Radiomics with artificial intelligence for precision medicine in radiation therapy

Abstract

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

“…Being relatively easy to input data, the most advanced areas with AI technology are diagnostic imaging, followed by genetics [10]. Due to the exponential growth in medical image analyses and pipelines that extract features to be used as valuable decision supporting data, a new practice termed radiomics has emerged [46]. Unfortunately, this trend has been focused on the fields of cancer or diseases related to the cardiovascular or nervous system [10].…”

<p>Application of Radiomics for Personalized Treatment of Cancer Patients</p>

“…Concerning medical imaging, it is theoretically possible to perform many tasks, related to images, through AI techniques, including lesion detection, disease classification, diagnosis and staging, quantification, treatment planning, and assessment of response to treatment and prognosis. 46,47 Recent studies found that radiomics combined with AI provide additional improvements, such as improved operational workflow, financial management, and quality. For example, AI (trained via machine learning) was applied to preoperative MRI studies to distinguish between low-level and high-level tumors using image texture features obtained using multi-mode MRI to achieve World Health Organization (WHO) tumor grading levels.…”

Artificial intelligence in radiation oncology treatment planning: a brief overview

“…There is currently no way to reliably compare between MRI radiomics studies, because variations exist among all of them in MRI scanner sequence, scanner vendor, and scan acquisition parameters (90). We refer the interested reader to comprehensive reviews on the use of radiomics in the field of radiation oncology (90)(91)(92)(93).…”