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Published on: December 15, 2014
Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture
Wen-Juan Xu1, Bing-Jie Zheng1, Jun Lu1
1Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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 (Ktrans, Kep and Ve) 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 Kep_Range (p = 0.032) and Ve_SumVariance (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 Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (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.
Insights
Dynamic contrast-enhanced MRI (DCE-MRI) texture analysis can identify triple-negative breast cancer (TNBC) and predict androgen receptor (AR) expression. This imaging approach aids in personalizing TNBC treatment strategies.
Area of Science:
- Radiology
- Oncology
- Medical Imaging Analysis
Background:
- Triple-negative breast cancer (TNBC) is aggressive with limited therapeutic options.
- Androgen receptor (AR) is a potential therapeutic target in TNBC.
- Intratumor heterogeneity impacts TNBC prognosis and treatment response.
Purpose of the Study:
- To discriminate TNBC from non-TNBC using DCE-MRI.
- To identify AR expression in TNBC using DCE-MRI.
- To assess intratumor heterogeneity via pharmacokinetic and texture analysis.
Main Methods:
- Retrospective analysis of 99 breast cancer patients (36 TNBC, 63 non-TNBC) with pre-operative DCE-MRI.
- Calculation of pharmacokinetic (Ktrans, Kep, Ve) and texture parameters.
- Logistic regression and ROC analysis to build predictive models for TNBC and AR expression.
Main Results:
- Significant differences in Kep_Range and Ve_SumVariance between TNBC and non-TNBC.
- A combined DCE-MRI model achieved 73.5% AUC for TNBC identification.
- Specific texture features (Kep_Compactness2, Kep_SphericalDisproportion, Ve_GlcmEntropy) differentiated AR+ from AR- TNBC.
Conclusions:
- DCE-MRI histogram and texture analysis can identify TNBC.
- Specific imaging features may predict AR expression in TNBC.
- This approach supports personalized treatment strategies for TNBC patients.
