A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data

Wei Li; Shanshan Wang; Weidong Xie; Chaolu Feng

doi:10.21037/qims-22-1230

A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data

Quant Imaging Med Surg. 2023 Jul 1;13(7):4429-4446. doi: 10.21037/qims-22-1230. Epub 2023 May 26.

Authors

Wei Li^{1

2}, Shanshan Wang², Weidong Xie², Chaolu Feng^{1

2}

Affiliations

¹ Key Laboratory of Intelligent Computing in Medical Image (MIIC), Northeastern University, Shenyang, China.
² School of Computer Science and Engineering, Northeastern University, Shenyang, China.

Abstract

Background: Breast cancer is a major cause of mortality among women worldwide. Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) is a good imaging technique that can show temporal information about the kinetics of the contrast agent in suspicious breast lesions as well as acceptable spatial resolution. Computer-aided detection systems assist in the detection of lesions through medical image processing techniques combined with computerized analysis and calculation, which in turn helps radiologists recognize molecular subtypes of breast lesions that will be beneficial for better treatment plan decisions.

Methods: In this paper, a computer-aided diagnosis method is proposed to automatically locate breast cancer lesions and identify molecular subtypes of breast cancer with heterogeneity analysis from radiomics data. A fast region-based convolutional network (Faster R-CNN) framework is first applied to images to detect breast cancer lesions. Then, the heterogeneous regions of every breast cancer lesion are extracted. Based on the multiple visual and kinetic radiomics features extracted from the heterogeneous regions, a temporal bag of visual word model is proposed, which takes into account the dynamic characteristics of both lesion and heterogeneous regions in images over time. The recognition task of molecular subtypes of breast lesions is realized based on a stacking classification model.

Results: At the genetic level, breast cancer is divided into four molecular subtypes, namely, luminal epithelial type A (Luminal A), luminal epithelial type B (Luminal B), HER-2 overexpression and basal cell type. The experimental results show that the precision of the four subtypes is 93%, 94%, 83%, 86%; the recall is 96%, 80%, 91%, 94%; and the F1-score is 95%, 86%, 87%.

Conclusions: The experimental results denote the influence of heterogeneous regions on the recognition task. The DCE-MRI-based approach to identify molecular typing of breast cancer for noninvasive diagnosis will contribute to the development of breast cancer treatment, improved outcomes and reduced mortality.

Keywords: Breast cancer; dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI); fast region-based convolutional network (Faster R-CNN); molecular subtypes; quantitative heterogeneity analysis.