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Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions

  • Breast
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European Radiology Aims and scope Submit manuscript

A Commentary to this article was published on 07 November 2023

Abstract

Objectives

To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications.

Materials and methods

From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes.

Results

The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970).

Conclusion

In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity.

Clinical relevance statement

Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions.

Key Points

• Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions.

• The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions.

• This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

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Abbreviations

AUC:

Area under the curve

DCE-MRI:

Dynamic contrast-enhanced magnetic resonance imaging

ROC:

Receiver operating characteristic

ROI:

Region of interest

TIC:

Time-intensity curve

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Funding

This study was partially supported by Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province (2020B1212060051), Guangdong Innovation Platform of Translational Research for Cerebrovascular Diseases, Shenzhen Clinical Research Center for Cancer (No. [2021] 287) and Shenzhen High-level Hospital Construction Fund.

Author information

Author notes

  1. Zhou Liu and Bingyu Yao contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 113 Baohe Avenue, 518116, Shenzhen, China

    Zhou Liu, Jie Wen, Meng Wang, Ya Ren & Dehong Luo

  2. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, China

    Bingyu Yao, Zhanli Hu, Ye Li, Dong Liang, Xin Liu, Hairong Zheng & Na Zhang

  3. College of Computer and Information Engineering, Xiamen University of Technology, 600 Ligong Road, Xiamen, China

    Bingyu Yao & Yuming Chen

Authors
  1. Zhou Liu
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  2. Bingyu Yao
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Corresponding authors

Correspondence to Dehong Luo or Na Zhang.

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Guarantor

The scientific guarantor of this publication is Na Zhang and Dehong Luo.

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

Yuming Chen kindly provided statistical advice for this manuscript.

Informed consent

Due to the retrospective study design, written informed consent from the subjects was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The manuscript or part of it has neither been published nor is currently under consideration for publication by any other journal.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Liu, Z., Yao, B., Wen, J. et al. Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions. Eur Radiol 34, 182–192 (2024). https://doi.org/10.1007/s00330-023-10102-7

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