Eur J Nucl Med Mol Imaging
January 2025
Background: Although higher-generation TKIs are associated with improved progression-free survival in advanced NSCLC patients with EGFR mutations, the optimal selection of TKI treatment remains uncertain. To address this gap, we developed a web application powered by a reinforcement learning (RL) algorithm to assist in guiding initial TKI treatment decisions.
Methods: Clinical and mutational data from advanced NSCLC patients were retrospectively collected from 14 medical centers.
BACKGROUND This retrospective study from a single center included 289 patients diagnosed with advanced non-small cell lung cancer (NSCLC) between 2010 to 2017 and aimed to evaluate the effects of body mass index (BMI) on overall survival. MATERIAL AND METHODS This retrospective study involved 289 patients diagnosed with metastatic-stage NSCLC at a single institution between January 2010 and December 2017. Patients were categorized into 2 groups based on their BMI at diagnosis: those with a BMI <25 kg/m² and those with a BMI ≥25 kg/m².
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