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. 2022 Aug 16;14(16):3944.
doi: 10.3390/cancers14163944.

A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer

Valeria Romeo  1   2 , Panagiotis Kapetas  2 , Paola Clauser  2 , Pascal A T Baltzer  2 , Sazan Rasul  3 , Peter Gibbs  4 , Marcus Hacker  3 , Ramona Woitek  2   5 , Katja Pinker  2   4 , Thomas H Helbich  2
Affiliations

A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer

Valeria Romeo et al. Cancers (Basel). .

Abstract

Purpose: To investigate whether a machine learning (ML)-based radiomics model applied to 18F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC.

Methods: Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous 18F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions.

Results: Eight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images.

Conclusion: A ML-based radiomics model applied to 18F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a "virtual biopsy" might be performed with radiomics signatures.

Keywords: 18F-FDG PET/MRI; artificial intelligence; breast cancer; machine learning.

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Conflict of interest statement

K.P., T.H.H., P.A.T.B. and P.C. received payment for activities not related to the present article, including lectures and service on speakers bureaus and for travel/accommodations/meeting expenses, related to activities listed from the European Society of Breast Imaging, the IDKD 2019 (educational course, K.P. and T.H.H.), Siemens Healthineers, Guerbet (T.H.H.), Novomed (T.H.H.) and for consulting activities from Vara/Merantix Healthcare GmbH and AURA Health Technologies GmbH (K.P.).

Figures

Figure 1
Figure 1
Examples of 2D ROI placement for the extraction of quantitative parameters (mean transit time; plasma flow; volume distribution; ADC mean; and SUVmax, mean, and minimum) (A–C), and whole tumor segmentation for radiomics features (first, second, and higher order) extraction (D–G) from primary BC tumor lesions on DCE (A,E), DWI (B,F), PET (C,G), and T2-weighted (D) images.
Figure 2
Figure 2
Flowchart of patient selection. * Low quality images, DCE images for which maps calculation was not feasible, small breast cancer lesions.
See this image and copyright information in PMC

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