Imaging of Burkitt lymphoma in pediatric patients

The imaging procedures utilized at presentation in the diagnostic work-up of 19 children with Burkitt lymphoma were reviewed. The distribution of disease was compared to other tumors of childhood so that the most valuable modalities could be identified. Burkitt lymphoma is a rapidly growing tumor in the child, making it essential to suggest the diagnosis as quickly as possible so that biopsy and treatment can be instigated. The primary area of involvement was abdominal (15 of 19), gastrointestinal, intraperitoneal adenopathy, hepatic and pancreatic without retroperitoneal adenopathy. Pleural effusions were common without hilar and mediastinal adenopathy. This is in contrast to other tumors of childhood where mediastinal and hilar disease in the chest and retroperitoneal node involvement in the abdomen are common. Thus sonography is an excellent imaging modality, easily identifying the extent of the disease and so suggesting the diagnosis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46683/1/247_2006_Article_BF02388718.pd

Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases