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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 01-01-2022 | Positron Emission Tomography | Original Article

AI-enhanced simultaneous multiparametric ¹⁸F-FDG PET/MRI for accurate breast cancer diagnosis

Authors: V. Romeo, P. Clauser, S. Rasul, P. Kapetas, P. Gibbs, P. A. T. Baltzer, M. Hacker, R. Woitek, T. H. Helbich, K. Pinker

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 2/2022

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.

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Title: AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis
Authors: V. Romeo
P. Clauser
S. Rasul
P. Kapetas
P. Gibbs
P. A. T. Baltzer
M. Hacker
R. Woitek
T. H. Helbich
K. Pinker
Publication date: 01-01-2022
Publisher: Springer Berlin Heidelberg
Keywords: Positron Emission Tomography
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Breast Cancer
Breast Cancer
Artificial Intelligence
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 2/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05492-z

Keynote webinar | Spotlight on sleep in brain health

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AI-enhanced simultaneous multiparametric ¹⁸F-FDG PET/MRI for accurate breast cancer diagnosis

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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