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    • Assessment of Outcomes and Machine Learning-based Models to Predict Local Failure Risk Following Stereotactic Radiosurgery for Small Brain Metastases

    Sreenija Yarlagadda, Yanjia Zhang,Anshul Saxena,Tugce Kutuk,Ranjini Tolakanahalli, Haley Appel, Robert Herrera, Matthew D. Hall, Robert H. Press, D Jay J. Wieczorek,Yongsook C. Lee, Tatiana Bejarano,Michael W. McDermott,Alonso N. Gutierrez, Minesh P. Mehta,Rupesh Kotecha

    Journal of Neuro-Oncology（2025）

    Miami Cancer Institute

    Cited 0|Views0
    Abstract
    We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learning (ML) algorithms to predict the probability of local failure (LF). Consecutive patients with SBM treated with SRS between January 2017 and July 2022 were included. Propensity score matching (PSM) was performed with related factors to enhance balance for comparison. Variable selection and three time-varied generalized estimating equations (GEE) were used to create predictive models. 1503 SBMs in 235 patients treated over 358 SRS courses were analyzable. The actuarial 1-year cumulative rate of LF was lower in lesions treated with 24 Gy (5.9
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    Key words
    Stereotactic radiosurgery,Machine-Learning,Brain metastases,Local failure,GEE
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