Toward Precision
Cancer Surveillance

Dec
5

The acquisition of diagnostic, treatment, and outcomes information on cancer cases for population-based cancer surveillance currently involves a tremendous amount of manual data abstraction and information processing by expert staff. A majority (estimated 65%) of clinical data elements that are needed to characterize cancer patients come from unstructured sources (e.g. pathology reports, radiology notes, treatment summaries, clinical visit notes). Many hospital-based cancer registries that abstract and report cancer cases to central cancer registries at the state level rely on manual data abstraction from document-based medical records. Central cancer registry staff also perform manual data processing to find additional cases, consolidate records, and fix data errors and gaps. Manual processes impose inherent limitations on the volume and types of information that registries can collect. Furthermore, with the increasing complexity of cancer care, staff may not have the resources to... Read more

natural language processingNLPcomputational linguisticscancer surveillancedata abstraction
Oct
18

NCI is collaborating with the U.S. Department of Energy (DOE) as part of the inter-agency coordination activities defined in the National Strategic Computing Initiative (NSCI) Presidential Order (July 29, 2015) and announced during Vice President Biden’s Cancer Moonshot Summit on June 29, 2016. The NCI-DOE collaboration has initiated three pilot efforts that will simultaneously impact the future of cancer research and guide future advances in scientific computing. These pilots will characterize and help overcome key precision oncology challenges at the molecular, patient, and population levels during the next three years.

DOEcollaborationpilotcancer surveillancenatural language processingalgorithmslinkagesmodeling