Test–Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability. Issue 2 (22nd December 2021)

• Record Type: Journal Article
• Authors: Granzier, R.W.Y.; Ibrahim, A.; Primakov, S.; Keek, S.A.; Halilaj, I.; Zwanenburg, A.; Engelen, S.M.E.; Lobbes, M.B.I.; Lambin, P.; Woodruff, H.C.; Smidt, M.L.
• Is Part Of: Journal of magnetic resonance imaging. Volume 56:Issue 2(2022)
• Page Start: 592
• Page End: 604
• Publication Date: 2021-12-22
• Subjects: breast -- MRI -- radiomics -- feature repeatability; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.28027
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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