No abstract

Human metapneumovirus (HMPV) shares clinical and epidemiological characteristics with well-known respiratory syncytial virus (RSV). The aim of this study was to investigate the clinical and epidemiological differences between HMPV- and RSV-induced wheezing illnesses. A total of 1,008 nasopharyngeal aspirate specimens was collected from 1,008 pediatric patients hospitalized with acute respiratory tract infection at Inje University Sanggye Paik Hospital from December 2003 to April 2008, and tested for seven common respiratory viruses. Conditions classified as wheezing illness were bronchiolitis, reactive airways disease, and bronchial asthma. HMPV caused a significantly lower proportion of wheezing illness when compared to RSV (48.1% vs. 82.2%, P<0.05). HMPV-induced wheezing illness occurred predominantly in older patients when compared to RSV patients (P<0.001). RSV infections peaked in the fall and winter followed by peaks of HMPV infection in winter and spring. Eosinophil counts were significantly higher (P<0.01) in RSV patients when compared to HMPV patients. These results show that human metapneumovirus patients exhibit several different clinical and epidemiological characteristics, such as higher proportion of wheezing illness, age and seasonal incidence, and eosinophil counts, when compared to RSV patients.

F-FDG PET was found to be useful for predicting the pCR after NCRT in patients with locally advanced rectal cancer. Among various PET parameters, SUVmax normalized to liver uptake after NCRT was the best predictor of the pCR.

No author has a financial or proprietary interest in any material or method mentioned.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26–66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677–0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722–0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0:PET0 = 0.652:0.886, SUV1:PET1 = 0.687:0.980, and ADC1:MRI1 = 0.537:0.701), except for ADC0 (ADC0:MRI0 = 0.703:0.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.