Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Isabella Fornacon-Wood
  • Christoph J. Ackermann
  • Andrew Mcpartlin
  • Gareth Price


ObjectiveTo investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation of calculation settings and platform version on the statistical reliability of radiomic features and their corresponding ability to predict clinical outcome.MethodsThe statistical reliability of radiomic features was assessed retrospectively in three clinical datasets(patient numbers: 108 head and neck cancer, 37 small cell lung cancer, 47 non-small cell lung cancer).Features were calculated using four platforms (PyRadiomics, LIFEx, CERR and IBEX).PyRadiomics, LIFEx and CERR are IBSI-compliant, whereas IBEX is not. The effect of IBSI compliance, user defined calculation settings and platform version were assessed by calculating intraclass correlation coefficients and confidence intervals. The influence of platform choice on the relationship between radiomic biomarkers and survival was evaluated using univariable coxregression in the largest dataset.ResultsThe reliability of radiomic features calculated by the different software platforms was only excellent(ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved toICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonise calculation settings resulted in poor reliability, even across the IBSI compliant platforms. Software platform version also had a marked effect on feature reliability inCERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios.Conclusion IBSI compliance, user defined calculation settings and choice of platform version all influence the statistical reliability and corresponding performance of prognostic models in radiomics.

Bibliographical metadata

Original languageEnglish
JournalEuropean Radiology
Publication statusAccepted/In press - 17 Apr 2020