Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence and machine learning show promise in predicting immunotherapy response for non-small cell lung cancer (NSCLC). These advanced computational models can help personalize treatment and improve patient outcomes.
Area Of Science
- Oncology
- Computational Biology
- Medical Informatics
Background
- Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer mortality, with immunotherapy offering improved outcomes but variable response rates.
- Predictive biomarkers are essential for optimizing immune checkpoint inhibitor (ICI) therapy in NSCLC patients.
- Artificial intelligence (AI) and machine learning (ML) are emerging tools for analyzing complex biological and clinical data.
Purpose Of The Study
- To systematically review the application of AI and ML techniques in predicting immunotherapy response in NSCLC.
- To assess the performance and limitations of AI/ML models using diverse data types.
- To identify challenges and future directions for clinical implementation of AI/ML in NSCLC immunotherapy.
Main Methods
- A comprehensive literature search was conducted to identify relevant studies.
- Included studies utilized various AI/ML algorithms (e.g., deep learning, neural networks, SVMs).
- Data modalities analyzed included medical imaging, genomics, clinical data, and immunohistochemistry.
Main Results
- AI/ML models demonstrated significant accuracy in predicting ICI response, progression-free survival, and overall survival in NSCLC.
- Studies showcased the potential of AI/ML across different data types for predictive biomarker discovery.
- Identified challenges include data scarcity, quality issues, model interpretability, and clinical translation hurdles.
Conclusions
- AI and ML hold substantial potential for predicting immunotherapy response in NSCLC, enabling personalized treatment strategies.
- Further research is needed to enhance model transparency, address data limitations, and facilitate clinical integration.
- Successful implementation can lead to improved patient outcomes, reduced toxicity, and optimized healthcare resource utilization.
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