Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images.
FLAIR
MRI
brain
radiomics
reproducibility
robustness
sensitivity
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
02
06
2020
revised:
14
08
2020
accepted:
14
08
2020
pubmed:
31
8
2020
medline:
29
1
2021
entrez:
1
9
2020
Statut:
ppublish
Résumé
Radiomics in neuroimaging has gained momentum as a noninvasive prediction tool not only to differentiate between types of brain tumors, but also to create phenotypic signatures in neurological and neuropsychiatric disorders. However, there is currently little understating about the robustness and reproducibility of radiomic features in a baseline normative population. To investigate the intra- and interscanner reproducibility, spatial robustness, and sensitivity of radiomics on fluid attenuation inversion recovery (FLAIR) images, which are widely used in neuro-oncology investigations. Retrospective. Three separate datasets of healthy controls: 1) 87 subjects (age range 12-64 years), 2) intrascanner three timepoints, four subjects, and 3) interscanner, eight subjects at three different sites. T Spatial variance across lobes, and their relation with age/gender, intra- and inter-scanner reproducibility (with and without site harmonization) of radiomics. Analysis of variance (ANOVA), interclass correlation (ICC), coefficient of variation (CoV), Bland-Altman analysis. Analysis of data revealed no differences between genders; however, multiple radiomic features were highly associated with age (P < 0.05). Spatial variability was also evaluated where only 29.04% gray matter and 38.7% white matter features demonstrated an ICC >0.5. Furthermore, the results demonstrated intra-scanner reliability (ICC >0.5); however, inter-scanner reproducibility was poor, with ICC < 0.5 for 82% gray matter and 78.5% white matter features. The inter-scanner reliability improved (ICC < 0.5 for 39.67% gray matter and 38% white matter features) using site-harmonization techniques. These findings suggest that, accounting for age, spatial locations in radiomics-based analysis and use of intersite radiomics harmonization is crucial before interpreting these features for pathological inference. Level of Evidence 3. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:394-407.
Sections du résumé
BACKGROUND
Radiomics in neuroimaging has gained momentum as a noninvasive prediction tool not only to differentiate between types of brain tumors, but also to create phenotypic signatures in neurological and neuropsychiatric disorders. However, there is currently little understating about the robustness and reproducibility of radiomic features in a baseline normative population.
PURPOSE
To investigate the intra- and interscanner reproducibility, spatial robustness, and sensitivity of radiomics on fluid attenuation inversion recovery (FLAIR) images, which are widely used in neuro-oncology investigations.
STUDY TYPE
Retrospective.
POPULATION
Three separate datasets of healthy controls: 1) 87 subjects (age range 12-64 years), 2) intrascanner three timepoints, four subjects, and 3) interscanner, eight subjects at three different sites.
FIELD STRENGTH/SEQUENCE
T
ASSESSMENT
Spatial variance across lobes, and their relation with age/gender, intra- and inter-scanner reproducibility (with and without site harmonization) of radiomics.
STATISTICAL TESTS
Analysis of variance (ANOVA), interclass correlation (ICC), coefficient of variation (CoV), Bland-Altman analysis.
RESULTS
Analysis of data revealed no differences between genders; however, multiple radiomic features were highly associated with age (P < 0.05). Spatial variability was also evaluated where only 29.04% gray matter and 38.7% white matter features demonstrated an ICC >0.5. Furthermore, the results demonstrated intra-scanner reliability (ICC >0.5); however, inter-scanner reproducibility was poor, with ICC < 0.5 for 82% gray matter and 78.5% white matter features. The inter-scanner reliability improved (ICC < 0.5 for 39.67% gray matter and 38% white matter features) using site-harmonization techniques.
DATA CONCLUSION
These findings suggest that, accounting for age, spatial locations in radiomics-based analysis and use of intersite radiomics harmonization is crucial before interpreting these features for pathological inference. Level of Evidence 3. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:394-407.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
394-407Informations de copyright
© 2020 International Society for Magnetic Resonance in Medicine.
Références
Ferris J, Chang PD, Chow DS. Radiomics and machine learning. In: Pope WB, editor. Glioma imaging: Physiologic, metabolic, and molecular approaches. Cham, Switzerland: Springer International Publishing; 2020. p 241-249.
Lu C-F, Hsu F-T, KL-C H, et al. Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 2018;24(18):4429-4436.
Gore S, Chougule T, Jagtap J, Saini J, Ingalhalikar M. A review of radiomics and deep predictive modeling in glioma characterization. Acad Radiol 2020; epub ahead of print.
Mazurowski MA. Radiogenomics: What it is and why it is important. J Am Coll Radiol 2015;12(8):862-866.
Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R. Radiogenomics: Bridging imaging and genomics. Abdom Radiol 2019;44(6):1960-1984.
Yu J, Shi Z, Lian Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 2017;27(8):3509-3522.
Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5(1):4006.
Arita H, Kinoshita M, Kawaguchi A, et al. Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas. Sci Rep 2018;8(1):11773.
Cho H-h, Lee S-H, Kim J, Park H. Classification of the glioma grading using radiomics analysis. PeerJ 2018;6:e5982.
Wang K, Wang Y, Fan X, et al. Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients. Neuro Oncol 2016;18(4):589-597.
Artzi M, Liberman G, Blumenthal DT, Aizenstein O, Bokstein F, Ben Bashat D. Differentiation between vasogenic edema and infiltrative tumor in patients with high-grade gliomas using texture patch-based analysis. J Magn Reson Imaging 2018;48:729-736.
Rathore S, Akbari H, Doshi J, et al. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: Implications for personalized radiotherapy planning. J Med Imaging 2018;5(2):021219.
Feng F, Wang P, Zhao K, et al. Radiomic features of hippocampal subregions in Alzheimer's disease and amnestic mild cognitive impairment. Front Aging Neurosci 2018;10:1-11.
Feng Q, Chen Y, Liao Z, et al. Corpus callosum radiomics-based classification model in Alzheimer's disease: A case-control study. Front Neurol 2018;9:618.
Chaddad A, Desrosiers C, Hassan L, Tanougast C. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci 2017;18(1):52.
Shinde S, Prasad S, Saboo Y, et al. Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clin 2019;22:101748.
Bakas S, Akbari H, Sotiras A, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 2017;4(1):170117.
Naganawa S, Sato K, Katagiri T, Mimura T, Ishigaki T. Regional ADC values of the normal brain: Differences due to age, gender, and laterality. Eur Radiol 2003;13(1):6-11.
Kumar V, Gu Y, Basu S, et al. Radiomics: The process and the challenges. Magn Reson Imaging 2012;30(9):1234-1248.
Zhao B, Tan Y, Tsai W-Y, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 2016;6:23428.
Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, et al. Radiomics of CT features may be nonreproducible and redundant: Influence of CT acquisition parameters. Radiology 2018;288(2):407-415.
Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of Radiomic features: A systematic review. Int J Radiat Oncol Biol Phys 2018;102(4):1143-1158.
Baeßler B, Weiss K, Pinto Dos Santos D. Robustness and reproducibility of radiomics in magnetic resonance imaging: A phantom study. Invest Radiol 2019;54(4):221-228.
Sahoo P, Gupta RK, Gupta PK, et al. Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1-weighted DCE-MRI. Magn Reson Imaging 2017;44:32-37.
Bhattacharjee R, Gupta RK, Patir R, Vaishya S, Ahlawat S, Singh A. Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma. J Magn Reson Imaging 2020;51(1):225-233.
Barnes J, Ridgway GR, Bartlett J, et al. Head size, age and gender adjustment in MRI studies: A necessary nuisance? Neuroimage 2010;53(4):1244-1255.
Fouke SJ, Benzinger T, Gibson D, Ryken TC, Kalkanis SN, Olson JJ. The role of imaging in the management of adults with diffuse low grade glioma: A systematic review and evidence-based clinical practice guideline. J Neurooncol 2015;125(3):457-479.
Ortiz-Ramón R, Valdés Hernández MDC, González-Castro V, et al. Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images. Comput MedImaging Graph. 2019;74:12-24.
Villanueva-Meyer JE, Wood MD, Choi B, et al. MRI features and IDH mutational status in grade II diffuse gliomas: Impact on diagnosis and prognosis. Am J Roentgenol 2018;210(3):621-628.
Cox RW, J. Ashburner H, Breman K. et al. “A (sort of) new image data format standard: NiFTI-1.” 10th Annual Meeting of Organisation of Human Brain Mapping, Budapest, Hungary, June 2004.
Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage 2012;62(2):782-790.
Avants B, Tustison N, Song G. Advanced normalization tools: V1.0. Insight J 2009;681:1-35.
Mao Z, Ma L, Zhao M, Xiao X. SUSAN structure preserving filtering for mesh denoising. Visual Comput 2006;22(4):276-284.
van Griethuysen JJ, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77(21):e104-e107.
Koo TK, Li MY. A guideline of selecting and reporting Intraclass correlation coefficients for reliability research. J Chiropr Med 2016;15(2):155-163.
Altman DG, Bland JM. Measurement in medicine: The analysis of method comparison studies. J R Stat Soc 1983;32(3):307-317.
Fortin J-P, Parker D, Tunç B, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 2017;161:149-170.
Yu M, Linn KA, Cook PA, et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp 2018;39(11):4213-4227.