The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features

[1]  Geoffrey G. Zhang,et al.  Voxel size and gray level normalization of CT radiomic features in lung cancer , 2018, Scientific Reports.

[2]  Dimitris Visvikis,et al.  Dependency of a validated radiomics signature on tumor volume and potential corrections , 2018 .

[3]  J. Canales‐Vázquez,et al.  Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. , 2018, Radiology.

[4]  Estanislao Arana,et al.  Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma. , 2018, Radiology.

[5]  Dimitris Visvikis,et al.  Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  M. Hatt,et al.  Responsible Radiomics Research for Faster Clinical Translation , 2017, The Journal of Nuclear Medicine.

[7]  Matthew Toews,et al.  Predicting survival time of lung cancer patients using radiomic analysis , 2017, Oncotarget.

[8]  F. Rybicki,et al.  Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions? , 2017, Academic radiology.

[9]  Geoffrey G. Zhang,et al.  Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects , 2017, Technology in cancer research & treatment.

[10]  J. E. van Timmeren,et al.  Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study , 2017, Acta oncologica.

[11]  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.

[12]  N. Paragios,et al.  Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[13]  I. Sohn,et al.  Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma , 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[14]  Geoffrey G. Zhang,et al.  Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.

[15]  Chintan Parmar,et al.  Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT , 2017, PloS one.

[16]  Lawrence H. Schwartz,et al.  Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings , 2016, PloS one.

[17]  Lubomir M. Hadjiiski,et al.  Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features , 2016, Tomography.

[18]  Wolfgang Weber,et al.  Reliability of PET/CT Shape and Heterogeneity Features in Functional and Morphologic Components of Non–Small Cell Lung Cancer Tumors: A Repeatability Analysis in a Prospective Multicenter Cohort , 2016, The Journal of Nuclear Medicine.

[19]  Jun Wang,et al.  Prediction of malignant and benign of lung tumor using a quantitative radiomic method , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  M. Hatt,et al.  MO-DE-207B-11: Reliability of PET/CT Radiomics Features in Functional and Morphological Components of NSCLC Lesions: A Repeatability Analysis in a Prospective Multicenter Cohort. , 2016, Medical Physics (Lancaster).

[21]  Erich P Huang,et al.  MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. , 2016, Radiology.

[22]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[23]  Jinzhong Yang,et al.  Measuring Computed Tomography Scanner Variability of Radiomics Features , 2015, Investigative radiology.

[24]  P. Lambin,et al.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[25]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[26]  Berkman Sahiner,et al.  Computerized characterization of lung nodule subtlety using thoracic CT images , 2014, Physics in medicine and biology.

[27]  W. Tsai,et al.  Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study. , 2014, Translational oncology.

[28]  Justin Guinney,et al.  Impact of Bioinformatic Procedures in the Development and Translation of High-Throughput Molecular Classifiers in Oncology , 2013, Clinical Cancer Research.

[29]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[30]  A. Razek,et al.  Soft tissue tumors of the head and neck: imaging-based review of the WHO classification. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[31]  C. Compton,et al.  The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM , 2010, Annals of Surgical Oncology.

[32]  D. Miglioretti,et al.  Rising use of diagnostic medical imaging in a large integrated health system. , 2008, Health affairs.

[33]  L P Clarke,et al.  The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software , 2008, Clinical pharmacology and therapeutics.

[34]  Rangaraj M. Rangayyan,et al.  Fractal Analysis of Contours of Breast Masses in Mammograms , 2007, Journal of Digital Imaging.

[35]  Joan Miquel Torta,et al.  A simple approach to the transformation of spherical harmonic models under coordinate system rotation , 1996 .