4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. Issue 1 (October 2017)

• Record Type: Journal Article
• Authors: Larue, Ruben T.H.M.; Van De Voorde, Lien; van Timmeren, Janna E.; Leijenaar, Ralph T.H.; Berbée, Maaike; Sosef, Meindert N.; Schreurs, Wendy M.J.; van Elmpt, Wouter; Lambin, Philippe
• Is Part Of: Radiotherapy and oncology. Volume 125:Issue 1(2017:Oct.)
• Page Start: 147
• Page End: 153
• Publication Date: 2017-10
• Subjects: Radiomics -- Oesophageal cancer -- Lung cancer -- Test-retest -- 4D-CT -- Feature stability; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2017.07.023
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: 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.