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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.
RESUMO
Subcortical vascular mild cognitive impairment (svMCI) is a common prodromal stage of vascular dementia. Although mounting evidence has suggested abnormalities in several single brain network metrics, few studies have explored the consistency between functional and structural connectivity networks in svMCI. Here, we constructed such networks using resting-state fMRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls. The functional networks were then parcellated into topological modules, corresponding to several well-defined functional domains. The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level (whole brain and modular level). We found no significant intergroup differences in the functional–structural coupling within the whole brain; however, there was significantly increased functional–structural coupling within the dorsal attention module and decreased functional–structural coupling within the ventral attention module in the svMCI group. In addition, the svMCI patients demonstrated decreased intramodular connectivity strength in the visual, somatomotor, and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network, mainly linking the visual, somatomotor, dorsal attention, ventral attention, and frontoparietal control modules. There was no significant correlation between the altered module-level functional–structural coupling and cognitive performance in patients with svMCI. These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.