ReviewNeurosurgical review2026
Current applications of radiomics in neurotrauma.
Review in Neurosurgical review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is an emerging clinical application in medicine that extracts quantitative features from diagnostic imaging modalities to reveal patterns beyond human qualitative perception. While radiomic applications have been well developed in neuro-oncology, their relevance to other neurosurgical subspecialties and broader utility remain unexplored. Particularly, traumatic brain and spinal cord injuries demand rapid, high-stakes decisions often based on subtle or evolving imaging findings. Radiomics offers the potential to enhance real-time decision-making through predictive analytics and risk stratification. This review explores recent advances in radiomics for neurotrauma, highlighting its feasibility, barriers to clinical adoption, and pathways for integration into patient care. A comprehensive systematic search of PubMed, OVID, and Google Scholar was conducted through November 2025 using keywords related to radiomics and neurotrauma, including traumatic brain injury (TBI) and spinal cord injury (SCI). Studies were included if they involved human subjects and applied radiomics for predictive modeling in TBI or SCI. Non-traumatic, oncologic, degenerative, or purely descriptive studies were excluded. Reference lists were manually reviewed to ensure completeness, and the included studies were then categorized by TBI or SCI focus. Study quality and risk of bias were assessed using the PROBAST (Prediction model Risk of Bias Assessment Tool). Predictive models have shown significant promise in diagnosing neurological conditions, estimating prognosis, neurological recovery, in-hospital mortality, injury progression, intracranial hypertension, and diffuse axonal injury severity. Of the twenty-three studies that met the inclusion criteria, eight were multicenter studies. All but one study was retrospective, with six undergoing external validation. Radiomic models demonstrated strong predictive performance with all included studies reporting AUC values of greater than 0.80 (100%). Furthermore, eight studies that combined multi-omic modalities achieved high AUCs greater than 0.90. Radiomics can provide a tool to improve current clinical models to provide more accurate diagnosis, prediction of recovery, and risk stratification in neurotrauma. However, the current evidence is largely single-centered and retrospective without a clear direction for clinical translation and limited generalizability. Further research will need to standardize current protocols to create replicable models that can be applied across institutions.
Indexed as
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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.