Oncotarget

Research Papers:

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients

Ilke Tunali, Olya Stringfield, Albert Guvenis, Hua Wang, Ying Liu, Yoganand Balagurunathan, Philippe Lambin, Robert J. Gillies and Matthew B. Schabath _

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Oncotarget. 2017; 8:96013-96026. https://doi.org/10.18632/oncotarget.21629

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Abstract

Ilke Tunali1,3,4, Olya Stringfield1, Albert Guvenis3, Hua Wang5, Ying Liu5, Yoganand Balagurunathan1, Philippe Lambin6, Robert J. Gillies1 and Matthew B. Schabath2

1Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

2Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

3Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey

4Faculty of Biomedical Engineering, Namik Kemal University, Tekirdag, Turkey

5Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, PR China

6Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht, The Netherlands

Correspondence to:

Matthew B. Schabath, email: Matthew.Schabath@Moffitt.org

Keywords: radiomics; radial gradient; radial deviation; lung adenocarcinoma; quantitative imaging

Received: March 12, 2017     Accepted: August 26, 2017     Published: October 06, 2017

ABSTRACT

The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.


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