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J Radiat Res. 2019 Jan 1;60(1):150-157. doi: 10.1093/jrr/rry077.

Radiomics with artificial intelligence for precision medicine in radiation therapy.

Author information

1
Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, Japan.
2
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, Japan.
3
Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22, Misaki-machi, Omuta, Fukuoka, Japan.

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.

PMID:
30247662
PMCID:
PMC6373667
DOI:
10.1093/jrr/rry077
[Indexed for MEDLINE]
Free PMC Article

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