Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Area of Science:

• Oncology
• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Lung cancer has high mortality rates, often diagnosed at late stages with limited treatment options.
• Early detection through lung cancer screening significantly improves patient outcomes and treatment efficacy.
• Innovations in clinical care are crucial for better patient management and survival rates.

Purpose of the Study:

• To provide a comprehensive overview of artificial intelligence (AI) applications in lung cancer screening.
• To explore how AI can enhance various stages of the lung cancer screening workflow.
• To highlight AI's potential in improving the accuracy and efficiency of early lung cancer detection.

Main Methods:

• Review of current AI applications in lung cancer screening.
• Analysis of AI's role in image acquisition, dose reduction, and quality maintenance.
• Examination of AI for risk stratification, nodule detection (computer-aided detection - CAD), and characterization (virtual biopsy).

Main Results:

• AI can reduce radiation dose during low-dose computed tomography acquisition while maintaining image quality.
• AI facilitates personalized screening programs through risk stratification using extensive data analysis.
• AI-powered CAD systems improve nodule detection sensitivity and reduce interpretation time; AI aids in nodule characterization through segmentation and radiomic feature extraction.

Conclusions:

• Artificial intelligence offers significant potential to revolutionize lung cancer screening.
• AI integration across the screening workflow can lead to earlier and more accurate diagnoses.
• The adoption of AI in lung cancer screening is expected to improve patient outcomes and survival rates.