A clinical decision-support framework to differentiate radiation necrosis from tumor progression in brain metastases
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ID: 321523
2026
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Abstract
Abstract Background Differentiating radiation necrosis (RN) from tumor progression (TP) after stereotactic radiotherapy (SRT) in brain metastases (BMs) is a clinically consequential problem, as conventional MRI frequently fails to distinguish between them. Misclassification can lead to inappropriate treatment decisions or delayed therapy. The objective of this study was to develop a clinically interpretable, data-driven model that integrates lesion growth dynamics with routinely available clinical variables to improve discrimination between RN and TP. Methods We retrospectively analyzed 175 BMs from six institutions. Lesion volumes were extracted from three consecutive contrast-enhanced T1-weighted MRI, and growth dynamics were quantified by estimating the growth exponent β. Clinical and treatment-related variables were systematically evaluated, and a multivariable predictive model was trained on a development cohort (n = 131) and validated on an external cohort (n = 44). Results The final model combined β, primary tumor histology, and SRT modality. In the development cohort, the model demonstrated strong discriminative performance (AUC=0.887). External validation confirmed generalizability, achieving an overall accuracy of 0.75, with high specificity (0.85) and positive predictive value (0.92) for RN. Incorrect classifications were largely confined to an intermediate-probability zone, while predictions at low and high probability extremes were highly reliable. The model was translated into a freely accessible, web-based tool to facilitate clinical decision-making. Conclusions By integrating lesion growth dynamics with routine clinical variables, this probability-based framework supports clinically meaningful differentiation between RN and TP. Its ability to explicitly represent diagnostic uncertainty, together with external validation, highlight its potential utility as a decision-support tool in the management of BMs.
| Reference Key |
openalex_W7169573878
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| Authors | Beatriz Ocaña-Tienda, Zhao Hui Chen Zhou, Ana Ramos, Ana Ortiz de Mendivil, Fátima Nagib-Raya, Beatriz Asenjo, Alberto Bastero, Carlos Tramblin, Estanislao Arana, Víctor M. Pérez‐García |
| Journal | Neuro-Oncology Advances |
| Year | 2026 |
| DOI |
10.1093/noajnl/vdag185
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| URL | |
| Keywords | Keywords not found |
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