Journal of Magnetic Resonance Imaging · 2022 · 19 citations · 24 references
MedicineRadiologyDiagnostic ImagingDiagnostic RadiologyNeuroradiologyBrain ImagingImaging AssessmentImaging InterpretationMagnetic Resonance ImagingMedical ImagingRadiographic ImagingIntraclass CorrelationRadiomic AnalysisRadiomic BiomarkersRadiomicsRadiomics In Magnetic Resonance ImagingTest RepeatabilityTest-retest ReliabilityTest-retest Reproducibility
BACKGROUND: Radiomics is the high throughput analysis of medical images using computer algorithms, which specifically assess textural features. It has increasingly been proposed as a tool for the development of imaging biomarkers. However, an important acknowledged limitation of radiomics is the lack of reproducibility of features produced. PURPOSE: To assess reproducibility and repeatability of radiomics variables in brain MRI through a multivisit, multicenter study. STUDY TYPE: Retrospective. POPULATION: Fourteen individuals visiting three institutions twice, 10 males with the mean age of 36.3 years and age range 25-51. FIELD STRENGTH: 3D T1W inversion recovery on three 1.5-T General Electric scanners. ASSESSMENT: Radiomics analysis by a consultant radiologist performed on the T1W images of the whole brain on all visits. All possible radiomics features were generated. STATISTICAL TEST: Concordance correlation coefficient (CCC) and dynamic range (DR) for all variables were calculated to assess the test-retest repeatability. Intraclass correlation coefficients (ICCs) were calculated to investigate the reproducibility of features across centers. RESULTS: Of 1596 features generated, 57 from center 1, 15 from center 2, and 22 from center 3 had a CCC > 0.9 and DR > 0.9. Eight variables had CCC > 0.9 and DR > 0.9 in all centers. Forty-one variables had an ICC of >0.9. No variables had CCC > 0.9, DR > 0.9, and ICC > 0.9. DATA CONCLUSION: Repeatability and reproducibility of variables is a significant limitation of radiomics analysis in 3DT1W brain MRI. Careful selection of radiomic features is required. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.
24
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
A Concordance Correlation Coefficient to Evaluate Reproducibility
Biometrics · 1989 · 8.7K citations
Radiomics: Images Are More than Pictures, They Are Data
Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak · Radiology · 2015 · 8.3K citations
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen, Andriy Fedorov, Chintan Parmar et al. · Cancer Research · 2017 · 6.5K citations · Full text