ArticleJCO precision oncology2022
Predictive Model to Guide Brain Magnetic Resonance Imaging Surveillance in Patients With Metastatic Lung Cancer: Impact on Real-World Outcomes.
Article in JCO precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.Journal of medical radiation sciences · 2024Pooled it
- Incidence and prognosis of brain metastases in esophageal carcinoma: a systematic review and meta-analysis.Journal of thoracic disease · 2025Article
- Article
- Integration of Surgical and Coagulation Risk Factors for Predicting Postoperative Pulmonary Embolism in Thoracic Surgery: A Multi-Center Retrospective Study.International journal of general medicine · 2025Article
- Effect of adding individualized health education for patients with brain metastasis of lung cancer undergoing radiotherapy, as measured by MRI and cognitive testing.American journal of translational research · 2024Article
- Brain metastasis screening in the molecular age.Neuro-oncology advancesReview
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Authors and funding
15 authors.
Funding
Abstract
purposeBrain metastasis is common in lung cancer, and treatment of brain metastasis can lead to significant morbidity. Although early detection of brain metastasis may improve outcomes, there are no prediction models to identify high-risk patients for brain magnetic resonance imaging (MRI) surveillance. Our goal is to develop a machine learning-based clinicogenomic prediction model to estimate patient-level brain metastasis risk.
methodsA penalized regression competing risk model was developed using 330 patients diagnosed with lung cancer between January 2014 and June 2019 and followed through June 2021 at Stanford HealthCare. The main outcome was time from the diagnosis of distant metastatic disease to the development of brain metastasis, death, or censoring.
resultsAmong the 330 patients, 84 (25%) developed brain metastasis over 627 person-years, with a 1-year cumulative brain metastasis incidence of 10.2% (95% CI, 6.8 to 13.6). Features selected for model inclusion were histology, cancer stage, age at diagnosis, primary site, and
conclusionThe proposed model can identify high-risk patients, who may benefit from more intensive brain MRI surveillance to reduce morbidity of subsequent treatment through early detection.
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