[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81901-en":3,"doc-seo-81901-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81901,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Dynamic Image Informed Selection of Biomechanical Tumor Growth Models","Glioblastoma progression depends on evolving mechanical interactions between tumor tissue and the surrounding brain, yet it remains unclear how finite-deformation mechanics and constitutive assumptions affect subject-specific prediction over time. A sequential Bayesian inference and dynamic model selection framework assimilates longitudinal murine MRI to calibrate spatially varying tumor diffusivity, proliferation, and tissue stiffness. Reaction–diffusion models are compared with mechanics coupled variants for one-scan-ahead forecasting, using posterior plausibility to adapt model choice as new scans arrive. Mechanically coupled formulations are consistently more plausible, while linear and hyperelastic assumptions differ mainly in inferred stress, deformation, and stiffness fields, with hyperelastic often favored later.","Dynamic Image-Informed Selection of Biomechanical  \nTumor Growth Models  \nAbdullah Al Nomana , Pratyush Kumar Singha , David A Hormuth, IIb ,  \narXiv :2607 .0455 1v 1 [ cs .CE] 5 Jul 2026  \nDanial Faghihia,∗  \na Department of Mechanical and Aerospace Engineering,  \nUniversity at Buffalo, Buffalo, NY, USA  \nb Oden Institute for Computational Engineering and Sciences, Livestrong Cancer Institutes,  \nUniversity of Texas at Austin, Austin, TX, USA  \nAbstract  \nGlioblastoma progression is strongly influenced by evolving mechanical interactions between the tumor and surrounding brain tissue. However, the extent to which finitedeformation mechanics and constitutive assumptions improve subject-specific prediction as tumor burden evolves remains unclear. We introduce a sequential Bayesian inference and dynamic model selection framework that assimilates longitudinal murine magnetic resonance imaging (MRI) data to calibrate spatially varying tumor diffusivity, proliferation rate, and tissue stiffness in biomechanical tumor growth models. Competing formulations were compared at each imaging time, including reaction–diffusion without mechanics and reaction–diffusion coupled to linear elasticity or hyperelastic mechanics, using posterior model plausibility to adapt model choice for individualized one-scan-ahead prediction as new MRI scans are acquired. Across the studied animals, mechanically coupled models were consistently more plausible than the uncoupled reaction-–diffusion model, and the evolution of model plausibility indicated an increasing role of mass effect and stress-mediated feedback of tumor growth during progression. While linear and hyperelastic coupled tumor growth models often produced similar tumor morphology, they yield distinct stress, deformation, and inferred stiffness fields, with the hyperelastic formulation often receiving higher posterior plausibility at later imaging times. These results indicate that, within the present longitudinal murine dataset, mechanical coupling is favored for image-informed glioma growth prediction and that constitutive assumptions should be evaluated sequentially for each subject rather than fixed a priori.  \nKeywords: Biomechanical tumor growth, Finite deformation elasticity, Dynamic model selection, Image-informed modeling  \n∗ Corresponding Author, [danialfa@buffalo.edu](danialfa@buffalo.edu) (D. Faghihi) Submitted to Biomechanics and modeling in mechanobiology  \n1. Introduction  \nGlioblastoma (GBM), the most aggressive primary brain tumor, exhibits irregular and strongly subject-specific growth patterns driven by heterogeneous proliferation and invasion. A growing body of work in mechanobiology and biomechanics indicates that these patterns are governed by mechanical interaction with the confined brain and an evolving microenvironmental stiffness [1, 2, 3, 4] . As GBM expands within the confined cranial cavity, solid stresses develop due to internal growth heterogeneity and external constraint from surrounding tissue. These stresses have been shown to modulate proliferation, compress vasculature, and promote invasive phenotypes through mechanotransduction [5, 6, 7, 8] . Capturing this coupled tumor-tissue interaction is therefore central to predictive, biomechanical models of GBM progression and to mechanistic interpretation of growth patterns observed in longitudinal imaging [1, 2] .  \nMany current continuum tumor growth models for predicting GBMs rely on reaction– diffusion (RD) formulations [9] that represent proliferation and invasion but do not explicitly account for mass effect. Mechanically coupled extensions have incorporated deformation by coupling RD dynamics to small-strain linear elasticity mechanics and calibrating model parameters from imaging data [10, 11, 12, 13] . More recent biomechanics models have introduced more comprehensive descriptions, including multiphase models coupled with finite-deformation elasticity, to better represent soft-tissue mechanics and quantify ","cbCaibQzsKsdGf9N","https://ap.wps.com/l/cbCaibQzsKsdGf9N","pdf",13157523,3,1,33,"English","en",105,"# Introduction\n## Tumor–tissue mechanics and mechanobiology\n## Continuum tumor growth models and constitutive assumptions\n## Sequential Bayesian inference and dynamic model selection","[{\"question\":\"What is the main goal of the proposed framework for glioblastoma growth modeling?\",\"answer\":\"To assimilate longitudinal MRI data with sequential Bayesian inference and dynamically select among competing biomechanical tumor growth models for individualized one-scan-ahead prediction.\"},{\"question\":\"Which model classes are compared in the study?\",\"answer\":\"Reaction–diffusion without mechanics is compared against reaction–diffusion coupled to linear elasticity or hyperelastic mechanics, with posterior model plausibility recomputed at each imaging time.\"},{\"question\":\"How do linear and hyperelastic mechanics differ in the study’s findings?\",\"answer\":\"They often produce similar tumor morphology, but they yield distinct stress, deformation, and inferred tissue stiffness fields, with hyperelastic frequently receiving higher posterior plausibility at later times.\"}]","Dynamic Image Informed Selection of Biomechanical Tumor Growth Models | 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is the main goal of the proposed framework for glioblastoma growth modeling?","Question",{"text":76,"@type":77},"To assimilate longitudinal MRI data with sequential Bayesian inference and dynamically select among competing biomechanical tumor growth models for individualized one-scan-ahead prediction.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which model classes are compared in the study?",{"text":81,"@type":77},"Reaction–diffusion without mechanics is compared against reaction–diffusion coupled to linear elasticity or hyperelastic mechanics, with posterior model plausibility recomputed at each imaging time.",{"name":83,"@type":74,"acceptedAnswer":84},"How do linear and hyperelastic mechanics differ in the study’s findings?",{"text":85,"@type":77},"They often produce similar tumor morphology, but they yield distinct stress, deformation, and inferred tissue stiffness fields, with hyperelastic frequently receiving higher posterior plausibility at later 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