Predictive Model for the Diagnosis of Benign/Malignant Complex Cystic and Solid Breast Nodules.

PURPOSE: To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs). METHODS: A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018-2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (p = 0.03), cystic fluid transmission (p = 0.02), longitudinal diameter (p < 0.001), and age (p = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (M is the malignancy score, e = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model. CONCLUSIONS: A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images.

This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.

Intracranial large artery embolism due to carotid thrombosis caused by a neck massager: A case report.

BACKGROUND: There are few reported cases of intracranial large artery embolism due to carotid thrombosis caused by a neck massager. Herein we report such a case. CASE SUMMARY: A 49-year-old woman presented with left limb weakness and dysarthria after a history of neck massage for 1 mo. Neurological examination showed left central facial paralysis and left hemiparesis with a National Institutes of Health Stroke Scale score of 12. Brain magnetic resonance imaging revealed restricted diffusion on diffusion-weighted imaging in the right parietal and temporal lobes. Computed tomography angiography (CTA) indicated M3 segment embolism of the right middle cerebral artery. Neck CTA revealed thrombosis of the bilateral common carotid arteries. Carotid ultrasound showed thrombosis in the bilateral common carotid arteries (approximately 2 cm below the proximal end of the carotid sinus), and contrast-enhanced ultrasound did not suggest enhancement. No hypertension, diabetes, heart disease, vasculitis, or thrombophilia was found after admission. After 1 wk of treatment with aspirin 200 mg and atorvastatin 40 mg, a carotid ultrasound reexamination showed that the thrombosis had significantly reduced. CONCLUSION: Neck massager may cause carotid artery thrombosis.

Plasma LUNX mRNA, a non-invasive specific biomarker for diagnosis and prognostic prediction of non-small cell lung cancer.

Lung cancer is the most common cancer worldwide. However, no specific biomarker has been found in diagnosis and evaluation of therapeutic efficacy for lung cancer. The human lung-specific X protein gene (LUNX) was recently identified with a feature of lung tissue specificity. We applied the fluorescent quantitative polymerase chain reaction method to examine LUNX mRNA in plasma and peripheral blood mononuclear cells (PBMC) in patients with non-small cell lung cancer (NSCLC), benign lung diseases, extrapulmonary tumors, and healthy subjects. The results showed that LUNX mRNA in both of plasma and PBMC were significantly higher in lung cancer patients compared to other groups. In plasma, there were higher sensitivity and negative predictive value of LUNX mRNA than in PBMC. Patients with III~IV stages of lung cancer had more LUNX mRNA in plasma than the early stage of lung cancer sufferers. After a period of therapy, significant reductions of plasma LUNX mRNA in patients with I and II stages of lung cancer were found. Levels of plasma LUNX mRNA in patients who had succeeded to respond to therapy decreased compared to prior treatment. On the other hand, the post-treatment level was obviously increased in patients that had failed to respond to therapy. Patients with negative plasma LUNX mRNA after therapy displayed a favorable prognosis and survival rate. These preliminary data suggested that cell-free LUNX mRNA in plasma as a non-invasive biomarker, is superior to peripheral intracellular LUNX mRNA, and plays a critical role in specific diagnosis and prognostic prediction of non-small cell lung cancer.