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Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake

Authors :
Sylwia Nowakowska
Karol Borkowski
Carlotta Ruppert
Patryk Hejduk
Alexander Ciritsis
Anna Landsmann
Magda Marcon
Nicole Berger
Andreas Boss
Cristina Rossi
Source :
Bioengineering, Vol 11, Iss 6, p 556 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network’s predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.

Details

Language :
English
ISSN :
23065354
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.fa70e593357479b951b2056c4790433
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering11060556