Technical Note: ROdiomX: A Validated Software for Radiomics Analysis of Medical Images in Radiation Oncology.

PURPOSE This study introduces an in-house-designed software platform (ROdiomX) for the radiomics analysis of medical images in radiation oncology. ROdiomX is a MATLAB-based framework for the computation of radiomic features and feature aggregation techniques in compliance with the Image-Biomarker-Standardization-Initiative (IBSI) guidelines, which includes pre-processing protocols and quantitative benchmark results for analysis of computational phantom images. METHODS AND MATERIALS The ROdiomX software system consists of a series of computation cores implemented on the basis of the guidelines proposed by the IBSI. It is capable of quantitative computation of the following 11 different feature categories: Local-Intensity, Intensity-Histogram, Intensity-Based-Statistical, Intensity-Volume-Histogram, Gray-Level-Co-occurrence, Gray-Level-Run-Length, Gray-Level-Size-Zone, Gray-Level-Distance-Zone, Neighborhood-Grey-Tone-Difference, Neighboring-Grey-Level-Dependence, and Morphological feature. ROdiomX was validated against benchmark values for the IBSI 3D digital phantom, as well as one designed in-house (HFH). The Intra-class correlation coefficient (ICC) for estimating the degree of absolute agreement between ROdiomX computation and benchmark values for different features at the 95% confidence level (CL) was used for comparison. RESULTS Among the 11 feature categories with 151 total features including 10 different feature aggregation methods (following the IBSI guidelines), the percent difference between absolute feature values computed by the ROdiomX software and benchmark values reported for IBSI and HFH digital phantoms were 0.14% ± 0.43% and 0.11% ± 0.27% respectively. The ICC values were ≥0.997 for all ten feature categories for both the IBSI and HFH digital phantoms. CONCLUSION The authors successfully developed a platform for computation of quantitative radiomic features. The image preprocessing and computational software cores were designed following the procedures specified by the IBSI. Benchmarking testing was in excellent agreement against the IBSI and HFH-designed computational phantoms.

[1]  Steffen Löck,et al.  Assessing robustness of radiomic features by image perturbation , 2018, Scientific Reports.

[2]  I. Chetty,et al.  Application of radiomics for prediction of HPV status for patients with head and neck cancers. , 2019, Medical physics.

[3]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[4]  Erich P Huang,et al.  Metrology Standards for Quantitative Imaging Biomarkers. , 2015, Radiology.

[5]  Issam El-Naqa,et al.  Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..

[6]  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.

[7]  Ronald Boellaard,et al.  Repeatability of metabolically active tumor volume measurements with FDG PET/CT in advanced gastrointestinal malignancies: a multicenter study. , 2014, Radiology.

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

[9]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

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

[11]  Stuart A. Taylor,et al.  Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.

[12]  M. Soussan,et al.  A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET , 2018, The Journal of Nuclear Medicine.

[13]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

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

[15]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[16]  R. Steenbakkers,et al.  The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.

[17]  Laurence Court,et al.  Harmonizing the pixel size in retrospective computed tomography radiomics studies , 2017, PloS one.

[18]  Benjamin Haibe-Kains,et al.  Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma. , 2018, International journal of radiation oncology, biology, physics.

[19]  Jen-Tzung Chien,et al.  Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation. , 2018, International journal of radiation oncology, biology, physics.

[20]  Jayashree Kalpathy-Cramer,et al.  Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges , 2017, Scientific Data.

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

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

[23]  R Mohan,et al.  Using Pretreatment Radiomics and Delta-Radiomics Features to Predict Non-Small Cell Lung Cancer Patient Outcomes. , 2017, International journal of radiation oncology, biology, physics.

[24]  I. El Naqa,et al.  Beyond imaging: The promise of radiomics. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[25]  R. Gillies,et al.  Repeatability and Reproducibility of Radiomic Features: A Systematic Review , 2018, International journal of radiation oncology, biology, physics.

[26]  Brian O'Sullivan,et al.  Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study , 2018, The British journal of radiology.

[27]  Raymond H Mak,et al.  CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[28]  S. Armato,et al.  Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules. , 2015, Journal of the American College of Radiology : JACR.

[29]  Harini Veeraraghavan,et al.  Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research. , 2018, Medical physics.

[30]  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.

[31]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[32]  Olivier Gevaert,et al.  Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes , 2019, EBioMedicine.

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

[34]  I. Chetty,et al.  Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis , 2019, Front. Oncol..

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

[36]  Geoffrey G. Zhang,et al.  Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms , 2017, Journal of applied clinical medical physics.

[37]  Dan Han,et al.  A biomarker basing on radiomics for the prediction of overall survival in non–small cell lung cancer patients , 2018, Respiratory Research.

[38]  Clifton D Fuller,et al.  Radiomics in head and neck cancer: from exploration to application. , 2016, Translational cancer research.