{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T19:53:57Z","timestamp":1791575637558,"version":"4.3.4"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20386"],"award-info":[{"award-number":["U21A20386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82371941"],"award-info":[{"award-number":["82371941"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.cmpb.2026.109668","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:09:58Z","timestamp":1790698198000},"page":"109668","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Transformer-based decision support with CNN and ViT for prognostic risk stratification following adjuvant chemotherapy in high-grade glioma: a multicenter study"],"prefix":"10.1016","volume":"288","author":[{"given":"Zehui","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bihua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenju","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqiang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangfeng","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangli","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2026.109668_bib0001","article-title":"The diagnostic value of advanced tracer kinetic models in evaluating high grade gliomas recurrence and treatment response using dynamic contrast-enhanced MRI","volume":"15","author":"Zhou","year":"2025","journal-title":"Front. Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0002","doi-asserted-by":"crossref","first-page":"e842","DOI":"10.1016\/j.crad.2024.02.011","article-title":"Metabolic habitat imaging with hemodynamic heterogeneity predicts individual progression-free survival in high-grade glioma","volume":"79","author":"Qiao","year":"2024","journal-title":"Clin. Radiol."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109668_bib0003","article-title":"Temozolomide for high grade glioma","volume":"2013","author":"Hart","year":"2013","journal-title":"Cochrane Database Syst. Rev."},{"issue":"10","key":"10.1016\/j.cmpb.2026.109668_bib0004","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1056\/NEJMoa043330","article-title":"Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma","volume":"352","author":"Stupp","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0005","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1056\/NEJMoa1611977","article-title":"Short-course radiation plus temozolomide in elderly patients with glioblastoma","volume":"376","author":"Perry","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0006","doi-asserted-by":"crossref","DOI":"10.1101\/cshperspect.a026583","article-title":"Spatial heterogeneity in the tumor microenvironment","volume":"6","author":"Yuan","year":"2016","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0007","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1093\/neuonc\/noae227","article-title":"Prospective longitudinal analysis of physiologic MRI-based tumor habitat predicts short-term patient outcomes in IDH-wildtype glioblastoma","volume":"27","author":"Moon","year":"2025","journal-title":"Neuro Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0008","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1038\/nrneurol.2014.100","article-title":"MGMT testing\u2014the challenges for biomarker-based glioma treatment","volume":"10","author":"Wick","year":"2014","journal-title":"Nat. Rev. Neurol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0009","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1002\/cncr.31630","article-title":"Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats","volume":"124","author":"Napel","year":"2018","journal-title":"Cancer"},{"key":"10.1016\/j.cmpb.2026.109668_bib0010","doi-asserted-by":"crossref","first-page":"6374","DOI":"10.1007\/s00330-021-07718-y","article-title":"Spatiotemporal habitats from multiparametric physiologic MRI distinguish tumor progression from treatment-related change in post-treatment glioblastoma","volume":"31","author":"Kim","year":"2021","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0011","doi-asserted-by":"crossref","first-page":"112992","DOI":"10.18632\/oncotarget.22947","article-title":"Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma","volume":"8","author":"Dextraze","year":"2017","journal-title":"Oncotarget"},{"key":"10.1016\/j.cmpb.2026.109668_bib0012","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1016\/j.acra.2024.09.021","article-title":"Deep learning and habitat radiomics for the prediction of glioma pathology using multiparametric MRI: a multicenter study","volume":"32","author":"Zhu","year":"2025","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0013","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s00262-024-03724-3","article-title":"Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients","volume":"73","author":"Cai","year":"2024","journal-title":"Cancer Immunol. Immunther."},{"key":"10.1016\/j.cmpb.2026.109668_bib0014","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1202170","article-title":"Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis","volume":"13","author":"Liu","year":"2023","journal-title":"Front. Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0015","doi-asserted-by":"crossref","first-page":"2073","DOI":"10.1002\/acn3.52128","article-title":"Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas","volume":"11","author":"Yang","year":"2024","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0016","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.3174\/ajnr.A7591","article-title":"Stable and discriminatory radiomic features from the tumor and its habitat associated with progression-free survival in glioblastoma: a multi-institutional study","volume":"43","author":"Verma","year":"2022","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0017","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1093\/neuonc\/noaa177","article-title":"Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics","volume":"23","author":"Choi","year":"2021","journal-title":"Neuro Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0018","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.cmpb.2026.109668_bib0019","series-title":"Proceedings of the 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence and Networking (QPAIN)","first-page":"1","article-title":"Classification of watermelon diseases using a customized convolutional neural network","author":"Reza","year":"2026"},{"key":"10.1016\/j.cmpb.2026.109668_bib0020","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2023","journal-title":"IEEe Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cmpb.2026.109668_bib0021","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., et al. \u201cAttention Is All You Need.