Show simple item record

Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.

dc.contributor.author Cai, Jing
dc.contributor.author Hong, Julian
dc.contributor.author Kelsey, Christopher
dc.contributor.author Yin, Fang-Fang
dc.contributor.author Lafata, Kyle
dc.contributor.author Wang, Chunhao
dc.date.accessioned 2019-08-20T13:08:53Z
dc.date.available 2019-08-20T13:08:53Z
dc.date.issued 2018-11-08
dc.identifier.issn 0031-9155
dc.identifier.issn 1361-6560
dc.identifier.uri https://hdl.handle.net/10161/19227
dc.description.abstract The purpose of this research was to study the sensitivity of Computed Tomography (CT) radiomic features to motion blurring and signal-to-noise ratios (SNR), and investigate its downstream effect regarding the classification of non-small cell lung cancer (NSCLC) histology. Forty-three radiomic features were considered and classified into one of four categories: Morphological, Intensity, Fine Texture, and Coarse Texture. First, a series of simulations were used to study feature-sensitivity to changes in spatial-temporal resolution. A dynamic digital phantom was used to generate images with different breathing amplitudes and SNR, from which features were extracted and characterized relative to initial simulation conditions. Stage I NSCLC patients were then retrospectively identified, from which three different acquisition-specific feature-spaces were generated based on free-breathing (FB), average-intensity-projection (AIP), and end-of-exhalation (EOE) CT images. These feature-spaces were derived to cover a wide range of spatial-temporal tradeoff. Normalized percent differences and concordance correlation coefficients (CCC) were used to assess the variability between the 3D and 4D acquisition techniques. Subsequently, three corresponding acquisition-specific logistic regression models were developed to classify lung tumor histology. Classification performance was compared between the different data-dependent models. Simulation results demonstrated strong linear dependences (p  >  0.95) between respiratory motion and morphological features, as well as between SNR and texture features. The feature Short Run Emphasis was found to be particularly stable to both motion blurring and changes in SNR. When comparing FB-to-EOE, 37% of features demonstrated high CCC agreement (CCC  >  0.8), compared to only 30% for FB-to-AIP. In classifying tumor histology, EoE images achieved an average AUC, Accuracy, Sensitivity, and Specificity of [Formula: see text], respectively. FB images achieved respective values of [Formula: see text], and AIP images achieved respective values of [Formula: see text]. Several radiomic features have been identified as being relatively robust to spatial-temporal variations based on both simulation data and patient data. In general, features that were sensitive to motion blurring were not necessarily the same features that were sensitive to changes in SNR. Our modeling results suggest that the EoE phase of a 4DCT acquisition may provide useful radiomic information, particularly for features that are highly sensitive to respiratory motion.
dc.language eng
dc.publisher IOP Publishing
dc.relation.ispartof Physics in medicine and biology
dc.relation.isversionof 10.1088/1361-6560/aae56a
dc.subject Science & Technology
dc.subject Technology
dc.subject Life Sciences & Biomedicine
dc.subject Engineering, Biomedical
dc.subject Radiology, Nuclear Medicine & Medical Imaging
dc.subject Engineering
dc.subject radiomics
dc.subject quantitative imaging
dc.subject feature variability
dc.subject STABILITY
dc.subject INFORMATION
dc.subject IMAGES
dc.title Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.
dc.type Journal article
dc.date.updated 2019-08-20T13:08:51Z
pubs.begin-page 225003
pubs.issue 22
pubs.organisational-group School of Medicine
pubs.organisational-group Duke
pubs.organisational-group Radiation Oncology
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Staff
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Institutes and Centers
pubs.organisational-group Duke Kunshan University Faculty
pubs.organisational-group Duke Kunshan University
pubs.organisational-group Physics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 63
duke.contributor.orcid Hong, Julian|0000-0001-5172-6889
duke.contributor.orcid Yin, Fang-Fang|0000-0002-2025-4740


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record