Abstract
We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients. Pairs of CT images (baseline, 3-week post therapy) of 46 NSCLC patients with known EGFR mutation status were collected and a FDA-customized anthropomorphic thoracic phantom was scanned on two vendors’ scanners at four different tube currents. Delta radiomics features were extracted from the NSCLC patient CTs and reproducible, non-redundant, and informative features were identified. The feature value differences between EGFR mutant and EGFR wildtype patients were quantitatively measured as the biological signal. Similarly, radiomics features were extracted from the phantom CTs. A pairwise comparison between settings resulted in a feature value difference that was quantitatively measured as the noise signal. Biological signals were compared to noise signals at each setting to determine if the distributions were significantly different by two-sample t-test, and thus robust. Four optimal features were selected to predict EGFR mutation status, Tumor-Mass, Sigmoid-Offset-Mean, Gabor-Energy and DWT-Energy, which quantified tumor mass, tumor-parenchyma density transition at boundary, line-like pattern inside tumor and intratumoral heterogeneity, respectively. The first three variables showed robustness across the majority of studied CT acquisition parameters. The textual feature DWT-Energy was less robust. The proposed framework was able to determine robustness of radiomics features at specific settings by comparing biological signal to noise signal. Identification of robust radiomics features may improve the generalizability of radiomics models in future studies.
Highlights
Since its conception, radiomics [1,2] has made an enormous impact on establishing medical images as quantitative data and led to the creation of algorithmic models capable of performing diagnostic [3,4], prognostic [5,6], histological [7,8,9], and even genomic classification [10,11]
In addition to reproducibility and repeatability, identifying robustness is crucial to finding features and building models that will withstand the test of external validation [14]
We investigated the usefulness of the proposed framework in a real medical application by evaluating reproducible radiomics features to non-invasively predict epidermal growth factor receptor (EGFR) mutant status in non-small cell lung cancer (NSCLC) patients
Summary
Radiomics [1,2] has made an enormous impact on establishing medical images as quantitative data and led to the creation of algorithmic models capable of performing diagnostic [3,4], prognostic [5,6], histological [7,8,9], and even genomic classification [10,11]. We have shown that in general, radiomics features can be more reproducible between images of different slice thicknesses than at different reconstruction kernels [12,15]. This changes depending on the type of radiomics feature and the specific scanning parameter called into question. In addition to slice thickness and reconstruction kernel, tube current, radiation dose, and many other parameters have been studied for their impact on radiomics feature reproducibility [16,17,18]
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
