A Multicenter Observer Performance Study of 3D JPEG2000 Compression of Thin-Slice CT

The goal of this study was to determine the compression level at which 3D JPEG2000 compression of thin-slice CTs of the chest and abdomen–pelvis becomes visually perceptible. A secondary goal was to determine if residents in training and non-physicians are substantially different from experienced radiologists in their perception of compression-related changes. This study used multidetector computed tomography 3D datasets with 0.625–1-mm thickness slices of standard chest, abdomen, or pelvis, clipped to 12 bits. The Kakadu v5.2 JPEG2000 compression algorithm was used to compress and decompress the 80 examinations creating four sets of images: lossless, 1.5 bpp (8:1), 1 bpp (12:1), and 0.75 bpp (16:1). Two randomly selected slices from each examination were shown to observers using a flicker mode paradigm in which observers rapidly toggled between two images, the original and a compressed version, with the task of deciding whether differences between them could be detected. Six staff radiologists, four residents, and six PhDs experienced in medical imaging (from three institutions) served as observers. Overall, 77.46% of observers detected differences at 8:1, 94.75% at 12:1, and 98.59% at 16:1 compression levels. Across all compression levels, the staff radiologists noted differences 64.70% of the time, the resident’s detected differences 71.91% of the time, and the PhDs detected differences 69.95% of the time. Even mild compression is perceptible with current technology. The ability to detect differences does not equate to diagnostic differences, although perception of compression artifacts could affect diagnostic decision making and diagnostic workflow

Retrieval of Radiology Reports Citing Critical Findings with Disease-Specific Customization

Background: Communication of critical results from diagnostic procedures between caregivers is a Joint Commission national patient safety goal. Evaluating critical result communication often requires manual analysis of voluminous data, especially when reviewing unstructured textual results of radiologic findings. Information retrieval (IR) tools can facilitate this process by enabling automated retrieval of radiology reports that cite critical imaging findings. However, IR tools that have been developed for one disease or imaging modality often need substantial reconfiguration before they can be utilized for another disease entity. Purpose: This paper: 1) describes the process of customizing two Natural Language Processing (NLP) and Information Retrieval/Extraction applications – an open-source toolkit, A Nearly New Information Extraction system (ANNIE); and an application developed in-house, Information for Searching Content with an Ontology-Utilizing Toolkit (iSCOUT) – to illustrate the varying levels of customization required for different disease entities and; 2) evaluates each application’s performance in identifying and retrieving radiology reports citing critical imaging findings for three distinct diseases, pulmonary nodule, pneumothorax, and pulmonary embolus. Results: Both applications can be utilized for retrieval. iSCOUT and ANNIE had precision values between 0.90-0.98 and recall values between 0.79 and 0.94. ANNIE had consistently higher precision but required more customization. Conclusion: Understanding the customizations involved in utilizing NLP applications for various diseases will enable users to select the most suitable tool for specific tasks

Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks

The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies

Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for Thirteen Cancer Types

Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites