AI-Enabled Lung Cancer Prognosis

TL;DR

This paper discusses how AI, including machine learning and deep learning, improves lung cancer prognosis by analyzing complex data to enable personalized treatment and better survival predictions for NSCLC patients.

Contribution

It introduces AI-driven methods for lung cancer prognosis, highlighting their potential to enhance prediction accuracy and support personalized treatment strategies.

Findings

AI techniques improve survival prediction accuracy.

AI enables personalized treatment planning.

AI analysis of multi-omics data enhances understanding.

Abstract

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.