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Updated: Apr 19, 2026

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
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Radiomics-based Differentiation of Recurrent Brain Metastases from Treatment Effects: A Multi-Institutional

Hyemin Um1, Marwa Ismail1, Virginia B Hill2

  • 1Department of Radiology, University of Wisconsin-Madison, 1111 Highland Ave, WIMR 2488, Madison, WI 53792.

Radiology. Imaging Cancer
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

Radiomics analysis showed superior accuracy in distinguishing tumor recurrence from treatment effects in brain metastases compared to advanced imaging and RANO-BM criteria. This approach offers improved differentiation across lesion subcompartments for better patient management.

Keywords:
Advanced ImagingBrain MetastasesRANO-BMRadiomicsTreatment Effects

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Area of Science:

  • Neuro-oncology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Differentiating tumor recurrence from treatment effects in brain metastases is crucial for effective patient management.
  • Current methods like advanced imaging and Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria have limitations in accuracy.

Purpose of the Study:

  • To compare the diagnostic performance of radiomics, advanced imaging, and RANO-BM criteria in distinguishing tumor recurrence from treatment effects in brain metastases.
  • To evaluate the effectiveness of radiomics features extracted from different tumor subcompartments.

Main Methods:

  • Retrospective analysis of posttreatment MRI data from 242 patients across three institutions.
  • Extraction of 1104 radiomic features from enhancing lesion, edema, and necrotic core subcompartments.
  • Classification using random forest models and comparative assessment with advanced imaging and RANO-BM criteria.

Main Results:

  • Radiomics models achieved accuracies ranging from 70.6% to 72.0% based on different subcompartment features.
  • Radiomics demonstrated significantly higher accuracy (76.5%) compared to RANO-BM criteria (39.2%) in distinguishing tumor recurrence (P < .001).
  • Consistent higher accuracies were observed for radiomics across all lesion subcompartments.

Conclusions:

  • Radiomics analysis is a highly effective tool for differentiating tumor recurrence from treatment effects in brain metastases.
  • Radiomics outperforms RANO-BM criteria and advanced imaging modalities, offering improved diagnostic accuracy.
  • The study highlights the potential of radiomics in neuro-oncology for precise treatment response assessment.