Jessi J. White

Author    Correspondence author
Cancer Genetics and Epigenetics, 2024, Vol. 12, No.   
Received: 01 Jan., 1970    Accepted: 01 Jan., 1970    Published: 11 Oct., 2024

Abstract The integration of multi-modal data using artificial intelligence (AI) has shown significant promise in the prediction and management of colon cancer. This study explores the current advancements in AI-driven multi-modal data fusion techniques, which combine histopathological images, genomic data, and clinical information to enhance the accuracy of colon cancer prediction. Recent studies have demonstrated that AI models, particularly those employing deep learning and machine learning algorithms, can outperform traditional diagnostic methods by leveraging the complementary strengths of different data modalities. For instance, AI-based systems have been successfully applied to colorectal cancer pathology image analysis, lymph node staging, and the detection of liver metastases, showcasing improved diagnostic accuracy and prognostic capabilities. Furthermore, innovative frameworks such as Pathomic Fusion and multi-task learning approaches have been developed to integrate histopathology and genomic features, leading to better survival outcome predictions and personalized treatment plans. Despite these advancements, challenges such as data heterogeneity, the need for large-scale datasets, and ethical considerations remain. This study underscores the potential of AI in revolutionizing colon cancer prediction and highlights the necessity for continued research and collaboration across disciplines to fully realize its benefits. Keywords Multi-modal data fusion; Artificial intelligence; Colon cancer prediction; Deep learning; Genomic data integration