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

Background and purpose: In 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 methods: One training dataset of 132 and two validation datasets of 62 and 94 stage 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.

Results: 13.3% (149 out of 1119) of the radiomic features, including all features of the previously published radiomic signature, showed an R-2 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.

Conclusions: The 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. (C) 2017 The Authors. Published by Elsevier Ireland Ltd.