TY - JOUR
T1 - Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability
AU - Granzier, R.W.Y.
AU - Ibrahim, A.
AU - Primakov, S.
AU - Keek, S.A.
AU - Halilaj, I.
AU - Zwanenburg, A.
AU - Engelen, S.M.E.
AU - Lobbes, M.B.I.
AU - Lambin, P.
AU - Woodruff, H.C.
AU - Smidt, M.L.
N1 - Funding Information:
Contract grant sponsor: Renée W.Y. Granzier received a salary from Kankeronderzoekfonds Limburg. Authors acknowledge financial support from an ERC advanced grant (ERC‐ADG‐2015, 694812–Hypoximmuno). Authors also acknowledge financial support from the European Union's Horizon 2020 research and innovation program under grant agreement: MSCA‐ITN‐PREDICT 766276, CHAIMELEON 952172 and EuCanImage 952103.
Publisher Copyright:
© 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
PY - 2022/8
Y1 - 2022/8
N2 - Background Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. Objective Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements. Study Type Prospective. Population 11 healthy female volunteers. Field Strength/Sequence 1.5 T; MRI exams, comprising T2-weighted turbo spin-echo (T2W) sequence, native T1-weighted turbo gradient-echo (T1W) sequence, diffusion-weighted imaging (DWI) sequence using b-values 0/150/800, and corresponding derived ADC maps. Assessment 18 MRI exams (three test-retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z-score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z-score normalization + grayscale discretization using 32 and 64 bins with and without BFC. Statistical Tests Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut-off value of CCC > 0.90. Results Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z-score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. Data Conclusion Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. Level of Evidence 2 Technical Efficacy Stage 1
AB - Background Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. Objective Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements. Study Type Prospective. Population 11 healthy female volunteers. Field Strength/Sequence 1.5 T; MRI exams, comprising T2-weighted turbo spin-echo (T2W) sequence, native T1-weighted turbo gradient-echo (T1W) sequence, diffusion-weighted imaging (DWI) sequence using b-values 0/150/800, and corresponding derived ADC maps. Assessment 18 MRI exams (three test-retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z-score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z-score normalization + grayscale discretization using 32 and 64 bins with and without BFC. Statistical Tests Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut-off value of CCC > 0.90. Results Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z-score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. Data Conclusion Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. Level of Evidence 2 Technical Efficacy Stage 1
KW - breast
KW - MRI
KW - radiomics
KW - feature repeatability
KW - REPRODUCIBILITY
KW - HARMONIZATION
KW - PERFORMANCE
KW - COEFFICIENT
KW - IMPACT
U2 - 10.1002/jmri.28027
DO - 10.1002/jmri.28027
M3 - Article
C2 - 34936160
SN - 1053-1807
VL - 56
SP - 592
EP - 604
JO - Journal of Magnetic Resonance Imaging
JF - Journal of Magnetic Resonance Imaging
IS - 2
ER -