Value of cone-beam breast CT in differentiating benign from malignant dense breast masses
10.3760/cma.j.cn112149-20201120-01252
- VernacularTitle:锥光束乳腺CT鉴别致密型乳腺内肿块良恶性的价值
- Author:
Yafei WANG
1
;
Yue MA
;
Yueqiang ZHU
;
Aidi LIU
;
Juanwei MA
;
Lu YIN
;
Zhaoxiang YE
Author Information
1. 天津医科大学肿瘤医院放射科 国家肿瘤临床医学研究中心 天津市“肿瘤防治”重点实验室 天津市恶性肿瘤临床医学研究中心 乳腺癌防治教育部重点实验室 300060
- Keywords:
Breast neoplasms;
Cone-beam computed tomography;
Diagnosis, differential;
Regression model
- From:
Chinese Journal of Radiology
2021;55(9):961-967
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of logistic regression model based on the features of cone-beam breast CT (CBBCT) for the identification of benign and malignant masses in dense breast.Methods:The data of 106 patients (130 masses) with dense breast who underwent contrast-enhanced CBBCT examination and obtained pathological results from May 2011 to August 2020 were retrospectively analyzed as the training set. From August 2020, the data of 49 patients (54 masses) who met the same criteria were prospectively and consecutively collected and used as the validation set. Taking pathological results as the gold standard, the training set was divided into benign and malignant groups. The t-test, χ 2 test and Fisher′s exact test were used to compare the differences in CBBCT image characteristics between the two groups in the training set. A binary logistic regression model was established by multivariate analysis. ROC curves were used to assess the diagnostic efficacy of the model as a whole in the training and validation sets and the diagnostic efficacy of each feature in the model, and the cut-off value of the intensity (ΔCT) value was determined. The H-L method was used to test the goodness of fit of the model. Decision curve analysis (DCA) was drawn to validate the clinical power of the model. Results:Univariate analysis showed that the breast parenchymal background enhancement (BPE), shape, margin, lobulation, spiculation, density, calcifications, ΔCT value, enhancement pattern, non-mass enhancement, ipsilateral increased vascularity (IIV), and peripheral vascular signs had statistical difference between benign group and malignant group ( P<0.05). BPE, margin, ΔCT value and IIV were included in the multivariate analysis, the equation was logit( P′)=-8.510+0.830×BPE+0.822×margin+1.919× ΔCT+1.896 × IIV. The are a under curve of the model in the training set was 0.879 ( P<0.001) and in the validation set was 0.851 ( P=0.001). The are a under curve of BPE, margin, ΔCT value, and IIV in the diagnosis of malignant mass were 0.645, 0.711, 0.712, 0.775 (all P<0.05); the best cut-off value of ΔCT was 50.38 HU. The fit of this model was good ( P = 0.776). The DCA curve showed that when the risk threshold was 0.05-0.97, the net benefit rate was>0, and this model had some clinical value. Conclusion:The logistic regression model based on the features of CBBCT is helpful to distinguish benign and malignant masses in dense breasts.
