MRI; breast; feature repeatability; radiomics; Diffusion Magnetic Resonance Imaging; Female; Humans; Prospective Studies; Radiography; Breast/diagnostic imaging; Magnetic Resonance Imaging/methods; Magnetic Resonance Imaging; Radiology, Nuclear Medicine and Imaging
Abstract :
[en] [en] 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.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Granzier, R W Y ; Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands ; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
Ibrahim, Abdalla Khalil ; Université de Liège - ULiège > Université de Liège - ULiège ; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
Primakov, S; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
Keek, S A; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
Halilaj, I; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; Health Innovation Ventures, Maastricht, The Netherlands
Zwanenburg, A ; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden, Rossendorf, Dresden, Germany ; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany ; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany ; Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
Engelen, S M E; Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands
Lobbes, M B I; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands
Lambin, P; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
Woodruff, H C; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
Smidt, M L; Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands ; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
Language :
English
Title :
Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.
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.
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