Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review

R. W. Y. Granzier*, T. J. A. van Nijnatten, H. C. Woodruff, M. L. Smidt, M. B. Lobbes

*Corresponding author for this work

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

34 Citations (Web of Science)

Abstract

Purpose: MRI-based tumor response prediction to neoadjuvant systemic therapy (NST) in breast cancer patients is increasingly being studied using radiomics with outcomes that appear to be promising.

The aim of this study is to systematically review the current literature and reflect on its quality.

Methods: PubMed and EMBASE databases were systematically searched for studies investigating MRI-based radiomics for tumor response prediction. Abstracts were screened by two reviewers independently. The quality of the radiomics workflow of eligible studies was assessed using the Radiomics Quality Score (RQS). An overview of the methodologies used in steps of the radiomics workflow and current results are presented.

Results: Sixteen studies were included with cohort sizes ranging from 35 to 414 patients. The RQS scores varied from 0 % to 41.2 %. Methodologies in the radiomics workflow varied greatly, especially region of interest segmentation, features selection, and model development with heterogeneous outcomes as a result. Seven studies applied univariate analysis and nine studies applied multivariate analysis. Most studies performed their analysis on the pretreatment dynamic contrast-enhanced T1-weighted sequence. Entropy was the best performing individual feature with AUC values ranging from 0.83 to 0.85. The best performing multivariate prediction model, based on logistic regression analysis, scored a validation AUC of 0.94.

Conclusion: This systematic review revealed large methodological heterogeneity for each step of the MRI-based radiomics workflow, consequently, the (overall promising) results are difficult to compare. Consensus for standardization of MRI-based radiomics workflow for tumor response prediction to NST in breast cancer patients is needed to further improve research.

Original languageEnglish
Article number108736
Number of pages11
JournalEuropean Journal of Radiology
Volume121
DOIs
Publication statusPublished - Dec 2019

Keywords

  • Breast neoplasms
  • Tumor response prediction
  • Neoadjuvant therapy
  • Magnetic resonance imaging
  • Radiomics
  • PATHOLOGICAL COMPLETE RESPONSE
  • TEXTURE ANALYSIS
  • TUMOR HETEROGENEITY
  • DCE-MRI
  • CHEMOTHERAPY
  • FEATURES
  • CLASSIFICATION
  • IMAGES

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