Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC
Oraianthi Fiste1, Ioannis Gkiozos1, Andriani Charpidou1
1Oncology Unit, Third Department of Internal Medicine and Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Abstract:
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.
Insights
Artificial intelligence (AI) offers new ways to manage non-small cell lung cancer (NSCLC) by analyzing complex data. AI, through radiomics and pathomics, shows promise for personalized NSCLC treatment despite implementation challenges.
Area of Science:
- Oncology
- Medical Informatics
- Radiology
Background:
- Non-small cell lung cancer (NSCLC) remains a leading cause of cancer mortality globally.
- Current NSCLC treatments, including targeted therapies and immunotherapy, have improved outcomes but require better predictive biomarkers.
- Artificial intelligence (AI) offers advanced computational tools for analyzing large datasets in complex medical problems.
Purpose of the Study:
- To review the current applications of AI in managing NSCLC.
- To focus on AI-driven radiomics and pathomics for NSCLC.
- To discuss the limitations and future directions of AI in NSCLC treatment.
Main Methods:
- Review of current literature on AI applications in NSCLC.
- Focus on radiomics and pathomics as key AI-driven modalities.
- Critical discussion of AI implementation challenges and future potential.
Main Results:
- AI demonstrates significant potential in improving NSCLC diagnosis, treatment guidance, and prognosis.
- Radiomics and pathomics are emerging AI applications with promise for NSCLC management.
- Despite challenges, AI is poised to transform personalized NSCLC treatment strategies.
Conclusions:
- AI represents a paradigm shift in oncology with vast potential for NSCLC.
- Further development and standardization of AI tools are needed for clinical integration.
- The transformative impact of AI on personalized NSCLC treatment is undeniable, warranting continued research and implementation efforts.
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