Precision diagnostics based on machine learning-derived imaging signatures

Christos Davatzikos, Aristeidis Sotiras, Yong Fan, Mohamad Habes, Guray Erus, Saima Rathore, Spyridon Bakas, Rhea Chitalia, Aimilia Gastounioti, Despina Kontos

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.

Original languageEnglish (US)
Pages (from-to)49-61
Number of pages13
JournalMagnetic Resonance Imaging
Volume64
DOIs
StatePublished - Dec 2019
Externally publishedYes

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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