Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

Elizabeth Huynh,Raymond H Mak,Idalid Franco,Hugo J W L Aerts,Thibaud P Coroller,Ying Hou,John Romano,Chintan Parmar,Vishesh Agrawal,Vivek Narayan

doi:10.1371/journal.pone.0169172

Abstract

Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638–0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643–0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601–0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.

Highlights

Advances in science and technology have led to the understanding that each tumor, even within the same cancer type, has a myriad of distinct genotypic and phenotypic characteristics
Radiomics may have a critical role in precision medicine as it quantitatively describes, with great detail, the tumor phenotype captured in medical images by applying advanced mathematical algorithms to generate a high-dimensional atlas of imaging features
The type of image used for radiomic feature extraction impacts the feature values [26], and it is important to evaluate the impact of these variations in the features on their association with and potential ability to predict clinical outcomes

Summary

Introduction

Advances in science and technology have led to the understanding that each tumor, even within the same cancer type, has a myriad of distinct genotypic and phenotypic characteristics. Radiomics is one method that aims to do this non-invasively by creating a quantitative portrayal of the tumor phenotype through the extraction of advanced imaging features from medical images [2,3,4] These radiomic features describe the tumor phenotype through quantifying properties related to its shape, texture and image intensity, and have been predictive of clinical outcomes [5,6,7,8,9,10,11,12,13,14,15] and tumor characteristics, such as genotype and protein expression [16,17,18]

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