Decoding Patient Heterogeneity Influencing Radiation-Induced Brain Necrosis.

Autor:	Chamseddine I; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Shah K; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Lee H; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Ehret F; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.; Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Berlin, Germany.; German Cancer Consortium (DKTK), partner site Berlin, a partnership between DKFZ and Charité-Universitätsmedizin Berlin, Berlin, Germany., Schuemann J; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Bertolet A; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Shih HA; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Paganetti H; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Jazyk:	angličtina
Zdroj:	Clinical cancer research : an official journal of the American Association for Cancer Research [Clin Cancer Res] 2024 Oct 01; Vol. 30 (19), pp. 4424-4433.
DOI:	10.1158/1078-0432.CCR-24-1215
Abstrakt:	Purpose: 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. Experimental Design: 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 three-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, integrated discrimination index, net reclassification index, and receiver operating characteristic (ROC). Results: The analysis highlighted tumor location and proximity to critical structures such as 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 (log-likelihood ratio = 12.17; P = 0.016; integrated discrimination index = 0.15; net reclassification index = 0.74). The ROC curve area was 0.66, emphasizing the discriminative value of nondosimetric variables. Conclusions: 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. (©2024 American Association for Cancer Research.)
Databáze:	MEDLINE
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