Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.
dc.contributor.author | Lafata, Kyle J | |
dc.contributor.author | Hong, Julian C | |
dc.contributor.author | Geng, Ruiqi | |
dc.contributor.author | Ackerson, Bradley G | |
dc.contributor.author | Liu, Jian-Guo | |
dc.contributor.author | Zhou, Zhennan | |
dc.contributor.author | Torok, Jordan | |
dc.contributor.author | Kelsey, Chris R | |
dc.contributor.author | Yin, Fang-Fang | |
dc.date.accessioned | 2019-08-20T13:04:44Z | |
dc.date.available | 2019-08-20T13:04:44Z | |
dc.date.issued | 2019-01-08 | |
dc.date.updated | 2019-08-20T13:04:42Z | |
dc.description.abstract | The purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). Seventy patients who received SBRT for stage-1 NSCLC were retrospectively identified. The tumor was contoured on pre-treatment free-breathing CT images, from which 43 quantitative radiomic features were extracted to collectively capture tumor morphology, intensity, fine-texture, and coarse-texture. Treatment failure was defined based on cancer recurrence, local cancer recurrence, and non-local cancer recurrence following SBRT. The univariate association between each radiomic feature and each clinical endpoint was analyzed using Welch's t-test, and p-values were corrected for multiple hypothesis testing. Multivariate associations were based on regularized logistic regression with a singular value decomposition to reduce the dimensionality of the radiomics data. Two features demonstrated a statistically significant association with local failure: Homogeneity2 (p = 0.022) and Long-Run-High-Gray-Level-Emphasis (p = 0.048). These results indicate that relatively dense tumors with a homogenous coarse texture might be linked to higher rates of local recurrence. Multivariable logistic regression models produced maximum [Formula: see text] values of [Formula: see text], and [Formula: see text], for the recurrence, local recurrence, and non-local recurrence endpoints, respectively. The CT-based radiomic features used in this study may be more associated with local failure than non-local failure following SBRT for stage I NSCLC. This finding is supported by both univariate and multivariate analyses. | |
dc.identifier.issn | 0031-9155 | |
dc.identifier.issn | 1361-6560 | |
dc.identifier.uri | ||
dc.language | eng | |
dc.publisher | IOP Publishing | |
dc.relation.ispartof | Physics in medicine and biology | |
dc.relation.isversionof | 10.1088/1361-6560/aaf5a5 | |
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 | stereotactic body radiation therapy | |
dc.subject | non-small cell lung cancer | |
dc.subject | treatment outcomes | |
dc.subject | STAGE-I | |
dc.subject | SURVIVAL | |
dc.title | Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. | |
dc.type | Journal article | |
duke.contributor.orcid | Hong, Julian C|0000-0001-5172-6889 | |
duke.contributor.orcid | Liu, Jian-Guo|0000-0002-9911-4045 | |
duke.contributor.orcid | Yin, Fang-Fang|0000-0002-2025-4740|0000-0003-1064-2149 | |
pubs.begin-page | 025007 | |
pubs.issue | 2 | |
pubs.organisational-group | Staff | |
pubs.organisational-group | Duke | |
pubs.organisational-group | Radiation Oncology | |
pubs.organisational-group | Clinical Science Departments | |
pubs.organisational-group | School of Medicine | |
pubs.organisational-group | Trinity College of Arts & Sciences | |
pubs.organisational-group | Mathematics | |
pubs.organisational-group | Physics | |
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 | Faculty | |
pubs.publication-status | Published | |
pubs.volume | 64 |
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