\u201d Advances in Neural Information Processing Systems, 2017, pp. 5998-6008.https:\/\/arxiv.org\/abs\/1706.03762."},{"key":"10.1016\/j.cmpb.2026.109668_bib0022","unstructured":"Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., et al. An image is worth 16x16 words: transformers for image recognition at scale. ArXiv:2010.11929. 2020.10.48550\/arXiv.2010.11929."},{"key":"10.1016\/j.cmpb.2026.109668_bib0023","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2026.3690009","article-title":"Breaking the black box: interpretable AI achieves superior hemorrhage detection with the compensatory reserve measurement","author":"Tibbs","year":"2026","journal-title":"IEEe J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2026.109668_bib0024","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.ccell.2023.08.002","article-title":"Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study","volume":"41","author":"Wagner","year":"2023","journal-title":"Cancer Cell"},{"key":"10.1016\/j.cmpb.2026.109668_bib0025","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-78363-w","article-title":"Fusing global context with multiscale context for enhanced breast cancer classification","volume":"14","author":"Islam","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2026.109668_bib0026","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/JBHI.2022.3225416","article-title":"A 2.5D deep learning-based method for drowning diagnosis using post-mortem computed tomography","volume":"27","author":"Zeng","year":"2023","journal-title":"IEEe J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2026.109668_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109872","article-title":"Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models","volume":"188","author":"Elhadidy","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0028","article-title":"Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction","volume":"15","author":"Fan","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2026.109668_bib0029","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40644-025-00829-5","article-title":"Preoperative prediction of IDH genotypes and prognosis in adult-type diffuse gliomas: intratumor heterogeneity habitat analysis using dynamic contrast-enhanced MRI and diffusion-weighted imaging","volume":"25","author":"Wang","year":"2025","journal-title":"Cancer ImAging"},{"key":"10.1016\/j.cmpb.2026.109668_bib0030","doi-asserted-by":"crossref","first-page":"3237","DOI":"10.1016\/j.acra.2025.02.021","article-title":"Deep learning-assisted diagnosis of malignant cerebral edema following endovascular thrombectomy","volume":"32","author":"Song","year":"2025","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0031","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1515\/revneuro-2024-0088","article-title":"Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks","volume":"36","author":"Zhu","year":"2025","journal-title":"Rev. Neurosci."},{"key":"10.1016\/j.cmpb.2026.109668_bib0032","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1158\/1078-0432.CCR-09-1029","article-title":"New insights into checkpoint kinase 1 in the DNA damage response signaling network","volume":"16","author":"Dai","year":"2010","journal-title":"Clin. Cancer Res."},{"key":"10.1016\/j.cmpb.2026.109668_bib0033","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s13046-022-02334-0","article-title":"Flap endonuclease 1 and DNA-PKcs synergistically participate in stabilizing replication fork to encounter replication stress in glioma cells","volume":"41","author":"Zhang","year":"2022","journal-title":"J. Exp. Clin. Cancer Res."},{"key":"10.1016\/j.cmpb.2026.109668_bib0034","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-10744-1","article-title":"Identification and evaluation of pyrimidine based CDK6 inhibitors against glioblastoma using integrated computational approaches","volume":"15","author":"Manzoor","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2026.109668_bib0035","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.3390\/cancers15225460","article-title":"Current knowledge about the peritumoral microenvironment in glioblastoma","volume":"15","author":"Trevisi","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.cmpb.2026.109668_bib0036","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1016\/S1470-2045(16)30313-8","article-title":"TMZ chemotherapy versus radiotherapy in high-risk low-grade glioma (EORTC 22033-26033): a randomised, open-label, phase 3 intergroup study","volume":"17","author":"Baumert","year":"2016","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.bbcan.2021.188616","article-title":"Elucidating the mechanisms of TMZ resistance in gliomas and the strategies to overcome the resistance","volume":"1876","author":"Tomar","year":"2021","journal-title":"Biochim. Biophys. Acta Rev. Cancer"},{"key":"10.1016\/j.cmpb.2026.109668_bib0038","first-page":"1529","article-title":"Long-term analysis of the NOA-04 randomized phase III trial of sequential radiochemotherapy of anaplastic glioma with PCV or TMZ","volume":"18","author":"Wick","year":"2016","journal-title":"Neuro Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0039","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00330-018-5575-z","article-title":"A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication","volume":"29","author":"Wei","year":"2019","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0040","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15153845","article-title":"Radiomics and machine learning in brain tumors and their habitat: a systematic review","volume":"15","author":"Tabassum","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.cmpb.2026.109668_bib0041","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1158\/1078-0432.CCR-19-2556","article-title":"Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma","volume":"26","author":"Beig","year":"2020","journal-title":"Clin. Cancer Res."