Chronic rhinosinusitis with nasal polyps (CRSwNP) symptom verbal response scales: content validity testing for use in adults with CRSwNP.

Background: PRO measures of symptoms in clinical trials have historically utilized visual and numerical scales but verbal descriptors may make it easier for patients to clearly differentiate between response options. This study assessed content validity and meaningful change in five verbal response scales (VRSs) used to assess chronic rhinosinusitis with nasal polyp (CRSwNP) symptom severity.

Methodology: This qualitative, semi-structured interview study recruited adults from the US, Germany, and China with confirmed moderate-to-severe CRSwNP.

Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging.

Purpose: To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging.

Materials And Methods: Using two large publicly available radiology datasets (Society for Imaging Informatics in Medicine-American College of Radiology Pneumothorax Segmentation dataset and Radiological Society of North America Pneumonia Detection Challenge dataset), the performance of eight commonly used saliency map techniques were quantified in regard to localization utility (segmentation and detection), sensitivity to model weight randomization, repeatability, and reproducibility. Their performances versus baseline methods and localization network architectures were compared, using area under the precision-recall curve (AUPRC) and structural similarity index measure (SSIM) as metrics.

Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density.

Objective: We developed deep learning algorithms to automatically assess BI-RADS breast density.

Methods: Using a large multi-institution patient cohort of 108,230 digital screening mammograms from the Digital Mammographic Imaging Screening Trial, we investigated the effect of data, model, and training parameters on overall model performance and provided crowdsourcing evaluation from the attendees of the ACR 2019 Annual Meeting.

Results: Our best-performing algorithm achieved good agreement with radiologists who were qualified interpreters of mammograms, with a four-class κ of 0.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

Background: Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

Background: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs.

Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging.

Background: The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria.

Is nifedipine a suitable first-line treatment for essential hypertension in general practice?

Nifedipine has been used in hypertension, mainly as a third-line drug for rapid blood pressure reduction, for over 10 years. More recently it has been shown to be effective and safe in reducing mild to moderate blood pressure when used alone in a slow release formulation. A placebo-controlled study investigating the efficacy and safety of nifedipine in comparison with cyclopenthiazide-potassium has now been carried out by 4 general practitioners in the Glasgow area.