ArticleJCO clinical cancer informatics
Patient-Specific Computational Framework to Spatiotemporally Forecast Treatment Response and Identify Biologically Informed Radiotherapy Targets in High-Grade Glioma.
Article in JCO clinical cancer informatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
13 authors.
Funding
Abstract
purposeHigh-grade gliomas exhibit substantial spatial and temporal heterogeneity, posing a challenge for radiotherapy (RT) target definition. Adaptive RT, which prospectively modifies treatment plans, may benefit from early identification of intratumoral regions likely to exhibit differential treatment response. Apparent diffusion coefficient (
methodsUsing longitudinal multiparametric magnetic resonance imaging (MRI) from 21 patients, the mathematical model was calibrated to spatiotemporally forecast tumor cell density immediately after and 1-month post-RT. Predictions of tumor cell density were converted to
resultsThe framework achieved a median ROC AUC of 0.88 (0.80-0.93), indicating strong performance in distinguishing IDR from DMDR regions. No significant differences were observed between forecasting time points (
conclusionThis framework can delineate IDR and DMDR regions, offering a potential strategy for prospectively defining candidate biologically informed adaptive RT targets. Future studies are needed to evaluate their role in adaptive RT planning.
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