paperBrain tumor✓ VerifiedVC0077
Laslo D et al. · BMC medicine · 2026-05-18
Generative AI forecasting spatial tumor growth on MRI in pediatric DMG.
DOIPubMed
paperBrain tumor✓ VerifiedVC0154
Cerrone D, Riccobelli D, Gazzoni S, Vitullo P, Ballarin F, Falco J, Acerbi F, Manzoni A, Zunino P, Ciarletta P. · Mathematical Biosciences · 2025-06-03
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).
DOIPubMed
conferenceBrain tumor✓ VerifiedVC0122
Society for Neuro-Oncology · Philadelphia, PA (to Nov 15) · 2026-11-12
SNO 2026, Nov 12-15, Philadelphia; SNO 2027 Nov 18-21 Phoenix.
Source
preprintBrain tumor✓ VerifiedVC0176
Huang X, Qiao Y, Black GS, Sederman CR, Yang H, Musci T, Ludwick A, He G, Youssef O, Gutierrez M, Yang J, Chan PP, Memmott TH, Whipple NS, Bruggers CS, Quinlan A, Moos PJ, Cheshier SH, Marth GT. · bioRxiv (preprint, not peer-reviewed) · 2026-10-14
Preprint (not peer-reviewed): Huang et al. (University of Utah; senior authors Cheshier SH and Marth GT) describe a tiered framework combining deep whole-genome sequencing, scRNA-seq and ex vivo functional drug screening, applied to 17 children with malignant CNS tumors. Functional screening gave every patient at least one high-confidence targeted-therapy or chemotherapy nomination; scRNA-seq identified on average 37 druggable gene targets per patient, a fourfold increase in high-confidence targeted-therapy recommendations. In an exploratory analysis, functional classifications were directionally consistent with outcomes in 8 of 10 evaluable cases, but combination treatments and radiation precluded causal attribution.
DOI
conferenceBrain tumor✓ VerifiedVC0123
European Association of Neuro-Oncology · Rome, Italy (to Sep 27) · 2026-09-24
EANO 2026 Sep 24-27, Rome (already passed).
Source
paperPartly relevant✓ VerifiedVC0028
Huang T, Liu T, Babadi M, Ying R, Jin W. · NPJ digital medicine · 2025-11-14
STPath: generative model linking WSIs and spatial transcriptomics.
DOIPubMed