The diagnostic value of contrast-enhanced ultrasound combined with clinicopathological features in microinvasive ductal carcinoma in situ.
Ductal carcinoma in situ with microinvasion (DCISM) represents 1% of all breast cancer cases and is arguably a more aggressive subtype of ductal carcinoma in situ (DCIS). Preoperative evaluation of DCISM usually relies on core needle biopsy, and non-invasive evaluation methods are relatively limited. This study aims to explore the features of conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) in DCISM and to analyze the US and clinicopathological predictors of infiltrating components.
A retrospective collection of US, CEUS, and clinicopathologic data for DCIS and DCISM lesions was conducted from January 1, 2019 to June 30, 2022. The Breast Imaging Reporting and Data System (BI-RADS) criteria were used to evaluate breast lesions. On CEUS, the imaging features were scored using a 5-point scoring system to re-rate the BI-RADS category indicated by conventional US features. The pathological diagnosis served as the gold standard. Histopathologic features included comedo-type necrosis and pathological grade, while biomarkers included estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the Ki-67 index. A logistic regression analysis was performed to identify the independent risk factors for DCISM. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC).
A total of 89 women were included in the study. Of these, 66 had a pathologic diagnosis of DCIS (66 lesions, ranging in size from 0.6 to 4.9 cm), and 23 had a pathologic diagnosis of DCISM (23 lesions, ranging in size from 0.7 to 4.2 cm). Three features on conventional US (tumor size, margin, and calcification) and three enhancement features on CEUS (enhancement margin, enhancement mode, and enhancement scope) were found to be significantly different between the DCIS and DCISM lesions (P=0.03, P=0.04, P=0.02, P=0.03, P=0.03, P=0.007, respectively). Patients with DCISM were more likely to have a higher pathological grade, ER negativity, PR negativity, HER2 positivity, and a higher Ki-67 index than patients with DCIS (P<0.001, P=0.042, P=0.03, P=0.009, P=0.05, respectively). A multivariate logistic regression analysis further showed that only an enlarged enhancement scope and pathological grade were associated with DCISM. The sensitivity and specificity of this predictive model were 87.0% and 81.8%, respectively (AUC =0.89). The absence of calcifications, non-mass lesions, lack of vascularity, and the non-enlarged scope can lead to misdiagnosis of DCIS and DCISM.
Understanding the CEUS and clinicopathologic features of DCISM lesions may alert clinicians to the possibility of microinvasion and guide appropriate management.
Jiang Y
，Li JK
，Huang SS
，Li SY
，Niu RL
，Fu NQ
，Wang ZL
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Diagnostic performance of the Kaiser score for contrast-enhanced mammography and magnetic resonance imaging in breast masses: A Comparative Study.
The Kaiser score (KS) is a simple and intuitive machine-learning derived decision rule for characterizing breast lesions in a clinical setting and screening for breast cancer. The present study aims to investigate the applicability of the KS for contrast-enhanced mammography (CEM) in breast masses, and to compare its diagnostic accuracy with magnetic resonance imaging (MRI). CEM may provide an alternative option for patients with breast masses, especially for those with MRI contraindications.
Two hundred and seventy-five patients with breast enhanced masses were included in the study from May 2019 to September 2022. Patients were further divided into benign and malignant groups based on pathological diagnosis. The CEM and MRI imaging characteristics of these two groups were analyzed statistically. The paired chi-square and Cohen's kappa coefficient (κ) analysis were used to compare imaging characteristics between CEM and MRI. The Breast Imaging Reporting and Data System (BI-RADS) and KS for CEM and MRI were evaluated based on imaging characteristics. The diagnostic performance of BI-RADS and KS for CEM and MRI was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test.
The imaging characteristics of root sign, time-signal intensity curve (TIC/mTIC), margin, internal enhancement pattern (IEP), edema, apparent diffusion coefficient (ADC) values, and suspicious malignant microcalcifications showed significant differences between benign and malignant lesions (all p ≤ 0.011). The detection rate of root sign and margin showed substantial agreement between CEM and MRI (κ = 0.656, κ = 0.640), but IEP, TIC/mTIC, and edema showed poor agreement (κ = 0.380, κ = 0.320, κ = 0.324). For all lesion analyses, the area under the curves (AUCs) of the KS (0.897 ∼ 0.932) were higher than that of BI-RADS (0.691) in CEM (all p < 0.001). The AUC of KS (calcification)-CEM (0.932) was higher than those of both KS-CEM and KS (edema)-CEM (0.897 and 0.899) (all p < 0.001). For subgroup analyses, the AUCs of the KS (0.875 ∼ 0.876) were higher than that of BI-RADS (0.740) in MRI (all p < 0.001). The AUCs of KS-MRI (0.876) and KS (ADC)-MRI (0.875) were similar to those of KS-CEM (0.878) and KS (edema)-CEM (0.870) (all p > 0.100). The AUC of KS (calcification)-CEM (0.934) was slightly higher than those of both KS-MRI (0.876) and KS (ADC)-MRI (0.875), but no significant difference was observed (p = 0.051; p = 0.071).
The KS for CEM provided high diagnostic accuracy in distinguishing breast masses, comparable to that of MRI. The application of KS (calcification)-CEM combined with suspicious malignant microcalcifications can improve diagnostic efficiency with an AUC of 0.932 ∼ 0.934. However, edema did not significantly improve performance when using the KS for CEM.
Hua B
，Yang G
，Wang Y
，Chen J
，Rong X
，Yuan T
，Quan G
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Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics had been used to evaluate lymphovascular invasion (LVI) in patients with breast cancer. However, no studies had explored the associations between features from delayed phase as well as multiphases DCE-MRI and the LVI status. Thus, we aimed to develop an efficient nomogram based on multiphases DCE-MRI to predict the LVI status in invasive (IBC) breast cancer patients.
A retrospective analysis was conducted on preoperative clinical, pathological, and DCE-MRI data of 173 breast cancer patients. All patients were randomly assigned into training set (n=121) and validation set (n=52) in 7:3 ratio. The clinical, pathologic, and conventional MRI characteristics were then subjected to univariate and multivariate logistic regression analysis, and the clinical risk factors with P < 0.05 in the multivariate logistic regression were used to build clinical models. Different single-phase models (early phase, peak phase, and terminal phase), as well as a multiphases model integrating radiomics features from multiple phases, were established. Furthermore, a preoperative radiomics nomogram model was constructed by combining the rad-score of the multiphases model with clinicopathologic independent risk factors. Finally, the performance of the multiphases model, clinical model, and rad-score was compared using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and decision curve analysis (DCA). The clinical utility of the rad-score was evaluated using calibration curves, and Delong test was used to compare the differences in AUC values among the different models.
The axillary lymph nodes (ALN) status and Ki-67 had been identified as clinicopathologic independent predictors and a clinical model had been constructed. Image features that were extracted from the terminal phase of the DCE-MRI exhibited notably superior predictive performances compared to features from the other single phases. Particularly, in the multiphases model, terminal phase features were identified as potentially providing more predictive information. Among the nine features that were found to be associated with LVI in the multiphase model, one was derived from the early phase, two from the peak phase, and six from the terminal phase, indicating that terminal phase features contributed significantly more information towards predicting LVI. Evaluation of the nomogram performance revealed promising results in both the training set (AUCs: clinical model vs. multiphase model vs. nomogram=0.734 vs. 0.840 vs. 0.876) and the validation set (AUCs: clinical model vs. multiphase model vs. nomogram=0.765 vs. 0.753 vs. 0.832).
The DCE-MRI-based radiomics model demonstrated utility in predicting LVI status, features of the terminal phase offered more valuable information particularly. The preoperative radiomics nomogram enhanced the diagnostic capability of identifying LVI status in IBC patients, and might aid clinicians in making personalized treatment decisions.
Ma Q
，Lu X
，Chen Q
，Gong H
，Lei J
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ResearchGate
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