Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.

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.

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Published Version (Please cite this version)

10.1088/1361-6560/aaf5a5

Publication Info

Lafata, Kyle J, Julian C Hong, Ruiqi Geng, Bradley G Ackerson, Jian-Guo Liu, Zhennan Zhou, Jordan Torok, Chris R Kelsey, et al. (2019). Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Physics in medicine and biology, 64(2). p. 025007. 10.1088/1361-6560/aaf5a5 Retrieved from https://hdl.handle.net/10161/19226.

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Scholars@Duke

Lafata

Kyle Jon Lafata

Thaddeus V. Samulski Associate Professor of Radiation Oncology

Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems). 

Prof. Lafata has worked in various areas of computational medicine and biology, resulting in 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.

Liu

Jian-Guo Liu

Professor of Physics
Kelsey

Christopher Ryan Kelsey

Professor of Radiation Oncology

I specialize in the treatment of hematologic and thoracic malignancies. I have a special research interest in optimizing radiation therapy in lymphomas and leukemias, particularly consolidation radiation therapy in diffuse large B-cell lymphoma and total body irradiation in the setting of allogeneic stem cell transplantation. Other academic interests include cardiac toxicity after radiation therapy for lung cancer and optimizing stereotactic body radiation therapy for stage I non-small cell lung cancer.


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