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◆ British journal of cancer2026-08-26

Risk prediction model for brain metastasis risk among patients diagnosed with primary gastrointestinal cancers.

Marc Vincent N Barcelona, Kaushik Jagannath Kataki, Fadwa Abdel Rahman, Fabio Y Moraes, Barbara Ann Millar, Tatiana Conrad, Dana Keilty, Andrew Hope, Alexander Sun, Paul Kongkham, Elena Elimova, Yangqing Deng, David Shultz, Michael Yan

一句话结论

We developed an internally validated prediction model for BrM in GI cancers. External validation is required.

原始摘要(原文)
BACKGROUND: The risk of radiotherapy-treated brain metastasis (BrM) in gastrointestinal (GI) cancers is poorly defined. We aimed to determine its incidence and develop a clinically interpretable prediction model. METHODS: Patients with GI cancers diagnosed between 2005-2020 were identified from our registry. Receipt of brain radiotherapy was used to define radiotherapy-treated BrM. Logistic regression was performed on a training cohort to identify BrM predictors, which were then used to construct a risk score, and validate performance in a held-out cohort. Model performance was assessed with receiver operating characteristic (ROC) analysis, calibration, Brier score, and Multiple imputation by chained equations (MICE) sensitivity analysis. RESULTS: Among 14,863 eligible patients, the 15-year cumulative incidence of clinically treated BrM was 1.89%. Median OS following BrM was 3.7 months. Logistic regression was performed in 11,022 patients with complete data. In the complete-case training cohort (n = 7348), significant predictors included primary site, clinical stage, chemotherapy, and recurrence at presentation. The AUC was 0.80, with good calibration and Brier score 0.012. Medium- and high-risk groups had higher BrM risk in validation (HR 4.57 and 11.35, respectively). CONCLUSIONS: We developed an internally validated prediction model for BrM in GI cancers. External validation is required.
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Risk prediction model for brain metastasis risk among patients diagnosed with primary gastrointestinal cancers. — 科研速览 Science Skim