Dataset Information

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

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.

SUBMITTER: Romeo V

PROVIDER: S-EPMC9406327 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Romeo Valeria V Kapetas Panagiotis P Clauser Paola P Baltzer Pascal A T PAT Rasul Sazan S Gibbs Peter P Hacker Marcus M Woitek Ramona R Pinker Katja K Helbich Thomas H TH

Cancers 20220816 16

<h4>Purpose</h4>To investigate whether a machine learning (ML)-based radiomics model applied to <sup>18</sup>F-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.<h4>Methods</h4>Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous <sup>18</sup>F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was ...[more]

PMID: 36010936

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Dataset Information

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

Purpose

Methods

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

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

Purpose

Methods

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

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