The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Abstract

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis

“…Radiomics, an emerging approach for image interpretation, can non-invasively predict treatment response solely by radiographic examination (17). Radiomics can profile tumor heterogeneity of different responders by extracting extensively quantitative features that reflect underlying pathophysiology from medical images (18).…”

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

“…Radiomics is a research branch in the field of medical imaging (10). Based on the rapid development of machine learning and image processing techniques, radiomic analyses have been successfully applied in the field of oncology (11)(12)(13)(14)(15)(16), including glioma (17). Magnetic resonance imaging (MRI) is a routinely used diagnostic tool for glioma management.…”

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

“…Compared with previous techniques that process medical images as pictures for visual inspection, radiomics introduces a new way to mine the information contained in medical images, and thus, radiomics offers an unprecedented opportunity for clinically assisted diagnosis. 17 Radiomics analysis has been successfully applied to produce imaging biomarkers for neuropsychiatric diseases such as Alzheimer disease, 18 human immunodeficiency virus-related neurodegeneration, 19 and bipolar disorder. Therefore, such studies have demonstrated the potential of radiomics in predicting hippocampal sclerosis.…”