{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:22:41Z","timestamp":1761952961006,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533978","type":"print"},{"value":"9789819533985","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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\/978-981-95-3398-5_29","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:23Z","timestamp":1761952763000},"page":"352-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatiotemporal Feature Fusion for\u00a0Glioblastoma Recurrence Prediction Using Mamba-Based Dual-Stream Framework"],"prefix":"10.1007","author":[{"given":"Chengwei","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuefei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianci","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junmei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Pan, Z., Zhao, R., et al.: EWSR1-induced circNEIL3 promotes glioma progression and exosome-mediated macrophage immunosuppressive polarization via stabilizing IGF2BP3. Molec. Canc. 21(5), 16 (2022)","DOI":"10.1186\/s12943-021-01485-6"},{"issue":"1","key":"29_CR2","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1093\/neuonc\/noae173","volume":"27","author":"M Mahootiha","year":"2025","unstructured":"Mahootiha, M., Tak, D., Ye, Z., et al.: Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas. Neuro Oncol. 27(1), 277\u2013290 (2025)","journal-title":"Neuro Oncol."},{"issue":"5","key":"29_CR3","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1007\/s40120-023-00524-2","volume":"12","author":"J Ren","year":"2023","unstructured":"Ren, J., Zhai, X., Yin, H., et al.: Multimodality MRI radiomics based on machine learning for identifying true tumor recurrence and treatment-related effects in patients with postoperative glioma. Neurol. Therapy 12(5), 1729\u20131743 (2023)","journal-title":"Neurol. Therapy"},{"key":"29_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.684996","volume":"11","author":"ZH Wang","year":"2021","unstructured":"Wang, Z.H., Xiao, X.L., Zhang, Z.T., et al.: A radiomics model for predicting early recurrence in grade II gliomas based on preoperative multiparametric magnetic resonance imaging. Front. Oncol. 11, 684996 (2021)","journal-title":"Front. Oncol."},{"issue":"1","key":"29_CR5","doi-asserted-by":"publisher","first-page":"10985","DOI":"10.1038\/s41598-024-61925-3","volume":"14","author":"AM Rykkje","year":"2024","unstructured":"Rykkje, A.M., Carlsen, J.F., Larsen, V.A., et al.: Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy. Sci. Rep. 14(1), 10985 (2024)","journal-title":"Sci. Rep."},{"key":"29_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.758622","volume":"12","author":"X Jia","year":"2022","unstructured":"Jia, X., Zhai, Y., Song, D., et al.: A multiparametric MRI-based radiomics nomogram for preoperative prediction of survival stratification in glioblastoma patients with standard treatment. Front. Oncol. 12, 758622 (2022)","journal-title":"Front. Oncol."},{"issue":"4","key":"29_CR7","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1007\/s00261-023-04141-3","volume":"49","author":"J Yang","year":"2024","unstructured":"Yang, J., Dong, X., Wang, F., et al.: A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma. Abdominal Radiol. 49(4), 1074\u20131083 (2024)","journal-title":"Abdominal Radiol."},{"issue":"1","key":"29_CR8","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s12072-023-10585-y","volume":"18","author":"M Sato","year":"2024","unstructured":"Sato, M., Moriyama, M., Fukumoto, T., et al.: Development of a transformer model for predicting the prognosis of patients with hepatocellular carcinoma after radiofrequency ablation. Hep. Intl. 18(1), 131\u2013137 (2024)","journal-title":"Hep. Intl."},{"key":"29_CR9","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752(2023)"},{"key":"29_CR10","unstructured":"Zhu, L., Liao, B., Zhang, Q., et al.: Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417 (2024)"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Cepeda, S., et al.: The R\u00edo Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM). Data Brief 23(50), 109617 (2023). https:\/\/doi.org\/10.1016\/j.dib.2023.109617. PMID: 37808543; PMCID: PMC10551826. https:\/\/www.cancerimagingarchive.net\/","DOI":"10.1016\/j.dib.2023.109617"},{"key":"29_CR12","doi-asserted-by":"publisher","unstructured":"Lucraft, M., Allin, K., Baynes, G.: Challenges and Opportunities for Data Sharing in China. figshare. Journal contribution (2019). https:\/\/doi.org\/10.6084\/m9.figshare.7326605.v3","DOI":"10.6084\/m9.figshare.7326605.v3"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Ressa, G., Levi, R., Savini, G., et al.: AI differentiates radionecrosis from true progression in brain metastasis upon stereotactic radiosurgery: analysis of 124 histologically assessed lesions. Neuro-Oncology, noaf090(2025)","DOI":"10.1093\/neuonc\/noaf090"},{"issue":"1","key":"29_CR14","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1038\/s41598-024-51329-8","volume":"14","author":"ON Oyelade","year":"2024","unstructured":"Oyelade, O.N., Irunokhai, E.A., Wang, H.A.: Twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification. Sci. Rep. 14(1), 692 (2024)","journal-title":"Sci. Rep."},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Kang, G., Liu, K., Hou, B., Zhang, N.: 3D multi-view convolutional neural networks for lung nodule classification. PloS One 12(11), e0188290 (2017)","DOI":"10.1371\/journal.pone.0188290"},{"issue":"03","key":"29_CR16","doi-asserted-by":"publisher","first-page":"2350010","DOI":"10.1142\/S0129065723500107","volume":"33","author":"H Zhu","year":"2023","unstructured":"Zhu, H., Wang, J., Wang, S.H., et al.: An evolutionary attention-based network for medical image classification. Int. J. Neural Syst. 33(03), 2350010 (2023)","journal-title":"Int. J. Neural Syst."},{"key":"29_CR17","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"HY Zhou","year":"2023","unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., et al.: nnFormer: volumetric medical image segmentation via a 3D transformer. IEEE Trans. Image Process. 32, 4036\u20134045 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., et al.: Mednext: transformer-driven scaling of convnets for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 405\u2013415. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_39","DOI":"10.1007\/978-3-031-43901-8_39"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3398-5_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:25Z","timestamp":1761952765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3398-5_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533978","9789819533985"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3398-5_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}