Skip to main content
Journal cover image

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

Publication ,  Journal Article
Wang, X; Pennello, G; deSouza, NM; Huang, EP; Buckler, AJ; Barnhart, HX; Delfino, JG; Raunig, DL; Wang, L; Guimaraes, AR; Hall, TJ; Obuchowski, NA
Published in: Acad Radiol
February 2023

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

February 2023

Volume

30

Issue

2

Start / End Page

215 / 229

Location

United States

Related Subject Headings

  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Multiparametric Magnetic Resonance Imaging
  • Lung Neoplasms
  • Lung
  • Humans
  • Diagnostic Imaging
  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, X., Pennello, G., deSouza, N. M., Huang, E. P., Buckler, A. J., Barnhart, H. X., … Obuchowski, N. A. (2023). Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics. Acad Radiol, 30(2), 215–229. https://doi.org/10.1016/j.acra.2022.10.001
Wang, Xiaofeng, Gene Pennello, Nandita M. deSouza, Erich P. Huang, Andrew J. Buckler, Huiman X. Barnhart, Jana G. Delfino, et al. “Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.Acad Radiol 30, no. 2 (February 2023): 215–29. https://doi.org/10.1016/j.acra.2022.10.001.
Wang X, Pennello G, deSouza NM, Huang EP, Buckler AJ, Barnhart HX, et al. Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics. Acad Radiol. 2023 Feb;30(2):215–29.
Wang, Xiaofeng, et al. “Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.Acad Radiol, vol. 30, no. 2, Feb. 2023, pp. 215–29. Pubmed, doi:10.1016/j.acra.2022.10.001.
Wang X, Pennello G, deSouza NM, Huang EP, Buckler AJ, Barnhart HX, Delfino JG, Raunig DL, Wang L, Guimaraes AR, Hall TJ, Obuchowski NA. Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics. Acad Radiol. 2023 Feb;30(2):215–229.
Journal cover image

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

February 2023

Volume

30

Issue

2

Start / End Page

215 / 229

Location

United States

Related Subject Headings

  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Multiparametric Magnetic Resonance Imaging
  • Lung Neoplasms
  • Lung
  • Humans
  • Diagnostic Imaging
  • 3202 Clinical sciences
  • 1103 Clinical Sciences