Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer

N.W. Schurink, S.R. van Kranen, M. Berbee, W. van Elmpt, F.C.H. Bakers, S. Roberti, J.J.M. van Griethuysen, L.A. Min, M.J. Lahaye, M. Maas, G.L. Beets, R.G.H. Beets-Tan, D.M.J. Lambregts*

*Corresponding author for this work

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Abstract

Objective To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data. Methods Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2W(volume)/T2W(entropy)/ADC(mean)/SUVmean/TLG/CTmean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset. Results When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture). Conclusion In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research.
Original languageEnglish
Pages (from-to)7031-7038
Number of pages8
JournalEuropean Radiology
Volume31
Issue number9
Early online date10 Feb 2021
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Chemoradiotherapy
  • Logistic models
  • Magnetic resonance imaging
  • Positron-emission tomography computed tomography
  • Rectal neoplasms
  • chemoradiotherapy
  • logistic models
  • magnetic resonance imaging
  • positron-emission tomography computed tomography
  • rectal neoplasms
  • F-18-FDG PET/CT
  • PREOPERATIVE CHEMORADIOTHERAPY
  • RADIOMICS
  • PATHOLOGICAL COMPLETE RESPONSE
  • POSITRON-EMISSION-TOMOGRAPHY
  • THERAPY
  • CHEMORADIATION
  • RESISTANCE

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