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

dc.contributor.author

Lafata, Kyle

dc.contributor.author

Cai, Jing

dc.contributor.author

Wang, Chunhao

dc.contributor.author

Hong, Julian

dc.contributor.author

Kelsey, Chris R

dc.contributor.author

Yin, Fang-Fang

dc.date.accessioned

2019-08-20T13:08:53Z

dc.date.available

2019-08-20T13:08:53Z

dc.date.issued

2018-11-08

dc.date.updated

2019-08-20T13:08:51Z

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.identifier.issn

0031-9155

dc.identifier.issn

1361-6560

dc.identifier.uri

https://hdl.handle.net/10161/19227

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

duke.contributor.orcid

Hong, Julian|0000-0001-5172-6889

duke.contributor.orcid

Yin, Fang-Fang|0000-0002-2025-4740|0000-0003-1064-2149

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

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Lafata_et_al-2018-Physics_Medicine_Biology.pdf
Size:
1.48 MB
Format:
Adobe Portable Document Format
Description:
Accepted version