Abstract
Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.
Highlights
Nonsmoker Ex-smoker Current smoker Eastern Cooperative Oncology group (ECOG) performance status 0 1 2 M descriptor M1a M1b Type of epidermal growth factor receptor (EGFR) mutation Exon 19 deletion L858R Line of EGFR tyrosine kinase inhibitor (TKI) First line Second line EGFR TKIs Gefitinib Elrotinib Overall survival Death Overall survival Follow-up period*
The result of our study showed that kurtosis of positive pixel values (p =
We found that the AUC2 of entropy at inner portion was successful for predicting overall survival time in advanced NSCLC
Summary
Nonsmoker Ex-smoker Current smoker ECOG performance status 0 1 2 M descriptor M1a M1b Type of EGFR mutation Exon 19 deletion L858R Line of EGFR TKIs First line Second line EGFR TKIs Gefitinib Elrotinib Overall survival Death Overall survival Follow-up period (months)*. Should not be used a surrogate of benefit in advanced epidermal growth factor receptor (EGFR)-mutant lung cancer[8]. Parameters such as continuous variables reflecting intratumoral heterogeneity and genomic alterations are needed to assess therapeutic response and clinical outcomes. Changes in quantitative features are considered a biomarker to accurately assess treatment response and clinical outcomes[11]. Delta-radiomics, a time dependent metric comprised of quantitative features extracted from medical images acquired during the course of treatment has been considered as an emerging biomarker for assessing therapeutic responses and clinical outcomes[12]. The purpose of this study was to develop biomarkers based on longitudinal changes in quantitative radiomic features in patients with advanced NSCLC, and assess the prognostic power of these biomarkers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
