Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.
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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.
SubjectScience & Technology
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
stereotactic body radiation therapy
non-small cell lung cancer
Published Version (Please cite this version)10.1088/1361-6560/aaf5a5
Publication InfoLafata, Kyle J; Hong, Julian C; Geng, Ruiqi; Ackerson, Bradley G; Liu, Jian-Guo; Zhou, Zhennan; ... Yin, Fang-Fang (2019). Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Physics in medicine and biology, 64(2). pp. 025007. 10.1088/1361-6560/aaf5a5. Retrieved from https://hdl.handle.net/10161/19226.
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I am a current resident physician in radiation oncology and will be completing residency in June 2019. I will be starting as faculty in the Department of Radiation Oncology and in the Bakar Computational Sciences Health Institute at the University of California, San Francisco in September 2019.
Professor of Radiation Oncology
Clinical trials that are currently enrolling patients include a study investigating lower doses of radiation therapy for patients with diffuse large B-cell lymphoma, with the goal of maintaining excellent tumor control but decreasing the risk of long-term side effects of treatment. I also have an interest in genetic determinants of radiation sensitivity, predictors of local recurrence after surgery for lung cancer, radiation-induced lung injury, and the role of radiation therapy in
Assistant Professor of Radiation Oncology
My research focuses on novel mathematical methods and computational techniques that facilitate the discovery of biomarkers otherwise dormant in biomedical images. By incorporating multi-scale information from both radiological images (i.e., radiomics) and digital pathology images (i.e., pathomics), my work aims to characterize the appearance and behavior of disease across different spatial, temporal, and functional domains. Methodologically, I incorporate various computational and mathemati
Professor of Physics
Professor in Radiation Oncology
Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics
William W. Elliott Assistant Research Professor
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