Mission

The OHDSI Oncology Working Group aims to provide a foundation for representing cancer data within the OMOP CDM at the levels of granularity and abstraction required to support observational cancer research.

Oncology support in OMOP is a work in progress. We welcome your participation!



Subgroups & Meeting Schedule

TODO: update the following table with the new 2024 meeting links. Until then, and even after then, it is highly recommended that you add the meetings series to your own calendar, which are found within the oncology teams calendar, instead of reyling on this table.

subgroup schedule meeting details Team Lead
CDM/Vocabulary & Development First Thu 1PM EST
Third Thu 9AM EST
link Thomas Falconer, Michael Gurley, & Robert Miller
Outreach/Research Second Tue 3PM EST
Fourth Wed 9AM EST
link Asieh Golozar & Christian Reich
Genomic Second and Fourth Tue 9AM EST link Asieh Golozar


Problem Space

In a typical observational study, the definition of the study population (cohort), exposures and outcomes are usually based on diagnostic codes in addition to drug exposures, procedure occurrences or lab measurements. For cancer studies, this information is typically not sufficient, as more details are required for the proper identification of the study population, treatment and subsequent outcomes.

Appropriate characterization of cancer requires details such as anatomical site, morphology, local penetration, affected lymph nodes, metastatic spread, biomarkers, and disease staging and grading. In typical observational data sources, this necessary level of detail is not regularly present. Patient results from diagnostic procedures are collected but may not be available within the given data source or what is collected cannot appropriately serve as a surrogate for the above attributes. Correct identification of cancer treatment regimens also tends to be more complex compared to other disease modalities within observational data. Most cancer treatments are administered in chemotherapy regimens with complex dosing and scheduling in multiple cycles and are often combined with targeted therapies, immunotherapies, surgery or radiotherapy. None of these attributes follow standard definition to be applied to observational data, as most regimens are personalized to the individual patient need, making a priori standardized definitions more complex. Additionally, clinically relevant information on disease, treatment and outcomes that appropriately reflects a patient’s journey including information on the time of diagnosis, response to treatments, time to treatment failure, disease progression, recurrence and (disease-free and overall) survival requires data abstraction and is rarely available in the source data and has not been traditionally supported in OMOP CDM.

The Oncology CDM Extension of the OMOP CDM aims to provide a foundation for representing cancer data at the levels of granularity and abstraction required to support observational cancer research.

The extension has been tested in EHR and Cancer Registry data against a number of typical use cases.



Site Map

This site contains the following sections:

WG Home

Problem Space High level summary of working group mission
Publications/Presentations Links to some relevant publications & presentations

Development Effort

Development Overview
Purpose
Goals
Notable Challenges
Context
Scope
What we need
Project Management
Overview of current development effort
Strategy
Delta Vocabulary
Validation Framework
Details regarding key components of the development strategy
Progress Map
Miro Map
Roadmap
Miro Map of completed and outstanding work within scope (with links)
Github Project Orientation
Architecture
Strategy
Project Navigation
Example Walkthrough
Documentation about navigating and understanding the Github Project and approach
Getting Involved
Join Collaboration Channels
Submit Collaborator Form
Review Project Documentation
Contributing
Suggestions and links for getting started in the effort!

Specifications

Conventions
Diagnostic
Treatment
Overview of current development effort
Model
Genomics

Implementation

Installation
Tooling
ETLs
NAACCR ETL