Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images

Publication date: Available online 12 May 2017
Source:Radiotherapy and Oncology
Author(s): Janna E. van Timmeren, Ralph T.H. Leijenaar, Wouter van Elmpt, Bart Reymen, Cary Oberije, René Monshouwer, Johan Bussink, Carsten Brink, Olfred Hansen, Philippe Lambin
Background and purposeIn this study we investigated the interchangeability of planning CT and cone-beam CT (CBCT) extracted radiomic features. Furthermore, a previously described CT based prognostic radiomic signature for non-small cell lung cancer (NSCLC) patients using CBCT based features was validated.Material and methodsOne training dataset of 132 and two validation datasets of 62 and 94stage I–IV NSCLC patients were included. Interchangeability was assessed by performing a linear regression on CT and CBCT extracted features. A two-step correction was applied prior to model validation of a previously published radiomic signature.Results13.3% (149 out of 1119) of the radiomic features, including all features of the previously published radiomic signature, showed an R² above 0.85 between intermodal imaging techniques. For the radiomic signature, Kaplan–Meier curves were significantly different between groups with high and low prognostic value for both modalities. Harrell's concordance index was 0.69 for CT and 0.66 for CBCT models for dataset 1.ConclusionsThe results show that a subset of radiomic features extracted from CT and CBCT images are interchangeable using simple linear regression. Moreover, a previously developed radiomics signature has prognostic value for overall survival in three CBCT cohorts, showing the potential of CBCT radiomics to be used as prognostic imaging biomarker.



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