Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network.

Prostate cancer primarily arises from the glandular epithelium. Histomophometric techniques have been used to assess the glandular epithelium in automated detection and classification pipelines; however, they are often rigid in their implementation, and their performance suffers on large datasets where variation in staining, imaging, and preparation is difficult to control. The purpose of this study is to quantify performance of a pixelwise segmentation algorithm that was trained using different combinations of weak and strong stroma, epithelium, and lumen labels in a prostate histology dataset.

Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer.

Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized.

Assessment of Consistency Between Peer-Reviewed Publications and Clinical Trial Registries.

Importance: Clinical trial registries are intended to increase clinical research transparency by nonselectively identifying and documenting clinical trial designs and outcomes. Inconsistencies in reported data undermine the utility of such registries and have previously been noted in general medical literature.

Objective: To assess whether inconsistencies in reported data exist between ophthalmic literature and clinical trial registries.

Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer.

Self-Assessment Feedback Form Improves Quality of Out-of-Hospital CPR.

Various continuous quality improvement (CQI) approaches have been used to improve quality of cardiopulmonary resuscitation (CPR) delivered at the scene of out-of-hospital cardiac arrest. We evaluated a post-event, self-assessment, CQI feedback form to determine its impact on delivery of CPR quality metrics. This before/after retrospective review evaluated data from a CQI program in a midsized urban emergency medical services (EMS) system using CPR quality metrics captured by Zoll Medical Inc.