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INTERNATIONAL CONGRESS OF

CancerGenetics

• AI-Enhanced Liquid Biopsy: Revolutionizing Early Cancer Detection and Personalized Treatment

• Fatemeh Rezaei,^1,* Javad Akhtari,²
  1. Student Research Committee, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran.
  
  
  
• Introduction: Early detection and personalized treatment are critical for improving cancer survival rates, yet traditional diagnostic methods, such as tissue biopsies, are invasive and often fail to capture tumor heterogeneity or early-stage cancer mutations. Liquid biopsy, a minimally invasive method that analyzes circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and other biomarkers from body fluids, has emerged as a promising tool in oncology. However, the vast amount of genetic and molecular data generated from these biopsies requires advanced analytic tools for accurate interpretation and clinical application.
• Methods: Artificial intelligence (AI) offers a powerful solution to the limitations of traditional liquid biopsy analysis. By integrating machine learning and deep learning algorithms, AI enhances the detection and interpretation of subtle genetic variations and tumor-related biomarkers that might otherwise go unnoticed. AI-driven models can detect cancerous mutations at very low frequencies, enabling earlier diagnosis than standard techniques. These models are particularly effective in detecting ctDNA signals in patients with early-stage cancers or in those with minimal residual disease (MRD), allowing for earlier intervention and monitoring of recurrence. AI-enhanced liquid biopsy also plays a vital role in personalizing cancer treatment. Machine learning algorithms can process large datasets to identify specific genetic and molecular signatures associated with different tumor types, subtypes, and stages. This information can guide oncologists in selecting the most effective treatment regimens, reducing the risk of trial-and-error approaches and improving patient outcomes. For instance, by identifying actionable mutations in genes such as EGFR, KRAS, and BRAF, AI-enhanced liquid biopsies help tailor targeted therapies to individual patients, offering a precision medicine approach that optimizes therapeutic efficacy while minimizing side effects. Furthermore, AI improves the efficiency and accuracy of longitudinal monitoring. By continuously analyzing liquid biopsy samples over time, AI algorithms can track the evolution of the tumor, detect emerging resistance mutations, and assess the patient's response to treatment. This dynamic monitoring enables timely adjustments in treatment plans, potentially preventing relapse or progression. In this way, AI-enhanced liquid biopsy offers a real-time, non-invasive alternative to traditional biopsy methods, which are often unable to capture the full complexity of tumor evolution and resistance mechanisms.
• Results: One of the most exciting applications of AI-enhanced liquid biopsy lies in cancer screening programs. AI's ability to detect minute genetic alterations makes it particularly useful for identifying cancers that are typically challenging to diagnose early, such as pancreatic, ovarian, and colorectal cancers. By analyzing cfDNA and other biomarkers in asymptomatic individuals or those at high risk, AI-enhanced liquid biopsy offers the potential to detect cancers at an early, more treatable stage, significantly improving survival rates. This advancement could revolutionize cancer screening and prevention strategies, moving towards earlier and more accurate detection. However, despite these advantages, challenges remain. Integrating AI into clinical workflows requires overcoming technical, ethical, and regulatory hurdles. The development of robust, generalizable AI models depends on access to large, diverse datasets, raising concerns around data privacy and equity in healthcare. Additionally, clinical validation and regulatory approval are essential to ensure the reliability and safety of AI-enhanced liquid biopsy before it can be widely implemented.
• Conclusion: In conclusion, AI-enhanced liquid biopsy represents a transformative shift in the landscape of cancer detection and treatment. By applying AI to the vast and complex datasets generated by liquid biopsy, clinicians can achieve earlier detection, more personalized treatment strategies, and improved monitoring of cancer progression and treatment response. As ongoing research and clinical trials continue to refine AI algorithms and validate their clinical utility, AI-enhanced liquid biopsy is poised to become an integral tool in the future of precision oncology. With the potential to improve patient outcomes, reduce the burden of invasive procedures, and enable earlier intervention, this technology holds promise for revolutionizing cancer care.
• Keywords: Circulating tumor DNA, Artificial Intelligence, Early Cancer Detection, Personalized cancer treatmen