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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Enhancing Survival Prediction: The Potential of Whole-Brain Radiomics in Multimodal Neuroimaging.

Gleb Danilov1, Diana Kalaeva2, Nina Vikhrova2

  • 1Laboratory of Biomedical Informatics and Artificial Intelligence, Moscow, Russian Federation.

Studies in Health Technology and Informatics
|April 9, 2025
PubMed
Summary

Whole-brain radiomics shows promise for predicting overall survival (OS) and progression-free survival (PFS) in brain glioma patients. Imaging biomarkers alone yielded the best predictive models, outperforming clinical data alone.

Keywords:
Radiomicsmachine learningneuroimagingneurosurgerysurvival prediction

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Radiomics offers potential for improving prognostic predictions in glial brain tumors.
  • The prognostic value of whole-brain imaging biomarkers requires further investigation.

Purpose of the Study:

  • To assess the predictive capability of radiomics for overall survival (OS) and progression-free survival (PFS) in brain glioma patients.
  • To compare the performance of models using clinical features, radiomic biomarkers, and combined approaches.

Main Methods:

  • Development and comparison of 13 prognostic models for OS and PFS.
  • Utilized clinical data, whole-brain radiomic features, and combined datasets.
  • Evaluated model performance using C-index.

Main Results:

  • Achieved high predictive accuracy with C-index values of 0.900 for OS and 0.903 for PFS.
  • Models based solely on imaging biomarkers demonstrated superior performance.
  • Models relying exclusively on clinical data showed the lowest predictive quality.

Conclusions:

  • Whole-brain radiomics shows significant promise for predicting survival outcomes in brain gliomas.
  • Imaging biomarkers derived from radiomics are highly valuable for prognostic modeling.
  • Further research is needed to validate the reproducibility of whole-brain radiomic models due to limited data.