About this Research Topic

Background

In the landscape of non-small cell lung cancer (NSCLC) management, the integration of biomarkers for precision medicine has been transformational, particularly with the advent of molecular profiling, immune-checkpoint therapies (IO), and targeted treatments. These advances have tailored therapy to individual patient profiles, improving outcomes in both limited and metastatic stages. However, challenges remain in areas such as predicting chemotherapy benefits across different stages, and the potential for reliable biomarkers in these scenarios remains underexploited. Additionally, the recent introduction of Artificial Intelligence (AI) in this domain promises further enhancements in treatment precision, opening new avenues for both research and clinical application.

This research topic aims to broaden the application and understanding of both existing and emerging biomarkers in NSCLC, exploring their role in treatment customization for both limited and extensive stage diseases. The collection seeks to uncover the synergistic potential of combining traditional biomarkers and novel AI tools to enhance prognosis and treatment prediction accuracy. Emphasis will be placed on dissecting the multifaceted roles of AI in aiding clinician decisions and prognostic assessments, aiming to establish a more refined framework for applying biomarker-driven strategies in NSCLC care.

This collection invites researchers to explore a wide range of themes related to biomarker application in NSCLC:
- Biomarker Discovery: Investigation into novel biomarkers for predicting treatment response and disease prognosis in various stages of NSCLC.
- AI in Predictive Models: Analysis of AI capability to enhance biomarker utility in NSCLC, focusing on practical implications and accuracy in clinical settings.
- Combinatorial Biomarker Strategies: Assessment of the effectiveness of existing and novel biomarker combinations in influencing therapeutic decisions and outcomes.

Themes include, but are not limited to:
- Novel Biomarkers: Research on emerging biomarkers for predicting NSCLC outcomes across different stages of the disease.
- AI Tools and Models: Studies examining the integration of AI with clinical data to improve treatment planning and prognosis.
- Established Biomarker Utilization: Innovative approaches to using recognized biomarkers such as TMB, PDL1, EGFR, and others, alone or in combination, to refine therapeutic strategies across all treatment settings.
- Predictive Tools for Chemotherapy and IO: Development and validation of models or tools to forecast the efficacy of chemotherapy and immune-checkpoint inhibitors.
- Hypothesis-Generating Tools: Exploration of new predictive tools with potential for clinical application in NSCLC.
This research collection aims to foster a deeper understanding of how biomarkers and AI can be synergistically utilized to refine and enhance treatment protocols in NSCLC, ultimately leading to improved patient outcomes and more efficient therapeutic strategies.

Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Non-small cell lung cancer, Predictive biomarkers, Targeted therapy, Artificial Intelligence in lung cancer, Immune-checkpoint therapy, chemotherapy.

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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