  Lung cancer is the primary cause of cancer-related mortality, claiming
approximately 1.79 million lives globally in 2020, with an estimated 2.21
million new cases diagnosed within the same period. Among these, Non-Small Cell
Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably
bleak prognosis and low overall survival rate of approximately 25% over five
years across all disease stages. However, survival outcomes vary considerably
based on the stage at diagnosis and the therapeutic interventions administered.
Recent advancements in artificial intelligence (AI) have revolutionized the
landscape of lung cancer prognosis. AI-driven methodologies, including machine
learning and deep learning algorithms, have shown promise in enhancing survival
prediction accuracy by efficiently analyzing complex multi-omics data and
integrating diverse clinical variables. By leveraging AI techniques, clinicians
can harness comprehensive prognostic insights to tailor personalized treatment
strategies, ultimately improving patient outcomes in NSCLC. Overviewing
AI-driven data processing can significantly help bolster the understanding and
provide better directions for using such systems.
