Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.


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:
08 2022
Historique:
revised: 03 12 2021
received: 27 08 2021
accepted: 03 12 2021
pubmed: 23 12 2021
medline: 14 7 2022
entrez: 22 12 2021
Statut: ppublish

Résumé

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. Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements. Prospective. 11 healthy female volunteers. 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. 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. 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. 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. 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. 2 TECHNICAL EFFICACY STAGE: 1.

Sections du résumé

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.

Identifiants

pubmed: 34936160
doi: 10.1002/jmri.28027
pmc: PMC9544420
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

592-604

Informations de copyright

© 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

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Auteurs

R W Y Granzier (RWY)

Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.
GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.

A Ibrahim (A)

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.
Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.
Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.

S 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.

S A Keek (SA)

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.

I 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.

A 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.

S M E Engelen (SME)

Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.

M B I Lobbes (MBI)

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.

P 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.

H C Woodruff (HC)

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.

M L Smidt (ML)

Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.
GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.

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