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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

  • Ming Fan
  • Peng Zhang
  • Yue Wang
  • Weijun Peng
  • Shiwei Wang
  • Xin Gao
  • Maosheng XuEmail author
  • Lihua LiEmail author
Breast
  • 45 Downloads

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.

Keywords

Breast neoplasms Magnetic resonance imaging Diagnostic imaging 

Abbreviations

CAM

Convex analysis of mixtures

GLCM

Grey level co-occurrence matrix

KPC

Kinetic pattern clustering

NAC

Neoadjuvant chemotherapy

PER

Peak enhancement rate

PVE

Partial-volume effect

TTP

Time-to-peak

WIS

Wash-in-slope

WOS

Wash-out-slope

Notes

Funding

This work has received funding by the National Natural Science Foundation of China (61731008, 61871428, and 61401131), the Natural Science Foundation of Zhejiang Province of China (LJ19H180001, LZ15F010001), and the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3450-01.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Professor Lihua Li.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5891_MOESM1_ESM.docx (752 kb)
ESM 1 (DOCX 751 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Institute of Biomedical Engineering and InstrumentationHangzhou Dianzi UniversityHangzhouChina
  2. 2.Department of Electrical and Computer EngineeringVirginia Polytechnic Institute and State UniversityArlingtonUSA
  3. 3.Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
  4. 4.Department of RadiologyFirst Affiliated Hospital of Zhejiang Chinese Medical UniversityZhejiangChina
  5. 5.Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)King Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia

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