Technical Note: An IBEX adaption toward image biomarker standardization.
IBSI
feature extraction
imaging biomarkers
radiomics
texture analysis
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
received:
13
08
2019
revised:
12
11
2019
accepted:
26
11
2019
pubmed:
13
12
2019
medline:
30
12
2020
entrez:
13
12
2019
Statut:
ppublish
Résumé
Interest in the field of radiomics is rapidly growing because of its potential to characterize tumor phenotype and provide predictive and prognostic information. Nevertheless, the reproducibility and robustness of radiomics studies are hampered by the lack of standardization in feature definition and calculation. In the context of the image biomarker standardization initiative (IBSI), we investigated the grade of compliance of the image biomarker explorer (IBEX), a free open-source radiomic software, and we developed and validated standardized-IBEX (S-IBEX), an adaptation of IBEX to IBSI. Image biomarker explorer source code was checked against IBSI standard. Both the feature implementation and the overall image preprocessing chain were evaluated. Sections were re-implemented wherever differences emerged: in particular, contour-to-binary-mask conversion, image sub-portion extraction, re-segmentation, gray-level discretization and interpolation were aligned to IBSI. All reported IBSI features were implemented in S-IBEX. On a patient phantom, S-IBEX was validated by benchmarking five different preprocessing configurations proposed by IBSI. Most IBEX feature definitions are IBSI compliant; however, IBEX preprocessing introduces non-negligible nonconformities, resulting in feature values not aligned with the corresponding IBSI benchmarks. On the contrary, S-IBEX features are in agreement with the standard regardless of preprocessing configurations: the percentage of features equal to their benchmark values ranges from 98.1% to 99.5%, with overall maximum percentage error below 1%. Moreover, the impact of noncompliant preprocessing steps has been assessed: in these cases, the percentage of features equal to the standard drops below 35%. The use of standardized software for radiomic feature extraction is essential to ensure the reproducibility of results across different institutions, easing at the same time their external validation. This work presents and validates S-IBEX, a free IBSI-compliant software, developed upon IBEX, for feature extraction that is both easy to use and quantitatively accurate.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1167-1173Informations de copyright
© 2019 American Association of Physicists in Medicine.
Références
Szczypiński PM, Strzelecki M, Materka A, Klepaczko A. MaZda-A software package for image texture analysis. Comput Methods Programs Biomed. 2009;94:66-76
Fang Y-HD, Lin C-Y, Shih M-J, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int. 2014;2014:1-9.
Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. Ibex: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42:1341-1353.
Nioche C, Orlhac F, Boughdad S, et al. Lifex: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786-4789.
Götz M, Nolden M, Maier-Hein K. MITK Phenotyping: an open-source toolchain for image-based personalized medicine with radiomics. Radiother Oncol. 2019;131:108-111.
Pfaehler E, Zwanenburg A, de Jong JR, Boellaard R. RACAT: an open source and easy to use radiomics calculator tool. PLoS ONE. 2019;14:1-26.
Apte AP, Iyer A, Crispin-Ortuzar M, et al. Technical note: extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research. Med Phys. 2018;45:3713-3720.
Hosny A, van Griethuysen JJM, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104-e107.
Leger S, Zwanenburg A, Pilz K, et al. A comparative study of machine learning methods for time-To-event survival data for radiomics risk modelling. Sci Rep. 2017;7:1-11.
Zwanenburg A, Leger S, Agolli L, et al. Assessing robustness of radiomic features by image perturbation. Sci Rep. 2019;9:1-31.
van Dijk LV, Thor M, Steenbakkers RJHM, et al. Parotid gland fat related Magnetic Resonance image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol. 2018;128:459-466.
Lucia F, Visvikis D, Desseroit MC, et al. Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2018;45:768-786.
Prezzi D, Owczarczyk K, Bassett P, et al. Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer. Eur Radiol. 29:5227-5235.
Lecler A, Duron L, Balvay D, et al. Combining multiple magnetic resonance imaging sequences provides independent reproducible radiomics features. Sci Rep. 2019;9:1-8.
Bousabarah K, Temming S, Hoevels M, et al. Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy. Strahlentherapie und Onkol. 2019;195:830-842.
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151-165.
Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. doi:https://doi.org/10.1038/s41598-017-00426-y.
Vallières M, Zwanenburg A, Badic B. Cheze Le Rest C, Visvikis D, Hatt M. Responsible radiomics research for faster clinical translation. J Nucl Med. 2017;59:189-193.
Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. December 2016. http://arxiv.org/abs/1612.07003. Accessed September 10, 2018.
Haralick RM, Shanmugam K. Dinstein IH Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3;610-621.
Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 2008;4:172-179.
Thibault G, Angulo J, Meyer F. Advanced statistical matrices for texture characterization: Application to DNA chromatin and microtubule network classification. In: Proceedings - International Conference on Image Processing, ICIP. IEEE; 2011:53-56. doi:https://doi.org/10.1109/ICIP.2011.6116401.
Amadasun M, King R. Texural features corresponding to texural properties. IEEE Trans Syst Man Cybern. 1989;19:1264-1274.
Sun C, Wee WG. Neighboring gray level dependence matrix for texture classification. Comput Vision Graph Image Process. 1983;23:341-352.
Branchini M, Zorz A, Zucchetta P, et al. Impact of acquisition count statistics reduction and SUV discretization on PET radiomic features in pediatric 18F-FDG-PET/MRI examinations. Phys Medica. 2019;59:117-126.
S-IBEX. https://github.com/abettinelli/SIBEX_Source.
Fave X, MacKin D, Yang J, et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys. 2015;42:6784-6797.
Hanania AN, Bantis LE, Feng Z, et al. Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget. 2016;7:85776-85784.
Yang J, Zhang L, Fave XJ, et al. Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph. 2016;48:1-8.
Fave X, Zhang L, Yang J, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017;7:1-11.
Reuben A, Spencer CN, Prieto PA, et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. npj Genomic Med. 2017;2
Zhang Z, Yang J, Ho A, et al. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol. 2018;28:2255-2263.
Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT, de Azevedo-Marques PM. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed. 2018;159:23-30.
Ger RB, Cardenas CE, Anderson BM, et al. Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. J Vis Exp. 2018.
Tiwari P, Prasanna P, Rogers L, et al. Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI. In: Aylward S, Hadjiiski LM, eds. Medical Imaging 2014: Computer-Aided Diagnosis. Vol. 9035. NIH Public Access; 2014:90352B. doi:https://doi.org/10.1117/12.2043969.
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-762.
Lambin P. Radiomics digital phantom. CancerData. 2016. https://doi.org/10.17195/candat.2016.08.1.
Engwirda Darren. A fast “point-in-polygon” test for MATLAB / OCTAVE. https://github.com/dengwirda/inpoly. 2018. Accessed June 25, 2019.
Schirra S. How Reliable Are Practical Point-in-Polygon Strategies?. 2008:744-755. https://doi.org/10.1007/978-3-540-87744-8_62.
Larue RTHM, van Timmeren JE, de Jong EEC, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol. 2017;56:1544-1553.
Duron L, Balvay D, Vande PS, et al. Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS ONE. 2019;14:1-14.
Leijenaar RTH, Nalbantov G, Carvalho S, et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015;5:11075.