New Perspectives on Lung Cancer Screening and Artificial Intelligence

Area of Science:

• Oncology
• Medical Imaging
• Biotechnology

Background:

• Lung cancer is a leading cause of cancer death globally, with low survival rates often due to late diagnosis.
• Current screening methods face limitations in real-world application, necessitating innovative approaches for early detection.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) and biomarker-driven methods, specifically liquid biopsy, for enhancing early lung cancer detection.
• To review the efficacy of AI and liquid biopsy in improving diagnostic accuracy and patient outcomes in lung cancer screening.

Main Methods:

• Review of major randomized controlled trials, cohort studies, and research on AI algorithms.
• Analysis of AI utilizing multi-modal imaging (CT, PET) and liquid biopsy for identifying early molecular alterations.
• Evaluation of biomarker-driven methods for detecting molecular changes preceding visible cancer signs.

Main Results:

• AI algorithms improve diagnostic accuracy, reduce inter-reader variability, and offer faster processing times.
• Liquid biopsy identifies molecular alterations, enabling detection of early-stage cancers potentially missed by traditional methods like low-dose CT (LDCT).
• AI-driven screening demonstrates over 90% sensitivity and 85-90% specificity, significantly outperforming traditional methods.

Conclusions:

• Integrating AI and liquid biopsy holds significant promise for transforming lung cancer screening, enabling earlier and more accurate detection.
• These technologies have the potential to significantly improve survival outcomes for lung cancer patients.
• Standardization and clinical integration challenges remain, requiring ongoing research to realize full clinical benefits.