{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:42:55Z","timestamp":1754163775726,"version":"3.41.2"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819506941"},{"type":"electronic","value":"9789819506958"}],"license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-0695-8_9","type":"book-chapter","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:54:23Z","timestamp":1753966463000},"page":"98-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Survival Prediction Model Integrating Hierarchical Pathological Image and Pathway Features"],"prefix":"10.1007","author":[{"given":"Xinyue","family":"Xu","sequence":"first","affiliation":[]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Mostavi, M., et al.: Convolutional neural network models for cancer type prediction based on gene expression. BMC Med. Genom. 13, 1\u201313 (2020)","DOI":"10.1186\/s12920-020-0677-2"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Kent, D.M., et al.: Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials. Int. J. Epidemiol. 45(6), 2075\u20132088 (2016)","DOI":"10.1093\/ije\/dyw118"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Fisher, R., Pusztai, L., Swanton, C.: Cancer heterogeneity: implications for targeted therapeutics. Br. J. Cancer 108(3), 479\u2013485 (2013)","DOI":"10.1038\/bjc.2012.581"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Peng, Y., et al.: Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients. World J. Surg. Oncol. 18, 1\u20138 (2020)","DOI":"10.1186\/s12957-020-01909-5"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Chen, P., et al.: Cellular architecture on whole slide images allows the prediction of survival in lung adenocarcinoma. In: International Workshop on Computational Mathematics Modeling in Cancer Analysis, pp. 1\u201310. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-17266-3_1"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Jiang, R., et al.: A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas. Nat. Mach. Intell. 6(8), 876\u2013891 (2024)","DOI":"10.1038\/s42256-024-00868-w"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Shao, Z., et al.: Hvtsurv: hierarchical vision transformer for patient-level survival prediction from whole slide image. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 2 (2023)","DOI":"10.1609\/aaai.v37i2.25315"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: 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 (2021)","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40(8), 865\u2013878 (2022)","DOI":"10.1016\/j.ccell.2022.07.004"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: The IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"9_CR11","unstructured":"Zaheer, M., et al.: Deep sets. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"9_CR13","unstructured":"Shao, Z., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. In: Advances in Neural Information Processing Systems, vol. 34, pp. 2136\u20132147 (2021)"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Lu, M.Y., et al.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomedical Eng. 5(6), 555\u2013570 (2021)","DOI":"10.1038\/s41551-020-00682-w"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: Explainable survival analysis with convolution-involved vision transformer. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 2, pp. 2207\u20132215 (2022)","DOI":"10.1609\/aaai.v36i2.20118"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Zadeh, S.G., Schmid, M.: Bias in cross-entropy-based training of deep survival networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(9), 3126\u20133137 (2020)","DOI":"10.1109\/TPAMI.2020.2979450"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Shin, J., et al.: DRPreter: interpretable anticancer drug response prediction using knowledge-guided graph neural networks and transformer. Int. J. Mol. Sci. 23(22), 13919 (2022)","DOI":"10.3390\/ijms232213919"},{"key":"9_CR19","unstructured":"Han, D., et al.: Demystify mamba in vision: A linear attention perspective. arXiv preprint arXiv:2405.16605 (2024)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113\u20131120 (2013)","DOI":"10.1038\/ng.2764"},{"key":"9_CR21","unstructured":"Wang, J., Zucker, J.-D.: Solving multiple-instance problem: a lazy learning approach, pp. 1119\u20131125 (2000)"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Anders, S., Huber, W.: Differential expression analysis for sequence count data. Nat. Precedings, pp. 1\u20131 (2010)","DOI":"10.1038\/npre.2010.4282.2"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Subramanian, A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102(43), 15545\u201315550 (2005)","DOI":"10.1073\/pnas.0506580102"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Whole slide images are 2d point clouds: context-aware survival prediction using patch-based graph convolutional networks. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI, vol. 24, pp. 339\u2013349 (2021)","DOI":"10.1007\/978-3-030-87237-3_33"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962\u20131971 (2016)","DOI":"10.1109\/TMI.2016.2529665"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0695-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:54:28Z","timestamp":1753966468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0695-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"ISBN":["9789819506941","9789819506958"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0695-8_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"assertion":[{"value":"1 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Helsinki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.helsinki.fi\/en\/conferences\/isbra2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}