Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis
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Heng Li | Rachel B. Ger | Shouhao Zhou | Clifton D. Fuller | Rebecca M. Howell | R. Jason Stafford | Laurence E. Court | Dennis S. Mackin | Daniel F. Craft | Rick R. Layman | Hesham Elhalawani | A. Kyle Jones | R. Stafford | D. Mackin | L. Court | A. Jones | Shouhao Zhou | R. Howell | Heng Li | H. Elhalawani | C. Fuller | R. Ger | D. Craft | R. Layman
[1] Laurence Court,et al. Effect of tube current on computed tomography radiomic features , 2018, Scientific Reports.
[2] Laurence Court,et al. Harmonizing the pixel size in retrospective computed tomography radiomics studies , 2017, PloS one.
[3] Jinzhong Yang,et al. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. , 2015, Medical physics.
[4] Jinzhong Yang,et al. Preliminary investigation into sources of uncertainty in quantitative imaging features , 2015, Comput. Medical Imaging Graph..
[5] W. Tsai,et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging , 2016, Scientific Reports.
[6] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[7] Matthias Guckenberger,et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma , 2017, Acta oncologica.
[8] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[9] Rebecca M. Howell,et al. Preparation and fabrication of a full‐scale, sagittal‐sliced, 3D‐printed, patient‐specific radiotherapy phantom , 2017, Journal of applied clinical medical physics.
[10] Peter Balter,et al. Material matters: Analysis of density uncertainty in 3D printing and its consequences for radiation oncology , 2018, Medical physics.
[11] Jinzhong Yang,et al. Measuring Computed Tomography Scanner Variability of Radiomics Features , 2015, Investigative radiology.
[12] Peter A Balter,et al. Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer , 2016 .
[13] Robert King,et al. Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..
[14] Jinzhong Yang,et al. Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. , 2018, Journal of visualized experiments : JoVE.
[15] Benjamin Movsas,et al. On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers , 2017, Medical physics.
[16] Mary M. Galloway,et al. Texture analysis using gray level run lengths , 1974 .
[17] W. Tsai,et al. Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study. , 2014, Translational oncology.
[18] Issam El-Naqa,et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer , 2017, Scientific Reports.
[19] Xiaoou Tang,et al. Texture information in run-length matrices , 1998, IEEE Trans. Image Process..
[20] Benjamin Haibe-Kains,et al. Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer , 2015, Scientific Reports.
[21] Odile Casiraghi,et al. Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status. , 2017, Oral oncology.
[22] Matthias Guckenberger,et al. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. , 2017, International journal of radiation oncology, biology, physics.
[23] P. Lambin,et al. Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer , 2015, Front. Oncol..
[24] Geoffrey G. Zhang,et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.
[25] Peter Balter,et al. Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer , 2017, Scientific Reports.
[26] Osama Mawlawi,et al. Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors. , 2016, Radiology.