Published online Dec 28, 2022. doi: 10.35711/aimi.v3.i4.87
Peer-review started: September 21, 2022
First decision: November 30, 2022
Revised: December 8, 2022
Accepted: December 21, 2022
Article in press: December 21, 2022
Published online: December 28, 2022
Processing time: 99 Days and 5.8 Hours
Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.
Core Tip: Radiomics is widespread applied in clinical researches through extracting high-dimensional quantitative imaging features as a relatively emerging and mature technique based on medical imaging. The basic principles and methodologies of radiomics were reviewed to make it easy to understand from the relatively fixed processes. The representative clinical utilizations were declared to show the benefits of radiomics in diagnosis, tumor biological features and prognosis. Radiomics has revealed potential of clinical applications, while there are still many limitations to resolve in the further researches.