@article {80, title = {Data from: 4DCT imaging to assess radiomics feature stability: an investigation for thoracic cancers}, year = {2017}, publisher = {CancerData}, 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.


Venn chart visualizing the overlap of stable features with CCC\>0.85 in the test-retest lung (TR-lung), 4D-lung and 4D-oesophagus (4D-OES) dataset respectively.

}, keywords = {4D-CT, feature stability, lung cancer, oesophageal cancer, Radiomics, test-retest}, doi = {10.17195/candat.2017.05.1}, author = {Ruben T.H.M. Larue and Lien Van De Voorde and Janna E. van Timmeren and Ralph T.H. Leijenaar and Maaike Berbee and Meindert N. Sosef and Wendy M.J. Schreurs and Wouter van Elmpt and Philippe Lambin} } @article {77, title = {Data from: Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images}, year = {2017}, abstract = {

Background and Purpose: In this study we investigated the interchangeability of planning CT and cone-beam CT (CBCT) extracted radiomic features. Furthermore, this study validates a previously\ described CT based prognostic radiomic signature for non-small cell lung cancer (NSCLC) patients using CBCT based features.

Material and Methods: One internal dataset of 132 and two external datasets of 62 and 94 stage I-IV NSCLC patients were included in this study. 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 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{\textquoteright}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.

Kaplan-Meier curves for pCT and CBCT. Kaplan-Meier curves are based on model predictions of the radiomic signature.

}, keywords = {computed tomography, cone-beam CT, non-small cell lung cancer, Radiomics, survival prediction}, doi = {10.17195/candat.2017.02.1}, author = {Janita van Timmeren and Ralph T.H. Leijenaar and Wouter van Elmpt and Bart Reymen and Cary Oberije and Ren{\'e} Monshouwer and Johan Bussink and Carsten Brink and Olfred Hansen and Philippe Lambin} } Error | CancerData.org

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