Decoding Patient Heterogeneity Influencing Radiation-Induced Brain Necrosis.

in Clinical cancer research : an official journal of the American Association for Cancer Research by Ibrahim Chamseddine, Keyur Shah, Hoyeon Lee, Felix Ehret, Jan Schuemann, Alejandro Bertolet, Helen A Shih, Harald Paganetti

TLDR

  • The study aimed to understand how different factors affect brain damage from radiation therapy. They used a special tool to look at how these factors are related to each other and how they affect the risk of brain damage. The study found that the location of the tumor and how close it is to certain parts of the brain are important factors in determining the risk of brain damage. The study also found that the tool they used can help doctors identify patients who are at higher risk of brain damage from radiation therapy. This information can help doctors make better decisions about treatment and reduce the risk of brain damage for patients.

Abstract

In radiotherapy (RT) for brain tumors, patient heterogeneity masks treatment effects, complicating the prediction and mitigation of radiation-induced brain necrosis. Therefore, understanding this heterogeneity is essential for improving outcome assessments and reducing toxicity. We developed a clinically practical pipeline to clarify the relationship between dosimetric features and outcomes by identifying key variables. We processed data from a cohort of 130 patients treated with proton therapy for brain and head and neck tumors, utilizing an expert-augmented Bayesian network to understand variable interdependencies and assess structural dependencies. Critical evaluation involved a 3-level grading system for each network connection and a Markov blanket analysis to identify variables directly impacting necrosis risk. Statistical assessments included log-likelihood ratio (LLR), integrated discrimination index (IDI), net reclassification index (NRI), and receiver operating characteristic (ROC). The analysis highlighted tumor location and proximity to critical structures like white matter and ventricles as major determinants of necrosis risk. The majority of network connections were clinically supported, with quantitative measures confirming the significance of these variables in patient stratification (LLR=12.17, p=0.016; IDI=0.15; NRI=0.74). The ROC curve area was 0.66, emphasizing the discriminative value of non-dosimetric variables. Key patient variables critical to understanding brain necrosis post-RT were identified, aiding the study of dosimetric impacts and providing treatment confounders and moderators. This pipeline aims to enhance outcome assessments by revealing at-risk patients, offering a versatile tool for broader applications in RT to improve treatment personalization in different disease sites.

Overview

  • The study aims to clarify the relationship between dosimetric features and outcomes in radiotherapy (RT) for brain tumors. The authors developed a clinically practical pipeline to identify key variables that contribute to radiation-induced brain necrosis. They processed data from a cohort of 130 patients treated with proton therapy for brain and head and neck tumors, utilizing an expert-augmented Bayesian network to understand variable interdependencies and assess structural dependencies. The study's primary objective is to provide a versatile tool for broader applications in RT to improve treatment personalization in different disease sites.

Comparative Analysis & Findings

  • The analysis revealed that tumor location and proximity to critical structures like white matter and ventricles are major determinants of necrosis risk. The majority of network connections were clinically supported, with quantitative measures confirming the significance of these variables in patient stratification. The ROC curve area was 0.66, emphasizing the discriminative value of non-dosimetric variables.

Implications and Future Directions

  • The study's findings provide a valuable tool for identifying at-risk patients and aiding the study of dosimetric impacts. The results can be used to improve outcome assessments and reduce toxicity in RT for brain tumors. Future research should focus on validating the pipeline's performance in other disease sites and incorporating additional dosimetric features to enhance its predictive power.
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