AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.
Authors
Romeo, V
Clauser, P
Rasul, S
Kapetas, P
Gibbs, P
Baltzer, PAT
Hacker, M
Woitek, R
Helbich, TH
Pinker, K
Publication Date
2022-01Journal Title
Eur J Nucl Med Mol Imaging
ISSN
1619-7070
Publisher
Springer Science and Business Media LLC
Volume
49
Issue
2
Pages
596-608
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Romeo, V., Clauser, P., Rasul, S., Kapetas, P., Gibbs, P., Baltzer, P., Hacker, M., et al. (2022). AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.. Eur J Nucl Med Mol Imaging, 49 (2), 596-608. https://doi.org/10.1007/s00259-021-05492-z
Description
Funder: Horizon 2020 Framework Programme; doi: http://dx.doi.org/10.13039/100010661
Funder: Medical University of Vienna
Abstract
PURPOSE: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. METHODS: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. RESULTS: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). CONCLUSION: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.
Keywords
Original Article, Advanced Image Analyses (Radiomics and Artificial Intelligence), 18F-FDG PET/MRI, Breast cancer, Artificial intelligence, Radiomics
Identifiers
s00259-021-05492-z, 5492
External DOI: https://doi.org/10.1007/s00259-021-05492-z
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333473
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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