Image biomarker standardisation initiative version 1 . 4

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.

[1]  Stephen B. Duffull,et al.  Quantification of Lean Bodyweight , 2005, Clinical pharmacokinetics.

[2]  Stefan Schirra,et al.  How Reliable Are Practical Point-in-Polygon Strategies? , 2008, ESA.

[3]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[5]  Tsuhan Chen,et al.  Efficient feature extraction for 2D/3D objects in mesh representation , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[6]  Michael J. Todd,et al.  On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids , 2007, Discret. Appl. Math..

[7]  P. Moran Notes on continuous stochastic phenomena. , 1950, Biometrika.

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

[9]  Dimitris Visvikis,et al.  Characterization of PET/CT images using texture analysis: the past, the present… any future? , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[10]  Leslie M. Collins,et al.  Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data , 2016, SPIE Medical Imaging.

[11]  H. Aerts,et al.  Applications and limitations of radiomics , 2016, Physics in medicine and biology.

[12]  Thomas Lewiner,et al.  Efficient Implementation of Marching Cubes' Cases with Topological Guarantees , 2003, J. Graphics, GPU, & Game Tools.

[13]  Peer Stelldinger,et al.  Topological Equivalence between a 3D Object and the Reconstruction of Its Digital Image , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[15]  F. Turkheimer,et al.  A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015 .

[16]  Jesús Angulo,et al.  Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification , 2014, IEEE Transactions on Biomedical Engineering.

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

[18]  Joel Max,et al.  Quantizing for minimum distortion , 1960, IRE Trans. Inf. Theory.

[19]  M. Hatt,et al.  18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient Cohort , 2015, The Journal of Nuclear Medicine.

[20]  D. Townsend,et al.  Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET , 2015, The Journal of Nuclear Medicine.

[21]  R. Wahl,et al.  From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors , 2009, Journal of Nuclear Medicine.

[22]  Robert J. Gillies,et al.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis , 2015, Scientific Reports.

[23]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[24]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[25]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[26]  Ronald Boellaard,et al.  University of Groningen Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [F-18]FDG-PET/CT Studies , 2016 .

[27]  I. Buvat,et al.  Partial-Volume Effect in PET Tumor Imaging* , 2007, Journal of Nuclear Medicine.

[28]  William G. Wee,et al.  Neighboring gray level dependence matrix for texture classification , 1982, Comput. Graph. Image Process..

[29]  J. Bradley,et al.  Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[30]  Ronald Boellaard,et al.  Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[31]  A. van der Schaaf,et al.  CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva. , 2017, Radiotherapy and Oncology.

[32]  Richard M. Heiberger,et al.  Statistical Analysis and Data Display , 2004 .

[33]  Leonid Khachiyan,et al.  Rounding of Polytopes in the Real Number Model of Computation , 1996, Math. Oper. Res..