Toward Precision
Cancer Surveillance

Nov
28

On July 12-14, 2017, the Surveillance, Epidemiology, and End Results Data Management System (SEER*DMS) 2017 Face-to-Face Meeting took place in Rockville, MD, at the National Cancer Institute’s (NCI) Shady Grove campus. The purpose of the meeting was to discuss enhancements to SEER*DMS and updates to SEER Program initiatives. SEER*DMS is a centrally designed data management system that allows registries to submit cancer data to the SEER Program. Developed in 2000 and first deployed in 2005, SEER*DMS also provides support for all core cancer registry functions, including importing, editing, linking, and consolidating data and producing reports. Since 2011, the SEER Program has taken steps to invest in and implement enhancements to SEER*DMS. In 2015, health informatics experts conducted a review of SEER*DMS and provided several recommendations for enhancement.

One recommendation included increasing transparency and clarity of changes and enhancements coming to SEER*DMS, and... Read more

SEERSEER*DMSMeetingData Management Systemregistryregistry functionsenhancementscancer datadata quality
Oct
20

Cancer is a major health burden, with an estimated 1,688,780 new diagnoses in 2017. Between the difficulty of receiving a cancer diagnosis and the complexity of cancer data, patients and loved ones may have difficulty interpreting cancer information. The National Cancer Institute’s (NCI) “Did You Know?” video series breaks down cancer statistics in layman’s terms and includes easy-to-understand charts and graphs.

The “Did You Know?” video series was created through a partnership between NCI’s Surveillance, Epidemiology, and End Results (SEER) Program and the NCI Office of Communications and Public Liaison (OCPL). In under 5 minutes, each “Did You Know?” video discusses a specific cancer type, such as bladder cancer, or a cancer-related topic, such as cancer survivorship. Each video presents statistics that include numbers, rates, and trends in new diagnoses, 5-year survival trends, and mortality rates and trends. The statistics are given for different racial and ethnic... Read more

DYKSEERvideoscancersurveillancetrendshighlights
Sep
19

Population-based cancer surveillance provides a quantitative measurement of cancer occurrence in the United States and globally. Core activities of surveillance include measuring cancer incidence and characterizing each cancer with regard to histopathology, stage, and treatment in the context of survival. Cancer surveillance has been crucial in informing policy and practice, as well as clinical and public health efforts to reduce the cancer burden. Surveillance also provides information for generating research hypotheses on cancer causes and outcomes, and for developing and evaluating interventions for cancer prevention and treatment.

genomicsbiomarkerscancer surveillanceprecision medicineBRCA
Jun
5

An integrated team from NCI’s Surveillance, Epidemiology, and End Results (SEER) Program, four Department of Energy (DOE) labs—Oak Ridge National Laboratory (ORNL), Lawrence Livermore National Lab, Los Alamos National Lab, and Argonne National Lab—Information Management Systems (IMS), and four SEER registries met on March 28th–30th, 2017 to continue their work on the NCI-DOE Pilot 3 collaboration. This partnership will enhance cancer research using the DOE’s expertise in high performance computing and SEER’s expertise in cancer surveillance. The meeting focused on the progress made in Aim 1 and Aim 2 of the pilot as well as future goals for Aim 3.

The goal of Aim 1 is to create natural language processing (NLP) and machine learning tools that can accurately capture information from unstructured clinical text for expanded cancer surveillance data reporting. The collaboration team has completed development of a Clinical Document Annotation and Processing (CDAP) pipeline. This... Read more

collaborationDOEsurveillance dataCDAPNLP
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