# Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

## TL;DR

This paper reviews how artificial intelligence can improve non-small cell lung cancer treatment by analyzing big data for better diagnosis and personalized care.

## Contribution

The paper highlights AI's emerging role in NSCLC management through radiomics and pathomics, emphasizing its potential for precision oncology.

## Key findings

- AI technologies offer transformative potential in NSCLC diagnosis and treatment guidance.
- Current AI applications in NSCLC include radiomics and pathomics for personalized treatment approaches.
- Despite progress, limitations remain in implementing AI for robust clinical decision-making.

## Abstract

Lung cancer therapeutics have dramatically improved in recent years. Indeed, precision oncology could be exemplified by non-small cell lung cancer (NSCLC), with molecular profiling and programmed death ligand 1 (PD-L1) immunohistochemical expression representing an integral part of its tailored treatment. The present narrative review aims to highlight the promising role of artificial intelligence (AI) technologies in the optimal, patient-centered management of NSCLC, by distilling as well as interpreting big data.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.