretable multimodal fusion model for bridged histology and genomics survival prediction in pan-cancer","volume":"000","author":"Gao","year":"2025","journal-title":"Adv. Sci."},{"issue":"6","key":"10.1016\/j.bspc.2026.110391_b12","doi-asserted-by":"crossref","first-page":"4809","DOI":"10.1007\/s10462-021-10121-0","article-title":"A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches","volume":"55","author":"Li","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.bspc.2026.110391_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102867","article-title":"SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations","volume":"88","author":"Pan","year":"2023","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.bspc.2026.110391_b14","doi-asserted-by":"crossref","first-page":"10509","DOI":"10.1038\/s41598-019-46718-3","article-title":"Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning","volume":"9","author":"Tabibu","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110391_b15","series-title":"Pacific Symposium on Biocomputing 2020","first-page":"355","article-title":"PAGE-net: interpretable and integrative deep learning for survival analysis using histopathological images and genomic data","author":"Hao","year":"2019"},{"issue":"9","key":"10.1016\/j.bspc.2026.110391_b16","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1093\/bioinformatics\/btaa056","article-title":"Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma","volume":"36","author":"Ning","year":"2020","journal-title":"Bioinformatics"},{"issue":"6","key":"10.1016\/j.bspc.2026.110391_b17","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0233678","article-title":"Deep learning-based survival prediction for multiple cancer types using histopathology images","volume":"15","author":"Wulczyn","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.110391_b18","series-title":"Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis","author":"Zhang","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2026.110391_b19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","article-title":"DeepSurv: personalized treatment recommender system using a cox proportional hazards deep neural network","volume":"18","author":"Katzman","year":"2018","journal-title":"BMC Med. Res. Methodol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110391_b20","doi-asserted-by":"crossref","first-page":"11707","DOI":"10.1038\/s41598-017-11817-6","article-title":"Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models","volume":"7","author":"Yousefi","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110391_b21","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3389\/fgene.2019.00166","article-title":"SALMON: survival analysis learning with multi-omics neural networks on breast cancer","volume":"10","author":"Huang","year":"2019","journal-title":"Front Genet"},{"issue":"18","key":"10.1016\/j.bspc.2026.110391_b22","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1093\/bioinformatics\/btab185","article-title":"GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction","volume":"37","author":"Wang","year":"2021","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.bspc.2026.110391_b23","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41568-021-00408-3","article-title":"Harnessing multimodal data integration to advance precision oncology","volume":"22","author":"Boehm","year":"2022","journal-title":"Nat. Rev. Cancer"},{"key":"10.1016\/j.bspc.2026.110391_b24","doi-asserted-by":"crossref","unstructured":"R.J. Chen, M.Y. Lu, W.-H. Weng, T.Y. Chen, D.F. Williamson, T. Manz, M. Shady, F. Mahmood, Multimodal co-attention transformer for survival prediction in gigapixel whole slide images, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 4015\u20134025.","DOI":"10.1109\/ICCV48922.2021.00398"},{"issue":"9","key":"10.1016\/j.bspc.2026.110391_b25","doi-asserted-by":"crossref","first-page":"2678","DOI":"10.1109\/TMI.2023.3263010","article-title":"Survival prediction via hierarchical multimodal co-attention transformer: A computational histology-radiology solution","volume":"42","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110391_b26","article-title":"PathBot: A foundation model for pathological image analysis","author":"Lu","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"13","key":"10.1016\/j.bspc.2026.110391_b27","doi-asserted-by":"crossref","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","article-title":"Predicting cancer outcomes from histology and genomics using convolutional networks","volume":"115","author":"Mobadersany","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"5","key":"10.1016\/j.bspc.2026.110391_b28","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1158\/2159-8290.CD-12-0095","article-title":"The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data","volume":"2","author":"Cerami","year":"2012","journal-title":"Cancer Discov."},{"key":"10.1016\/j.bspc.2026.110391_b29","article-title":"The 2016 world health organization classification of tumors of the central nervous system:A summary","author":"Kai","year":"2016","journal-title":"Chin. J. Magn. Reson. Imaging"},{"key":"10.1016\/j.bspc.2026.110391_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102260","article-title":"A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction","volume":"126","author":"Tan","year":"2022","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"10.1016\/j.bspc.2026.110391_b31","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","article-title":"Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis","volume":"41","author":"Chen","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110391_b32","doi-asserted-by":"crossref","unstructured":"F. Zhou, H. Chen, Cross-modal translation and alignment for survival analysis, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 21485\u201321494.","DOI":"10.1109\/ICCV51070.2023.01964"},{"key":"10.1016\/j.bspc.2026.110391_b33","doi-asserted-by":"crossref","unstructured":"G. Jaume, A. Vaidya, R.J. Chen, D.F. Williamson, P.P. Liang, F. Mahmood, Modeling dense multimodal interactions between biological pathways and histology for survival prediction, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 11579\u201311590.","DOI":"10.1109\/CVPR52733.2024.01100"},{"key":"10.1016\/j.bspc.2026.110391_b34","doi-asserted-by":"crossref","DOI":"10.1109\/TCBBIO.2025.3578334","article-title":"Multimodal fusion framework based on low-rank interaction for tumor prognostic prediction","author":"An","year":"2025","journal-title":"IEEE Trans. Comput. Biology Bioinform."},{"issue":"9","key":"10.1016\/j.bspc.2026.110391_b35","doi-asserted-by":"crossref","first-page":"2552","DOI":"10.1109\/TMI.2023.3262024","article-title":"FAM3L: Feature-aware multi-modal metric learning for integrative survival analysis of human cancers","volume":"42","author":"Shao","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110391_b36","article-title":"HSFSurv: A hybrid supervision framework at individual and feature levels for multimodal cancer survival analysis","author":"Fu","year":"2025","journal-title":"Med. Image Anal."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009456?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009456?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:14:41Z","timestamp":1778825681000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":36,"alternative-id":["S1746809426009456"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110391","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CD-AHF: A causal-driven adaptive hierarchical fusion framework for multimodal cancer prognosis","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110391","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110391"}}