},{"key":"10.1016\/j.cmpb.2026.109668_bib0042","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.3390\/cancers14061475","article-title":"Efficient radiomics-based classification of multi-parametric MR images to identify volumetric habitats and signatures in glioblastoma: a machine learning approach","volume":"14","author":"Chiu","year":"2022","journal-title":"Cancers"},{"key":"10.1016\/j.cmpb.2026.109668_bib0043","article-title":"Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study","volume":"14","author":"Wu","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2026.109668_bib0044","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ebiom.2019.05.023","article-title":"Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy","volume":"44","author":"Xie","year":"2019","journal-title":"EBioMedicine"},{"key":"10.1016\/j.cmpb.2026.109668_bib0045","doi-asserted-by":"crossref","first-page":"327","DOI":"10.2967\/jnumed.119.230037","article-title":"Tumor subregion evolution-based imaging features to assess early response and predict prognosis in oropharyngeal cancer","volume":"61","author":"Wu","year":"2020","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0046","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190168","article-title":"Tumor habitat-derived radiomic features at pretreatment MRI that are prognostic for progression-free survival in glioblastoma are associated with key morphologic attributes at histopathologic examination: a feasibility study","volume":"2","author":"Verma","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.cmpb.2026.109668_bib0047","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0048","first-page":"3034","article-title":"CoAtNet: marrying convolution and attention for all data sizes","volume":"34","author":"Dai","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2026.109668_bib0049","article-title":"AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer's screening","volume":"12","author":"Kishor","year":"2025","journal-title":"Front. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0050","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s13278-023-01095-8","article-title":"Relation extraction: advancements through deep learning and entity-related features","volume":"13","author":"Zhao","year":"2023","journal-title":"Soc. Netw. Anal. Min."},{"key":"10.1016\/j.cmpb.2026.109668_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102802","article-title":"Transformers in medical imaging: a survey","volume":"88","author":"Shamshad","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2026.109668_bib0052","doi-asserted-by":"crossref","DOI":"10.1002\/fsn3.70513","article-title":"Enhanced maize leaf disease detection and classification using an integrated CNN-ViT model","volume":"13","author":"Shandilya","year":"2025","journal-title":"Food Sci. Nutr."},{"key":"10.1016\/j.cmpb.2026.109668_bib0053","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1007\/s00330-023-10378-9","article-title":"Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model","volume":"34","author":"Wu","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0054","doi-asserted-by":"crossref","DOI":"10.1038\/s41698-026-01538-3","article-title":"Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma","author":"Zhang","year":"2026","journal-title":"npj Precis. Oncol."},{"key":"10.1016\/j.cmpb.2026.109668_bib0055","doi-asserted-by":"crossref","first-page":"978","DOI":"10.3390\/bioengineering12090978","article-title":"PHSP-Net: personalized habitat-aware deep learning for multi-center glioblastoma survival prediction using multiparametric MRI","volume":"12","author":"Liu","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.cmpb.2026.109668_bib0056","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1126\/science.1073096","article-title":"Addiction to oncogenes\u2014the Achilles heal of cancer","volume":"297","author":"Weinstein","year":"2002","journal-title":"Science"},{"key":"10.1016\/j.cmpb.2026.109668_bib0057","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/1756-9966-30-58","article-title":"Oncogene addiction in gliomas: implications for molecular targeted therapy","volume":"30","author":"Yan","year":"2011","journal-title":"J. Exp. Clin. Cancer Res."},{"key":"10.1016\/j.cmpb.2026.109668_bib0058","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.3390\/genes14071346","article-title":"Therapeutic targeting of DNA replication stress in cancer","volume":"14","author":"Gu","year":"2023","journal-title":"Genes"},{"key":"10.1016\/j.cmpb.2026.109668_bib0059","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3390\/brainsci15060612","article-title":"Hybrid deep learning architecture with adaptive feature fusion for multi-stage Alzheimer's disease classification","volume":"15","author":"Muhammad","year":"2025","journal-title":"Brain Sci."},{"key":"10.1016\/j.cmpb.2026.109668_bib0060","doi-asserted-by":"crossref","first-page":"456","DOI":"10.3390\/jcm13164800","article-title":"A novel model based on CNN-ViT fusion and ensemble learning for the automatic detection of pes planus","volume":"13","author":"Do\u011fan","year":"2024","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.cmpb.2026.109668_bib0061","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-01072-5","article-title":"Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models","volume":"15","author":"Almalki","year":"2025","journal-title":"Sci. Rep."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726004177?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726004177?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T19:40:29Z","timestamp":1791574829000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726004177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":61,"alternative-id":["S0169260726004177"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109668","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Transformer-based decision support with CNN and ViT for prognostic risk stratification following adjuvant chemotherapy in high-grade glioma: a multicenter study","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109668","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"109668"}}