@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 {79, title = {Data from: Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries}, year = {2017}, publisher = {CancerData}, abstract = {

Purpose

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with (chemo)radiotherapy are of limited quality. In this work, we develop a predictive model of survival at two years based on a large volume of historical patient data, as a proof of concept, using a distributed learning approach.

Patients and methods

Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected and stored in 2 different cancer institutes (559 patients at Maastro clinic (Netherlands), 139 at University of Manchester (UK). The model was further validated on 196 patients originating from the University of Michigan (USA).

A Bayesian network model is adapted for distributed learning (watch the animation). Two-year post-treatment survival was chosen as endpoint.\ The Institute 1 cohort data is publicly available\ and the developed models can be found at\ PredictCancer.org)

Results

Variables included in the final model were T and N stage, age, performance status, and total tumor dose. The model has an AUC of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross-validation. A model based on T and N stage performed with an AUC of 0.47 on the validation set, significantly worse than our model (P\<0.001). A high- and low-risk chance of survival group can be identified using the model presented in this study, these groups have significantly different overall survival (P\<0.01).

Conclusion

Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data sharing barriers.\ We believe that Distributed learning is the future of sharing data in health care.

}, keywords = {Bayesian network, lung cancer, NSCLC, prediction model}, doi = {10.17195/candat.2017.02.2}, author = {Arthur Jochems and Timo M. Deist and Issam El Naqa and Marc Kessler and Chuck Mayo and Jackson Reeves and Shruti Jolly and Martha Matuszak and Randall Ten Haken and Johan van Soest and Cary Oberije and Corinne Faivre-Finn and Gareth Price and Dirk De Ruysscher and Philippe Lambin and Andr{\'e} Dekker} } Error | CancerData.org

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