Deep Learning-Based Computer Vision Models for Early Cancer Detection Using Multimodal Medical Imaging and Radiogenomic Integration Frameworks

TL;DR

This paper reviews how deep learning models applied to multimodal medical imaging and radiogenomics are advancing early cancer detection, enabling more accurate, non-invasive diagnosis and personalized treatment strategies.

Contribution

It introduces a comprehensive framework combining deep learning, multimodal imaging, and radiogenomics for improved early cancer detection and characterization.

Findings

Deep learning models outperform traditional radiology in detecting subtle abnormalities.

Integration of multimodal imaging with radiogenomics enables non-invasive tumor profiling.

Framework supports personalized oncology through genotype and treatment response prediction.

Abstract

Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutional neural networks (CNNs), transformers, and hybrid attention architectures, can automatically extract complex spatial, morphological, and temporal patterns from multimodal imaging data including MRI, CT, PET, mammography, histopathology, and ultrasound. These models surpass traditional radiological assessment by identifying subtle tissue abnormalities and tumor microenvironment variations invisible to the human eye. At a broader scale, the integration of multimodal imaging with radiogenomics linking quantitative imaging features with genomics, transcriptomics, and epigenetic biomarkers has introduced a new paradigm for personalized oncology. This radiogenomic fusion allows the prediction of tumor genotype, immune response, molecular subtypes, and treatment resistance without invasive biopsies.