ReviewMolecular brain2026
Organoids as next-generation models for investigating intracranial tumours.
Review in Molecular brain, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumour organoids have emerged as important tools in brain tumour research, addressing long-standing limitations of conventional two-dimensional cultures, xenograft models, and genetically engineered mouse models. By preserving patient-specific genetic alterations, cellular diversity, spatial architecture, and key microenvironmental features, organoid systems enable more faithful modelling of tumour biology across a broad range of intracranial tumours, including gliomas, meningiomas, medulloblastomas, pituitary tumours, and craniopharyngiomas. Patient-derived organoids, genetically engineered models, co-culture systems, and bioprinted platforms have collectively advanced understanding of tumour initiation, invasion, heterogeneity, and therapeutic resistance, while offering clinically relevant systems for drug screening and personalised therapy prediction. Importantly, organoid models facilitate mechanistic interrogation of tumour-microenvironment interactions that are difficult to capture in other systems, including neural-tumour crosstalk, vascular niche formation, and immune modulation. Their compatibility with high-throughput screening and integration with emerging technologies, such as single-cell and spatial omics, CRISPR-based genome editing, microfluidics, and artificial intelligence, has further expanded their utility for functional genomics, biomarker discovery, and predictive modelling of treatment response. The development of large-scale organoid biobanks that represent diverse tumour subtypes and patient populations also provides critical infrastructure for reproducible research and collaborative precision oncology efforts. While challenges remain, including variability in culture protocols, incomplete immune and vascular representation, and barriers related to cost and technical complexity, ongoing methodological innovations are progressively enhancing the physiological fidelity and translational relevance of organoid systems. Overall, tumour organoids represent a promising interface between experimental research and clinical application in neuro-oncology, with significant potential to accelerate therapeutic discovery, refine patient stratification, and ultimately improve outcomes for individuals with brain tumours.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.