Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images.
发表日期： 2017.05.12 来源：Radiotherapy and Oncology. 2017 Jun;123(3):363-369.
van Timmeren JE1, Leijenaar RTH2, van Elmpt W2, Reymen B2, Oberije C2, Monshouwer R3, Bussink J3, Brink C4, Hansen O5, Lambin P2.
1. Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), The Netherlands.
2. Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), The Netherlands.
3. Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
4. Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Laboratory of Radiation Physics, Odense University Hospital, Denmark.
5. Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Laboratory of Radiation Physics, Odense University Hospital, Denmark; Department of Oncology, Odense University Hospital, Denmark.
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.
13.3% (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.
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.
Computed tomography; Cone-beam CT; Non-small cell lung cancer; Radiomics; Survival prediction