The apocrine morphology of the breast is observed in a broad pathological spectrum, ranging from benign cysts to invasive carcinomas. However, the number of clinical research investigating malignant apocrine lesions is limited. This study retrospectively reviewed the data of patients with malignant apocrine lesions admitted in a tertiary center between January 2004 and December 2021, based on the radiology-pathology correlation and the recent advances in their status to enhance the therapeutic implications of androgen receptor (AR). Among the 37 patients with lesions, 27 (73.0%) had triple-negative subtypes with predominant AR expression. The radiological features of malignant apocrine lesions did not differ from those of typical invasive ductal carcinoma or ductal carcinoma in situ. This study demonstrated that knowledge on the imaging features of malignant apocrine lesions and their histological basis could enhance the adoption of new targeted therapies in patients with this particular type of breast cancer.
Purpose This study aimed to identify independent predictors of favorable outcomes associated with emergent carotid artery stenting (CAS) in patients with acute anterior circulation stroke. Materials and Methods This study included 93 patients with acute stroke who underwent emergent CAS to treat stenoocclusive lesions in the cervical internal carotid artery (ICA) within 6 hours of the onset of the associated symptoms. Data were compared between patients with and without favorable outcomes. The independent predictors of a favorable outcome were determined via logistic regression analysis (modified Rankin Scale 0–2 at 90 days). Results Intracranial tandem occlusion was noted in 81.7% of patients (76/93) among which (76/93), 55 of whom underwent intracranial recanalization therapy. Intracranial reperfusion was successful in 74.2% (69/93) and favorable outcomes were noted in 51.6% of patients (48/93). The mortality rate was 6.5% (6/93). In logistic regression analysis, diffusion-weighted imaging-Alberta Stroke Program Early CT Score [odds ratio (OR), 1.487; 95% confidence interval (CI), 1.018–2.173, p = 0.04], successful reperfusion (OR, 5.199; 95% CI, 1.566–17.265, p = 0.007), and parenchymal hemorrhage (OR, 0.042; 95% CI, 0.003–0.522, p = 0.014) were independently associated with a favorable outcome. Conclusion Baseline infarct size, reperfusion status, and parenchymal hemorrhage were independent predictors of favorable outcomes after emergent CAS to treat stenoocclusive lesions in the cervical ICA in patients with acute anterior circulation stroke.
Objective To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.
The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.
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