Literature DB >> 30524018

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

Kyle J Lafata1, Julian C Hong, Ruiqi Geng, Bradley G Ackerson, Jian-Guo Liu, Zhennan Zhou, Jordan Torok, Chris R Kelsey, Fang-Fang Yin.   

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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Year:  2019        PMID: 30524018     DOI: 10.1088/1361-6560/aaf5a5

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  12 in total

1.  Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA.

Authors:  Kyle J Lafata; Michael N Corradetti; Junheng Gao; Corbin D Jacobs; Jingxi Weng; Yushi Chang; Chunhao Wang; Ace Hatch; Eric Xanthopoulos; Greg Jones; Chris R Kelsey; Fang-Fang Yin
Journal:  Radiol Imaging Cancer       Date:  2021-04

2.  Multiblock Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy.

Authors:  Sang Ho Lee; Gary D Kao; Steven J Feigenberg; Jay F Dorsey; Melissa A Frick; Samuel Jean-Baptiste; Chibueze Z Uche; Keith A Cengel; William P Levin; Abigail T Berman; Charu Aggarwal; Yong Fan; Ying Xiao
Journal:  Int J Radiat Oncol Biol Phys       Date:  2021-03-01       Impact factor: 8.013

Review 3.  Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.

Authors:  Isabella Fornacon-Wood; Corinne Faivre-Finn; James P B O'Connor; Gareth J Price
Journal:  Lung Cancer       Date:  2020-06-02       Impact factor: 5.705

4.  Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions.

Authors:  Khaled Bousabarah; Oliver Blanck; Susanne Temming; Maria-Lisa Wilhelm; Mauritius Hoevels; Wolfgang W Baus; Daniel Ruess; Veerle Visser-Vandewalle; Maximilian I Ruge; Harald Treuer; Martin Kocher
Journal:  Radiat Oncol       Date:  2021-04-16       Impact factor: 3.481

Review 5.  Liquid Biopsies for Molecular Biology-Based Radiotherapy.

Authors:  Erik S Blomain; Everett J Moding
Journal:  Int J Mol Sci       Date:  2021-10-19       Impact factor: 5.923

6.  A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters.

Authors:  Li-Mei Luo; Bao-Tian Huang; Chuang-Zhen Chen; Ying Wang; Chuang-Huang Su; Guo-Bo Peng; Cheng-Bing Zeng; Yan-Xuan Wu; Ruo-Heng Wang; Kang Huang; Zi-Han Qiu
Journal:  Front Oncol       Date:  2022-01-31       Impact factor: 6.244

Review 7.  Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Authors:  Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba
Journal:  Eur J Hybrid Imaging       Date:  2020-12-09

Review 8.  Digital pathology and computational image analysis in nephropathology.

Authors:  Laura Barisoni; Kyle J Lafata; Stephen M Hewitt; Anant Madabhushi; Ulysses G J Balis
Journal:  Nat Rev Nephrol       Date:  2020-08-26       Impact factor: 28.314

9.  An investigation of machine learning methods in delta-radiomics feature analysis.

Authors:  Yushi Chang; Kyle Lafata; Wenzheng Sun; Chunhao Wang; Zheng Chang; John P Kirkpatrick; Fang-Fang Yin
Journal:  PLoS One       Date:  2019-12-13       Impact factor: 3.240

Review 10.  [Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer].

Authors:  Jiawei Li; Xiadong Li; Xueqin Chen; Shenglin Ma
Journal:  Zhongguo Fei Ai Za Zhi       Date:  2020-08-17
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