Deep Learning for Multi-Modal Cancer Imaging: Integrating Radiomics, Genomics, and Clinical Data for Comprehensive Diagnosis and Prognosis

Abstract

At both the cellular and molecular levels, cancer is a heterogeneous disease. While traditional diagnostic and prognostic methods are often deficient in the ability to capture this complexity, limiting the potential to inform personalized treatment strategies. Deep learning (DL) results on multi-modal data sources have the potential to improve cancer diagnosis and prognosis by combining several types of data sources in the past few years: radiomics, genomics and clinical data. The first, radiomics, extracts quantitative features from medical images to reveal tumor characteristics invisible to the human eye; the second, genomic data, provides information about what genetic mutations are driving cancer progression. These modalities are complemented by clinical data with patient demographics and treatment history contributing context specific information to the data. The application of deep learning techniques on multi-modal cancer imaging is reviewed as well and their ability to combine radiomics, genomics and clinical data to improve diagnostic and prognostic accuracy is emphasized. We discuss the challenges and future directions for clinical implementation of this approach and its potential to transform cancer care, improve patient outcomes and empower precision medicine.