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Decoding Patient Heterogeneity Influencing Radiation-Induced Brain Necrosis.

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Area of Science:

  • Radiation Oncology
  • Neuro-oncology
  • Medical Physics

Background:

  • Patient heterogeneity in radiotherapy (RT) for brain tumors obscures treatment effects and complicates the prediction and mitigation of radiation-induced brain necrosis.
  • Understanding patient-specific factors is crucial for improving outcome assessments and reducing treatment toxicity in brain tumor patients undergoing RT.

Purpose of the Study:

  • To develop and validate a clinically practical pipeline for identifying key variables that clarify the relationship between dosimetric features and outcomes in patients treated with proton therapy.
  • To assess the impact of non-dosimetric variables on radiation-induced brain necrosis risk and improve patient stratification for personalized RT.

Main Methods:

  • A cohort of 130 patients treated with proton therapy for brain and head and neck tumors was analyzed.
  • An expert-augmented Bayesian network was utilized to understand variable interdependencies and assess structural dependencies, with critical evaluation using a three-level grading system.
  • Markov blanket analysis, log-likelihood ratio, integrated discrimination index, net reclassification index, and receiver operating characteristic (ROC) curves were employed for statistical assessment.

Main Results:

  • Tumor location and proximity to critical structures (white matter, ventricles) were identified as major determinants of necrosis risk.
  • Quantitative measures confirmed the clinical significance of these non-dosimetric variables in patient stratification (log-likelihood ratio = 12.17; P = 0.016; integrated discrimination index = 0.15; net reclassification index = 0.74).
  • The ROC curve area of 0.66 highlighted the discriminative value of non-dosimetric variables in predicting brain necrosis.

Conclusions:

  • Key patient variables, particularly non-dosimetric factors, critical to understanding brain necrosis post-RT were identified, serving as confounders and moderators of dosimetric impacts.
  • The developed pipeline enhances outcome assessments by identifying at-risk patients, offering a versatile tool for broader applications in RT.
  • This approach aims to improve treatment personalization for various disease sites by revealing critical patient variables influencing RT outcomes.