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239P Dual AI models for perioperative decision support in resectable NSCLC: A real-world cohort analysis
0
Zitationen
14
Autoren
2025
Jahr
Abstract
Accurate perioperative assessment is crucial in early-stage NSCLC, yet current staging and prognostic tools remain limited. We developed and internally validated two machine learning models using the IBM XGBoost algorithm: (1) to predict the likelihood of pathological upstaging (≥ stage II) at surgery based on perioperative data; (2) to estimate the probability of achieving a favorable postoperative outcome, defined as no evidence of disease (NED) at follow-up, versus non-NED. These AI-driven tools aim to enhance decision-making and personalize treatment strategies.
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