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Morphological MRI features as prognostic indicators in brain metastases

  • 0Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain. Beatriz.Ocana@uclm.es.
Cancer Imaging : the Official Publication of the International Cancer Imaging Society +

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August 20, 2024

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August 20, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Predicting brain metastases recurrence after stereotactic radiotherapy is crucial. Morphological features on follow-up MRI, including volume changes and necrosis, can identify high-risk patients for personalized treatment.

Area Of Science

  • Radiology
  • Oncology
  • Medical Imaging

Background

  • Stereotactic radiotherapy is a primary treatment for limited brain metastases (BMs).
  • Recurrence of BMs after treatment remains a clinical challenge.
  • Identifying patients at high risk for recurrence is essential for treatment optimization.

Purpose Of The Study

  • To identify patients at higher risk of treatment failure after stereotactic radiotherapy for brain metastases.
  • To discover imaging biomarkers for predicting recurrence.
  • To aid in adjusting treatment strategies and preventing recurrence.

Main Methods

  • Retrospective multicenter study analyzing morphological features from contrast-enhanced T1-weighted MR images.
  • Utilized Kaplan-Meier analysis and multivariate Cox analysis.
  • Evaluated total/necrotic volumes, surface regularity, CE rim width, and non-morphological variables.

Main Results

  • No baseline variables at diagnosis showed prognostic value.
  • Morphological features on first follow-up MRI, including total/necrotic volumes and CE rim width, are predictive biomarkers for recurrence.
  • Combining changes in tumor volume with necrosis status significantly improved classification accuracy (p < 0.001).

Conclusions

  • Interpretable morphological features from routine post-irradiation MR images have prognostic significance for brain metastases.
  • These imaging biomarkers offer valuable insights for developing personalized treatment strategies.
  • The findings support proactive management for patients identified as high-risk for recurrence.

Keywords: