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Updated: Jan 14, 2026
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Artificial intelligence-based pharmacological approach in non-small cell lung cancer in the precision medicine era
Stefano Fogli1, Alessandro Barberis2, Marzia Del Re3
1Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Abstract:
The increasing knowledge in the molecular pathophysiology of non-small-cell lung cancer (NSCLC) allowed early identification of druggable targets; however, the advanced disease remains incurable mainly due to drug resistance. Therefore, it is essential to explore new methodological approaches for pharmacological strategies based on longitudinal molecular and imaging monitoring of NSCLC evolution, which can support decision-making for personalized treatments in clinical practice and provide new insight for the design of innovative clinical trials. The advent of artificial intelligence (AI) presents an extraordinary opportunity to develop algorithms capable of decoding the complex, multifaceted patterns of NSCLC progression. AI needs input information from biomarker analyses on liquid biopsies, radiomic data, actionable targets involved in cancer drug resistance, and clinically relevant information for choosing personalized next-line therapies, including existing drugs that could target previously unconsidered resistance pathways (drug repurposing), and selecting sequential or combinatorial therapeutic approaches as a fundamental part of precision medicine. This narrative review explores the opportunity of integrating AI-based multiparametric models into reactive and proactive algorithms to offer patients new therapeutic options for long-term quality-adjusted survival.
Insights
Artificial intelligence (AI) can decode complex non-small-cell lung cancer (NSCLC) progression patterns. Integrating AI with molecular and imaging data offers new personalized treatment strategies for improved patient survival.
Area of Science:
- Oncology
- Medical Informatics
- Pharmacology
Background:
- Advanced non-small-cell lung cancer (NSCLC) remains largely incurable due to drug resistance.
- Understanding NSCLC molecular pathophysiology has identified targets, but effective treatments for advanced stages are limited.
- Longitudinal monitoring and novel therapeutic strategies are crucial for improving patient outcomes.
Purpose of the Study:
- To explore the integration of artificial intelligence (AI) into NSCLC treatment strategies.
- To investigate AI's potential in decoding complex NSCLC progression patterns.
- To identify new therapeutic options for long-term, quality-adjusted survival in NSCLC patients.
Main Methods:
- This narrative review explores the integration of AI-based multiparametric models.
- AI algorithms utilize biomarker data from liquid biopsies and radiomic data.
- The approach incorporates actionable targets, drug repurposing, and personalized therapy selection.
Main Results:
- AI offers a powerful tool for decoding multifaceted NSCLC progression patterns.
- AI can integrate diverse data sources for personalized treatment decision-making.
- AI facilitates the identification of novel therapeutic strategies, including drug repurposing.
Conclusions:
- Integrating AI into clinical practice can enhance personalized treatment selection for NSCLC.
- AI-driven models can support proactive and reactive therapeutic algorithms.
- AI holds significant promise for improving long-term quality-adjusted survival in NSCLC patients.
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