A review in radiomics: Making personalized medicine a reality via routine imaging

J. Guiot, A. Vaidyanathan, L. Deprez, F. Zerka, D. Danthine, A.N. Frix, P. Lambin, F. Bottari, N. Tsoutzidis, B. Miraglio, S. Walsh, W. Vos, R. Hustinx, M. Ferreira, P. Lovinfosse, R.T.H. Leijenaar*

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

15 Citations (Web of Science)

Abstract

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
Original languageEnglish
Pages (from-to)426-440
Number of pages15
JournalMedicinal Research Reviews
Volume42
Issue number1
Early online date26 Jul 2021
DOIs
Publication statusPublished - Jan 2022

Keywords

  • CHEST CT
  • COMPUTED-TOMOGRAPHY
  • DECISION-SUPPORT-SYSTEMS
  • DIAGNOSIS
  • FEDERATED DATABASES
  • LEARNING HEALTH-CARE
  • LUNG-CANCER
  • PET RADIOMICS
  • RADIOTHERAPY RESEARCH
  • SURVIVAL PREDICTION
  • artificial intelligence
  • deep learning
  • machine learning
  • personalized medicine
  • radiomics
  • CANCER PATIENTS

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