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However, the extent to which finite-deformation 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\u2013diffusion without mechanics and reaction\u2013diffusion 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\u2013diffusion 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.<\/jats:p>","DOI":"10.1007\/s10237-026-02121-2","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T15:09:21Z","timestamp":1788188961000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic image-informed selection of biomechanical tumor growth models"],"prefix":"10.1007","volume":"25","author":[{"given":"Abdullah Al","family":"Noman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pratyush Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"II","given":"David A.","family":"Hormuth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danial","family":"Faghihi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"issue":"11","key":"2121_CR1","doi-asserted-by":"publisher","first-page":"e2021WR029775","DOI":"10.1029\/2021WR029775","volume":"57","author":"A Alghamdi","year":"2021","unstructured":"Alghamdi A, Hesse MA, Chen J, Villa U, Ghattas O (2021) Bayesian poroelastic aquifer characterization from insar surface deformation data. 2. quantifying the uncertainty. 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