Hendersonhay4224

To compare the diagnostic value of high frame rate contrast-enhanced ultrasound (H-CEUS) in distinguishing gallbladder adenomas from cholesterol polyp lesions with that of CEUS.

This study enrolled 94 patients with gallbladder polyp lesions (GPLs) who underwent laparoscopic cholecystectomy. CEUS and H-CEUS were performed before surgery. The perfusion features of GPLs and the final diagnosis as determined by both technologies were compared.

There were differences in vascular types between gallbladder adenomas and cholesterol polyp lesions observed on H-CEUS (p < 0.05), while there were no differences in vascular types between gallbladder adenomas and cholesterol polyp lesions observed on CEUS (p > 0.05). In the cholesterol polyp lesion group, there were no differences in vascular types between CEUS and H-CEUS (p > 0.05), while the vascular types were different between CEUS and H-CEUS in the gallbladder adenoma group (p < 0.05). The diagnostic value of H-CEUS in distinguishing gallbladder adenUS helps patients with gallbladder polyp lesions to choose the appropriate treatment means.

To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer.

A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram's performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor.

The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-deredicting of SLN burden in patients with early-stage breast cancer.

• Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer. • Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors. • Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.

To evaluate whether the change in computed tomography pulmonary angiography (CTPA) metrics after balloon pulmonary angioplasty (BPA) can predict treatment effect in chronic thromboembolic pulmonary hypertension (CTEPH) patients.

This study included 82 CTEPH patients who underwent both CTPA and right heart catheterization (RHC) before and at the scheduled time of 6 months after BPA. The diameters of the main pulmonary artery (dPA), ascending aorta (dAA), right atrium (dRA), right ventricular free wall thickness (dRVW), and right and left ventricles (dRV, dLV) were measured on CTPA. The correlation of the New York Heart Association functional class (NYHA FC), 6-minute walking distance (6MWD), brain natriuretic peptide (BNP) level, and calculated CT metrics with a decrease in mean pulmonary artery pressure (ΔmPAP) using RHC (used as the reference for BPA effect) was investigated. Using multiple regression analysis, independent variables were also identified.

In univariate analysis, clinical indicators (NYHre after balloon pulmonary angioplasty in CTEPH patients was significantly correlated with the clinical indices improvement and CTPA parameter decrease. • The decreased diameter of the main pulmonary artery and the decreased diameter of the right atrium on CTPA were independent predictors of mean pulmonary artery pressure reduction.

• Radiology has developed into a central and important part of patient care.• A combination of technological developments, increasing workload and radiologists' behaviour run the risk of diminishing the visibility of radiologists to referrers and patientsRadiology has developed into a central and important part of patient care.• It is vital for the successful future of radiology that we remain conscious of the need to maintain visibility of who we are and what we contribute to patient care.

• Radiology has developed into a central and important part of patient care.• A combination of technological developments, increasing workload and radiologists' behaviour run the risk of diminishing the visibility of radiologists to referrers and patientsRadiology has developed into a central and important part of patient care.• It is vital for the successful future of radiology that we remain conscious of the need to maintain visibility of who we are and what we contribute to patient care.

To develop and compare several machine learning models to predict occult cervical lymph node (LN) metastasis in early-stage oral tongue squamous cell cancer (OTSCC) from preoperative MRI texture features.

We retrospectively enrolled 116 patients with early-stage OTSCC (cT1-2N0) who had been surgically treated by tumor excision and elective neck dissection (END). For each patient, we extracted 86 texture features from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. Dimension reduction was performed in three consecutive steps reproducibility analysis, collinearity analysis, and information gain algorithm. Models were created using six machine learning methods, including logistic regression (LR), random forest (RF), naïve Bayes (NB), support vector machine (SVM), AdaBoost, and neural network (NN). Their performance was assessed using tenfold cross-validation.

Occult LN metastasis was pathologically detected in 42.2% (49/116) of the patients. No significant assoc predict occult cervical node metastasis in early-stage OTSCC with no evidence of node involvement on conventional images. • Six texture features from T2WI and ceT1WI of preoperative MRI were selected to construct the predictive model. selleck chemicals • After comparing six machine learning methods, naïve Bayes (NB) achieved the best performance by correctly identifying the node status in 74.1% of the patients, using tenfold cross-validation.