ReviewBrain sciences2025
Spatial Mapping of Glioblastoma Infiltration: Diffusion Tensor Imaging-Based Radiomics and Connectomics in Recurrence Prediction.
Review in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis.Journal of neuro-oncology · 2025Pooled it
- White matter pathlength maps from diffusion-weighted MRI tractography for radiotherapy target planning in glioblastoma.Journal of neuro-oncology · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
- Long-term clinical prognosis of patients with gliomas infiltrating corticospinal tract on DTI tractography.Chinese neurosurgical journal · 2025Article
- Validation of open-source deep learning segmentation tools for automated glioma volumetry: a narrative review of Dice scores, workflow efficiency, and clinical RANO 2.0 implementation.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma (GBM) often exhibits distinct anatomical patterns of relapse after radiotherapy. Tumour cell migration along myelinated white matter tracts is a key driver of disease progression. The failure of conventional imaging to capture subclinical infiltration has driven interest in advanced imaging biomarkers capable of quantifying tumour-brain interactions. Diffusion tensor imaging (DTI), radiomics, and connectomics represent a triad of innovative, non-invasive approaches that map white matter architecture, predict recurrence risk, and inform biologically guided treatment strategies. This review examines the biological rationale and clinical applications of DTI-based metrics, radiomic signatures, and tractography-informed connectomics in GBM. We discuss the integration of these modalities into machine learning frameworks and radiotherapy/surgical planning, supported by landmark studies and multi-institutional data. The implications for personalised neuro-oncology are profound, marking a shift towards risk-adaptive, tract-aware treatment strategies that may improve local control and preserve neurocognitive function.
Indexed as
Identifiers
What OpenQuestion holds
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.