Background and purpose: Quantitative tissue characteristics derived from medical images, also called radiomics, contain valuable prognostic information in several tumour-sites. The large number of features available increases the risk of overfitting. Typically test-retest CT-scans are used to reduce dimensionality and select robust features. However, these scans are not always available. We propose to use different phases of respiratory-correlated 4D CT-scans (4DCT) as alternative.
Materials and methods: In test-retest CT-scans of 26 non-small cell lung cancer (NSCLC) patients and 4DCT-scans (8 breathing phases) of 20 NSCLC and 20 oesophageal cancer patients, 1045 radiomics features of the primary tumours were calculated. A concordance correlation coefficient (CCC) >0.85 was used to identify robust features. Correlation with prognostic value was tested using univariate cox regression in 120 oesophageal cancer patients.
Results: Features based on unfiltered images demonstrated greater robustness than wavelet-filtered features. In total 63/74 (85%) unfiltered features and 268/299 (90%) wavelet features stable in the 4D-lung dataset were also stable in the test-retest dataset. In oesophageal cancer 397/1045 (38%) features were robust, of which 108 features were significantly associated with overall-survival.
Conclusion: 4DCT-scans can be used as alternative to eliminate unstable radiomics features as first step in a feature selection procedure. Feature robustness is tumour-site specific and independent of prognostic value. (C) 2017 The Authors. Published by Elsevier Ireland Ltd.
|Number of pages||7|
|Journal||Radiotherapy and Oncology|
|Publication status||Published - Oct 2017|
- Oesophageal cancer
- Lung cancer
- Feature stability
- PRIMARY ESOPHAGEAL CANCER
- LEARNING HEALTH-CARE
- CELL LUNG-CANCER
- TUMOR HETEROGENEITY
- JUNCTIONAL CANCER
- TEXTURAL FEATURES
- F-18-FDG PET