Learning from scanners: Bias reduction and feature correction in radiomics

Ivan Zhovannik*, Johan Bussink, Alberto Traverso, Zhenwei Shi, Petros Kalendralis, Leonard Wee, Andre Dekker, Rianne Fijten, Rene Monshouwer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Purpose: Radiomics are quantitative features extracted from medical images. Many radiomic features depend not only on tumor properties, but also on non-tumor related factors such as scanner signal-to-noise ratio (SNR), reconstruction kernel and other image acquisition settings. This causes undesirable value variations in the features and reduces the performance of prediction models. In this paper, we investigate whether we can use phantom measurements to characterize and correct for the scanner SNR dependence.

Methods: We used a phantom with 17 regions of interest (ROI) to investigate the influence of different SNR values. CT scans were acquired with 9 different exposure settings. We developed an additive correction model to reduce scanner SNR influence.

Results: Sixty-two of 92 radiomic features showed high variance due to the scanner SNR. Of these 62 features, 47 showed at least a factor 2 significant standard deviation reduction by using the additive correction model. We assessed the clinical relevance of radiomics instability by using a 221 NSCLC patient cohort measured with the same scanner.

Conclusions: Phantom measurements show that roughly two third of the radiomic features depend on the exposure setting of the scanner. The dependence can be modeled and corrected significantly reducing the variation in feature values with at least a factor of 2. More complex models will likely increase the correctability. Scanner SNR correction will result in more reliable radiomics predictions in NSCLC. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.

Original languageEnglish
Pages (from-to)33-38
Number of pages6
JournalClinical and Translational Radiation Oncology
Publication statusPublished - Nov 2019


  • Radiomics
  • Reproducibility
  • Predictive modeling
  • Phantoms

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