Journal of Cancer Diagnosis
Open Access

Integrative Cancer Diagnosis Using Hybrid Diagnostic Algorithms: Combining Radiomics, Pathomics, and Multi-Omics Features

Dr. Aisha Rahman^*

Department of Biomedical Informatics, University of Medical Sciences Malaysia, Kuala Lumpur, Malaysia

Received Date: Mar 01, 2025 / Accepted Date: Mar 31, 2025 / Published Date: Mar 31, 2025

Citation: Aisha R (2025) Integrative Cancer Diagnosis Using Hybrid DiagnosticAlgorithms: Combining Radiomics, Pathomics, and Multi-Omics Features. JCancer Diagn 9: 290.

Copyright: © 2025 Aisha R. This is an open-access article distributed under theterms of the Creative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided the original author andsource are credited.

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The advent of high-throughput technologies has revolutionized cancer diagnostics, enabling clinicians and researchers to explore disease biology from multiple data modalities. Traditional diagnostic approaches relying solely on histopathology and radiology are increasingly being augmented by computational analyses of large-scale biomedical data. Radiomics and pathomics quantitative image features extracted from radiological and pathological images are being used alongside genomic, transcriptomic, proteomic, and metabolomic (“multi-omics”) data to enhance the sensitivity and specificity of cancer diagnostics. This article explores the implementation of hybrid diagnostic algorithms that integrate radiomics, pathomics, and multi-omics features. We highlight the technologies, workflows, challenges, and clinical potential of these integrative models in advancing precision oncology