Aerts, HugoGrossmann, PatrickTan, YongqiangOxnard, Geoffrey G.Rizvi, NaiyerSchwartz, Lawrence H.Zhao, Binsheng2016-11-182016Aerts, Hugo J. W. L., Patrick Grossmann, Yongqiang Tan, Geoffrey G. Oxnard, Naiyer Rizvi, Lawrence H. Schwartz, and Binsheng Zhao. 2016. “Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC.” Scientific Reports 6 (1): 33860. doi:10.1038/srep33860. http://dx.doi.org/10.1038/srep33860.2045-2322http://nrs.harvard.edu/urn-3:HUL.InstRepos:29407617Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74–0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean ± std: ICC = 0.96 ± 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.en-USJournal Article2016-11-1810.1038/srep33860