{"scope":{"layer":"brain_tumor","tissue_scope":"brain_tumor","species":"human","system":"in_silico","evidence_level":"peer_reviewed","importance":"","importance_reason":""},"id":"VC0154","type":"paper","title":"Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks","authors_or_org":"Cerrone D, Riccobelli D, Gazzoni S, Vitullo P, Ballarin F, Falco J, Acerbi F, Manzoni A, Zunino P, Ciarletta P.","date":"2025-06-03","year":"2025","venue":"Mathematical Biosciences","url":"https://doi.org/10.1016/j.mbs.2025.109468","doi":"10.1016/j.mbs.2025.109468","pmid":"40473100","brain_tumor_focus":"yes","tags":["GBM","digital-twin","mechanistic","reduced-order-model","neural-network","MRI"],"tag_slugs":["gbm","digital-twin","mechanistic","mri"],"tag_labels":["GBM","Digital twin","Mechanistic & agent-based model","MRI"],"tag_labels_zh":["胶质母细胞瘤","数字孪生","机制与基于主体的模型","磁共振成像"],"unmapped_tags":["reduced-order-model","neural-network"],"one_line_summary_en":"Cerrone et al. (Math Biosci 2025): diffuse-interface (Cahn–Hilliard-type) two-phase GBM growth model with oxygen reaction–diffusion and DTI-derived anisotropic tensors, reduced with a 20-function POD basis plus neural networks (POD-NN, ~150x faster than the full-order finite-element solver: 5 s vs 780 s); an inverse network estimates 6 patient-specific parameters from tumor distributions at two time points 20 days apart. The authors report ~96% tumor-volume forecasting accuracy and ~15% error on normalised parameters; Morris sensitivity: proliferation and oxygen-consumption rates dominate, oxygen supply moderate; a local Monte Carlo check (1% input perturbation, single reference parameter set) shows larger uncertainty for hypoxia threshold and brain stiffness. Limits: trained and validated on synthetic data only (one clinical case's anatomy used for the mesh); no treatment effects (real follow-up patients received Stupp therapy) and no phenotypic heterogeneity; clinical MRI follow-up is every two months, unlike the 20-day model interval; real-patient application is an ongoing study (GLIOMATH prospective observational trial).","one_line_summary_zh":"Cerrone 等（Math Biosci 2025）：基于弥散界面（Cahn–Hilliard 型）的两相 GBM 生长模型，耦合氧反应–扩散及 DTI 导出的各向异性张量；以 20 个基函数的 POD 降阶基加神经网络（POD-NN）近似正向映射，比全阶有限元求解器快约 150 倍（5 秒对 780 秒）；逆向网络由相隔 20 天的两个时间点的肿瘤分布估计 6 个患者特异参数。作者报告肿瘤体积预测准确度约 96%，归一化参数误差约 15%；Morris 敏感性分析显示增殖率与耗氧率起主导作用，供氧中等；局部蒙特卡洛检验（输入 1% 扰动、单一参考参数组）显示缺氧阈值与脑组织刚度的不确定性较大。局限：仅在合成数据上训练与验证（网格取自一例临床病例的解剖结构）；未建模治疗效应（真实随访患者接受了 Stupp 方案）及表型异质性；临床 MRI 随访间隔为两个月，而模型间隔为 20 天；真实患者应用仍在进行中的研究中（GLIOMATH 前瞻性观察性试验）。","verified":"yes","verification_source":"arXiv API 2412.05330 (journal_ref Mathematical Biosciences 387:109468 (2025); v1 2024-12-04, v2 2025-04-01) + DOI 10.1016/j.mbs.2025.109468 + full text read 2026-10-19; Crossref (title, 10 authors, vol 387, art. 109468, issue 2025-09, VOR licence start 2025-06-03) + PubMed PMID 40473100 (epub 2025 Jun 3, print 2025 Sep; title/journal match); date = online publication date","page_url":"https://gliostate.com/library/VC0154/","api_url":"https://gliostate.com/api/items/VC0154.json","sameAs":["https://doi.org/10.1016/j.mbs.2025.109468","https://pubmed.ncbi.nlm.nih.gov/40473100/"],"is_preprint":false,"facets":{"type":"https://gliostate.com/library/type/paper/","year":"https://gliostate.com/library/year/2025/","tags":["https://gliostate.com/library/tag/gbm/","https://gliostate.com/library/tag/digital-twin/","https://gliostate.com/library/tag/mechanistic/","https://gliostate.com/library/tag/mri/"]},"cited_by":[],"related":[{"id":"VC0077","title":"Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.","shared_tags":["mri","digital-twin"],"page_url":"https://gliostate.com/library/VC0077/","api_url":"https://gliostate.com/api/items/VC0077.json"},{"id":"VC0071","title":"A Proteome-Calibrated Mechanistic Digital Twin of the Glioblastoma Astrocyte Derived from Patient Multi-Omic Data","shared_tags":["gbm","digital-twin","mechanistic"],"page_url":"https://gliostate.com/library/VC0071/","api_url":"https://gliostate.com/api/items/VC0071.json"},{"id":"VC0075","title":"Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion.","shared_tags":["mechanistic","digital-twin"],"page_url":"https://gliostate.com/library/VC0075/","api_url":"https://gliostate.com/api/items/VC0075.json"},{"id":"VC0098","title":"Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction.","shared_tags":["mri"],"page_url":"https://gliostate.com/library/VC0098/","api_url":"https://gliostate.com/api/items/VC0098.json"},{"id":"VC0035","title":"CellFluxV2 : An Image Generative Foundation Model for Virtual Cell Modeling","shared_tags":["mri"],"page_url":"https://gliostate.com/library/VC0035/","api_url":"https://gliostate.com/api/items/VC0035.json"},{"id":"VC0099","title":"A foundation model for brain tumor MRI analysis: WHO grading and subtype classification.","shared_tags":["mri"],"page_url":"https://gliostate.com/library/VC0099/","api_url":"https://gliostate.com/api/items/VC0099.json"}]}