Artificial intelligence-driven integration of multi-omics and radiomics: A new hope for precision cancer diagnosis and prognosis.

Journal: Biochimica et biophysica acta. Molecular basis of disease
Published Date:

Abstract

Despite advances in cancer diagnosis and treatment, the disease remains a major health challenge. Integrating multi-omics, radiomics, and artificial intelligence has improved detection, prognosis, and treatment monitoring. Molecular multi-omics provides insights into tumor biology, while radiomics extracts imaging features for outcome prediction. Liquid biopsy and circulating tumor DNA aid early detection and personalized therapy. Artificial intelligence-driven models integrate data to identify biomarkers and guide precision oncology. Despite challenges like cost and data integration, future advancements aim to enhance resolution, scalability, and non-invasive diagnostics. This mini-review explores these methodologies, their clinical impact, and their potential in personalized cancer treatment.

Authors

  • Jordi Camps
    Unitat de Recerca Biomèdica (URB-CRB), Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Spain.
  • Andrea Jiménez-Franco
    Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain.
  • Raquel García-Pablo
    Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain.
  • Jorge Joven
    Unitat de Recerca Biomèdica (URB-CRB), Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Spain.
  • Meritxell Arenas
    Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain.