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European Radiology
Published in: European Radiology 8/2019

01-08-2019 | Magnetic Resonance Imaging | Breast

Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer

Authors: Ming Fan, Peng Zhang, Yue Wang, Weijun Peng, Shiwei Wang, Xin Gao, Maosheng Xu, Lihua Li

Published in: European Radiology | Issue 8/2019

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Abstract

Objectives

This study aimed to predict the molecular subtypes of breast cancer via intratumoural and peritumoural radiomic analysis with subregion identification based on the decomposition of contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

The study included 211 women with histopathologically confirmed breast cancer. We utilised a completely unsupervised convex analysis of mixtures (CAM) method by unmixing dynamic imaging series from heterogeneous tissues. Each tumour and the surrounding parenchyma were thus decomposed into multiple subregions, representing different vascular characterisations, from which radiomic features were extracted. A random forest model was trained and tested using a leave-one-out cross-validation (LOOCV) method to predict breast cancer subtypes. The predictive models from tumoural and peritumoural subregions were fused for classification.

Results

Tumour and peritumour DCE-MR images were decomposed into three compartments, representing plasma input, fast-flow kinetics, and slow-flow kinetics. The tumour subregion related to fast-flow kinetics showed the best performance among the subregions for differentiating between patients with four molecular subtypes (area under the receiver operating characteristic curve (AUC) = 0.832), exhibiting an AUC value significantly (p < 0.0001) higher than that obtained with the entire tumour (AUC = 0.719). When the tumour- and parenchyma-based predictive models were fused, the performance, measured as the AUC, increased to 0.897; this value was significantly higher than that obtained with other tumour partition methods.

Conclusions

Radiomic analysis of intratumoural and peritumoural heterogeneity based on the decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features and serve as a valuable clinical marker to enhance the prediction of breast cancer subtypes.

Key Points

• Decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features.
• Fusion of intratumoural- and peritumoural-based predictive models improves the prediction of breast cancer subtypes.
Appendix
Available only for authorised users
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Metadata
Title
Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer
Authors
Ming Fan
Peng Zhang
Yue Wang
Weijun Peng
Shiwei Wang
Xin Gao
Maosheng Xu
Lihua Li
Publication date
01-08-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5891-3

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