Radiomics: a critical step towards integrated healthcare

Zuhir Bodalal, Stefano Trebeschi, Regina Beets-Tan*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Web of Science)

Abstract

Medical imaging is a vital part of the clinical decision-making process, especially in an oncological setting. Radiology has experienced a great wave of change, and the advent of quantitative imaging has provided a unique opportunity to analyse patient images objectively. Leveraging radiomics and deep learning, there is increased potential for synergy between physicians and computer networksvia computer-aided diagnosis (CAD), computer-aided prediction of response (CARP), and computer-aided biological profiling (CABP). The ongoing digitalization of other specialties further opens the door for even greater multidisciplinary integration. We envision the development of an integrated system composed of an aggregation of sub-systems interoperating with the aim of achieving an overarching functionality (in this case, better CAD, CARP, and CABP). This will require close multidisciplinary cooperation among the clinicians, biomedical scientists, and (bio)engineers as well as an administrative framework where the departments will operate not in isolation but in successful harmony.Key Points center dot The advent of quantitative imaging provides a unique opportunity to analyse patient images objectively.center dot Radiomics and deep learning allow for a more detailed overview of the tumour (i.e., CAD, CARP, and CABP) from many different perspectives.center dot As it currently stands, different medical disciplines have developed different stratification methods, primarily based on their own fieldoften to the exclusion of other departments.center dot The digitalization of other specialties further opens the door for multidisciplinary integration.center dot The long-term vision for precision medicine should focus on the development of integration strategies, wherein data derived from the patients themselves (via multiple disciplines) can be used to guide clinical decisions.

Original languageEnglish
Pages (from-to)911-914
Number of pages4
JournalInsights into Imaging
Volume9
Issue number6
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Quantitative imaging
  • Radiomics
  • Deep learning
  • Healthcare systems
  • Integrated systems

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