Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer

  • Joost J. M. van Griethuysen
  • Doenja M. J. LambregtsEmail author
  • Stefano Trebeschi
  • Max J. Lahaye
  • Frans C. H. Bakers
  • Roy F. A. Vliegen
  • Geerard L. Beets
  • Hugo J. W. L. Aerts
  • Regina G. H. Beets-Tan



To compare the performance of advanced radiomics analysis to morphological assessment by expert radiologists to predict a good or complete response to chemoradiotherapy in rectal cancer using baseline staging MRI.

Materials and methods

We retrospectively assessed the primary staging MRIs [prior to chemoradiotherapy (CRT)] of 133 rectal cancer patients from 2 centers. First, two expert radiologists subjectively estimated the likelihood of achieving a “complete response” (ypT0) and “good response” (TRG 1–2), using a 5-point score (based on TN-stage, MRF/EMVI-status, size/signal/shape). Next, tumor volumes were segmented on high b value DWI (semi-automated, corrected by 2 non-expert and 2-expert readers, resulting in 5 segmentations), copied to the remaining sequences after which a total of 2505 radiomic features were extracted from T2W, low and high b value DWI and ADC. Stability of features for noise due to inter-reader and inter-scanner and protocol variations was assessed using intraclass correlation (ICC) and the Kruskal–Wallis test. Using data from center 1 (n = 86; training set), top 9 features were selected using minimum Redundancy Maximum Relevance and combined in a logistic regression model. Finally, diagnostic performance of the fitted models was assessed on data from center 2 (n = 47; validation set) and compared to the performance of the radiologists.


The Radiomic models resulted in AUCs of 0.69–0.79 (with similar results for the segmentations performed by expert/non-expert readers) to predict response, results similar to the morphologic prediction by the expert radiologists (AUC 0.67–0.83). Radiomics using semi-automatically generated segmentations (without manual input) did not result in significant predictive performance.


Radiomics could predict response to therapy with comparable diagnostic performance as expert radiologists, regardless of whether image segmentation was performed by non-expert or expert readers, indicating that expert input is not required in order for the radiomics workflow to produce significant predictive performance.


Rectal cancer Magnetic resonance imaging Response prediction Radiomics Texture analysis 



This work was supported by the Dutch Cancer Society (KWF) (Grant No. 2016-2/10611).

Compliance with ethical standards

Conflict of interest

Dr. Aerts declares stock options in Sphera and Genospace, all other authors declare no conflict of interest.

Supplementary material

261_2019_2321_MOESM1_ESM.docx (29 kb)
Supplementary material 1 (DOCX 29 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of RadiologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
  2. 2.GROW School for Oncology and Developmental BiologyMaastricht UniversityMaastrichtThe Netherlands
  3. 3.Department of Radiology & Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
  4. 4.Department of RadiologyZuyderland Medical CenterHeerlenThe Netherlands
  5. 5.Department of SurgeryThe Netherlands Cancer InstituteAmsterdamThe Netherlands
  6. 6.Department of Radiation Oncology and Radiology, Computational Imaging and Bioinformatics LaboratoryDana-Farber Cancer Institute, Harvard Medical SchoolBostonUSA

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