MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer

Kasia Owczarczyk,Davide Prezzi,Matthew Cascino,Robert Kozarski,Andrew Gaya,Muhammad Siddique,Gary J Cook,Rob Glynne-Jones,Vicky Goh

doi:10.1016/j.radonc.2019.01.022

Abstract

BackgroundThe aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome. The ability to predict disease recurrence following CRT has the potential to inform personalized radiotherapy approaches currently being explored in novel clinical trials. MethodsAn IRB waiver was obtained for retrospective analysis of standard care MRIs from ASCC patients presenting between 2010 and 2014. Whole tumor 3D volume-of-interest (VOI) was outlined on T2-weighted (T2w) and diffusion weighted imaging (DWI) of the pre- and post-treatment scans. Independent imaging features most predictive of disease recurrence were added to the baseline clinico-pathological model and the predictive value of respective extended models was calculated using net reclassification improvement (NRI) algorithm. Cross-validation analysis was carried out to determine percentage error reduction with inclusion of imaging features to the baseline model for both endpoints. ResultsForty patients who underwent 1.5 T pelvic MRI at baseline and following completion of CRT were included. A combination of two baseline MR heterogeneity features (baseline T2w energy and DWI coefficient of variation) was most predictive of disease recurrence resulting in significant NRI (p = 0 < 0.001). This was confirmed in cross-validation analysis with 34.8% percentage error reduction for the primary endpoint and 18.1% reduction for the secondary endpoint with addition of imaging variables to baseline model. ConclusionMRI heterogeneity analysis offers complementary information, in addition to clinical staging, in predicting outcome of CRT in anal SCC, warranting validation in larger datasets.

Highlights

The aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome
For the secondary endpoint the model extended with T2w Energy resulted in the greatest error reduction (30.3%) from baseline (CV error = 0.174) whereas the combined model (T2w energy and diffusion weighted imaging (DWI) coefficient of variation (CoV)) provided reduction by 18.1% (Fig. 3b, suppl Table 3). In this exploratory analysis of imaging heterogeneity features derived from standard care MRI acquired at baseline and following CRT in patients with anal cancer, we identified two imaging features, namely baseline T2w energy and DWI CoV, which appeared to be predictive of CRT outcome, independent of clinical characteristics alone
The addition of these two imaging features to multivariate logistic regression models based on clinical characteristics including age, gender, T and N stage yielded numeric increases in the predictive accuracy for both, disease recurrence as well as 2 year-disease free survival (DFS), when using both, conventional C-statistic as well as recently described net reclassification improvement (NRI) algorithm

Summary

Introduction

The aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome. A combination of two baseline MR heterogeneity features (baseline T2w energy and DWI coefficient of variation) was most predictive of disease recurrence resulting in significant NRI (p = 0 < 0.001). This was confirmed in cross-validation analysis with 34.8% percentage error reduction for the primary endpoint and 18.1% reduction for the secondary endpoint with addition of imaging variables to baseline model. Conclusion: MRI heterogeneity analysis offers complementary information, in addition to clinical staging, in predicting outcome of CRT in anal SCC, warranting validation in larger datasets.

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