Data Analysis Strategies in Medical Imaging

By Chintan Parmar, J. Barry, Ahmed Hosny, John Quackenbush, Hugo J. W. L. Aerts. 2018

140 citations recorded by the source corpus.

Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence has allowed for detailed quantification of radiographic characteristics of tissues using predefined engineered algorithms or deep learning methods. Precedents in radiology as well as a wealth of research studies hint at the clinical relevance of these characteristics. However, critical challenges are associated with the analysis of medical imaging data. Although some of these challenges are specific to the imaging field, many others like reproducibility and batch effects are generic and have already been addressed in other quantitative fields such as genomics. Here, we identify these pitfalls and provide recommendations for analysis strategies of medical imaging data, including data normalization, development of robust models, and rigorous statistical analyses. Adhering to these recommendations will not only improve analysis quality but also enhance precision medicine by allowing better integration of imaging data with other biomedical data sources. Clin Cancer Res; 24(15); 3492–9. ©2018 AACR.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Ahmed Hosny
  2. Hugo J. W. L. Aerts
  3. Chintan Parmar

In Health Informatics

  1. Ahmed Hosny
  2. Hugo J. W. L. Aerts
  3. Chintan Parmar

In Radiology, Nuclear Medicine and Imaging

  1. Hugo J. W. L. Aerts
  2. Ahmed Hosny
  3. Chintan Parmar
  4. Philippe Lambin

Citations in the corpus