Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Abstract

The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed. Contents 1. Introduction 2. Research methodology 3. Deep architectures used in current radiogenomics studies 4. Clinical applications of deep learning-based radiogenomics 5. Limitations of radiogenomic research 6. Discussion and future directions

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

“…Thus, clinicians should provide insight and participate in cooperation with the data science engineers regarding specific lesion attributes concerning the followed diagnosis protocols. Other types of clinical information, including laboratory exam results, anthropometricorphic (height and weight), demographic (age and sex) and supplementary imaging modalities can introduce diversity and complementarity toward achieving better problem formulation, improved predictive power, and a robust decision support process ( 178 ).…”

Precision Medicine, AI, and the Future of Personalized Health Care

“…Radiogenomics, as a novel precision medicine research field, focuses on establishing associations between cancer imaging features and gene expression to predict a patient's risk of developing toxicity following radiotherapy. [51][52][53] For example, Chang et al proposed a framework of multiple residual convolutional neural networks to noninvasively predict isocitrate dehydrogenase genotype in grades II-IV glioma using multi-institutional magnetic resonance imaging datasets. Besides, AI has been used in discovering radiogenomic associations in breast cancer, 52 liver cancer, 54 and colorectal cancer.…”

“…53 Currently, limited data availability remains the most formidable challenge for AI radiogenomics. 51 Knowing the response to therapy can help clinicians choose the right treatment plan. AI demonstrates potential applications in this area.…”

“…The initial successful paradigm of AI in imaging recognition has also given rise to radiogenomics. Radiogenomics, as a novel precision medicine research field, focuses on establishing associations between cancer imaging features and gene expression to predict a patient’s risk of developing toxicity following radiotherapy 51–53 . For example, Chang et al .…”

“…Besides, AI has been used in discovering radiogenomic associations in breast cancer, 52 liver cancer, 54 and colorectal cancer 53 . Currently, limited data availability remains the most formidable challenge for AI radiogenomics 51 …”