Radiomics with artificial intelligence for precision medicine in radiation therapy

Hidetaka Arimura, Mazen Soufi, Hidemi Kamezawa, Kenta Ninomiya, Masahiro Yamada

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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 noninvasive, 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.

Original languageEnglish
Pages (from-to)150-157
Number of pages8
JournalJournal of radiation research
Volume60
Issue number1
DOIs
Publication statusPublished - Jan 1 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Radiation
  • Radiology Nuclear Medicine and imaging
  • Health, Toxicology and Mutagenesis

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