Identification of Triple-negative Breast Cancer and Androgen Receptor Expression Based on Histogram and Texture Analysis of Dynamic Contrast-enhanced MRI

Overview

Journal BMC Med Imaging

Publisher Biomed Central

Specialty Radiology

Date 2023 Jun 1

PMID 37264313

Authors

Wen-Juan Xu

Bing-Jie Zheng

Jun Lu

Si-Yun Liu

Hai-Liang Li

Affiliations

Soon will be listed here.

Abstract

Background: Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC.

Methods: This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (K, K and V) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived.

Results: The binary logistic regression analysis revealed that K (p = 0.032) and V (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of K (p = 0.049), K (p = 0.049), and V (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group.

Conclusion: Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Citing Articles

Advancements and challenges in triple-negative breast cancer: a comprehensive review of therapeutic and diagnostic strategies.

Xiong N, Wu H, Yu Z Front Oncol. 2024; 14:1405491.

PMID: 38863622 PMC: 11165151. DOI: 10.3389/fonc.2024.1405491.

References

Li Z, Ai T, Hu Y, Yan X, Nickel M, Xu X . Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique. J Magn Reson Imaging. 2017; 47(1):91-96. DOI: 10.1002/jmri.25762. View

Choi Y, Kim S, Youn I, Kang B, Park W, Lee A . Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype. PLoS One. 2017; 12(5):e0177903. PMC: 5436838. DOI: 10.1371/journal.pone.0177903. View

Goldhirsch A, Winer E, Coates A, Gelber R, Piccart-Gebhart M, Thurlimann B . Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013; 24(9):2206-23. PMC: 3755334. DOI: 10.1093/annonc/mdt303. View

Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View

Jia Z, Geng D, Liu Y, Chen X, Zhang J . Microvascular permeability of brain astrocytoma with contrast-enhanced magnetic resonance imaging: correlation analysis with histopathologic grade. Chin Med J (Engl). 2013; 126(10):1953-6. View

Huang Z, Tu X, Lin Q, Zhan Z, Tang L, Liu J . Intramammary edema of invasive breast cancers on MRI T-weighted fat suppression sequence: Correlation with molecular subtypes and clinical-pathologic prognostic factors. Clin Imaging. 2022; 83:87-92. DOI: 10.1016/j.clinimag.2021.12.023. View

Fan M, Li H, Wang S, Zheng B, Zhang J, Li L . Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One. 2017; 12(2):e0171683. PMC: 5293281. DOI: 10.1371/journal.pone.0171683. View

Hwang K, Kim Y, Kim J, Park J, Choi I, Hwang K . Influence of Androgen Receptor on the Prognosis of Breast Cancer. J Clin Med. 2020; 9(4). PMC: 7230528. DOI: 10.3390/jcm9041083. View

Yang Z, Chen X, Zhang T, Cheng F, Liao Y, Chen X . Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes. Front Oncol. 2021; 11:628824. PMC: 8481692. DOI: 10.3389/fonc.2021.628824. View

10.

Sun K, Zhu H, Chai W, Zhan Y, Nickel D, Grimm R . Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE. Eur Radiol. 2019; 30(1):57-65. DOI: 10.1007/s00330-019-06365-8. View

11.

Lin N, Vanderplas A, Hughes M, Theriault R, Edge S, Wong Y . Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network. Cancer. 2012; 118(22):5463-72. PMC: 3611659. DOI: 10.1002/cncr.27581. View

12.

Just N . Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer. 2014; 111(12):2205-13. PMC: 4264439. DOI: 10.1038/bjc.2014.512. View

13.

Kim S, Lee H, Kang B, Song B, Kim H, Lee H . Dynamic Contrast-Enhanced MRI Perfusion Parameters as Imaging Biomarkers of Angiogenesis. PLoS One. 2016; 11(12):e0168632. PMC: 5201289. DOI: 10.1371/journal.pone.0168632. View

14.

Wang H, Hu Y, Li H, Xie Y, Wang X, Wan W . Preliminary study on identification of estrogen receptor-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis. Gland Surg. 2020; 9(3):622-628. PMC: 7347815. DOI: 10.21037/gs.2020.04.01. View

15.

Di Leone A, Fragomeni S, Scardina L, Ionta L, Mule A, Magno S . Androgen receptor expression and outcome of neoadjuvant chemotherapy in triple-negative breast cancer. Eur Rev Med Pharmacol Sci. 2021; 25(4):1910-1915. DOI: 10.26355/eurrev_202102_25087. View

16.

Tang W, Jin Z, Zhang Y, Liang Y, Cheng Z, Chen L . Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer. Front Oncol. 2021; 10:611571. PMC: 7820903. DOI: 10.3389/fonc.2020.611571. View

17.

Lehmann B, Bauer J, Chen X, Sanders M, Chakravarthy A, Shyr Y . Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011; 121(7):2750-67. PMC: 3127435. DOI: 10.1172/JCI45014. View

18.

Burge C, Chang H, Apple S . Do the histologic features and results of breast cancer biomarker studies differ between core biopsy and surgical excision specimens?. Breast. 2005; 15(2):167-72. DOI: 10.1016/j.breast.2005.06.004. View

19.

Jahan N, Jones C, Layeequr Rahman R . Androgen receptor expression in breast cancer: Implications on prognosis and treatment, a brief review. Mol Cell Endocrinol. 2021; 531:111324. DOI: 10.1016/j.mce.2021.111324. View

20.

Chang R, Chen H, Chang Y, Huang C, Chen J, Lo C . Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI. Magn Reson Imaging. 2016; 34(6):809-819. DOI: 10.1016/j.mri.2016.03.001. View