Colorectal cancer

Colorectal cancer is the second most frequent cancer type and represents 13.2% and 12.7% of all cancer cases in men and women, respectively. Approximately 50 % of all patients will develop liver metastasis, and surgical resection is currently the only curative treatment for these patients. Novel molecular diagnostics of the tumors, together with an expanding array of other diagnostic tools is challenging the decision processes for choice of treatment and patient pathways. There is a need for improved clinical decision tools to support this development.

The clinical challenges of today include access to data for clinical decisions – both in internal and external systems - and clinical decision support for making sense of and prioritizing the information available. The work in BigMed focuses on getting the right information to the point of care; structuring data for easier use and reuse of data, access to internal and external knowledge and support for interpretation of the data for clinical use.

Throughout the patient trajectory, there is a need for access to information. In the Multi Disciplinary Team meeting many important decisions are made, and access to the right information as a decision basis is key.


Technologies for clinical decision support both in and outside the MDT are today limited. Limitations in these technologies start with the referral process. Although clinical information is essential when forming treatment plans for patients with CRC, the Norwegian healthcare ICT infrastructure does not allow for information supply to MDT members at OUS in a way that is optimally efficient, precise, and reliable.

Activities and deliverables are described below:

  • Mapping of clinical needs based on design thinking
  • Implementation of Dashboard for clinical decision support in DIPS Arena – including timeline and structured patient information to reduce errors and reduce clinician time spent searching for information
  • Text mining from electronic health record for automatic population of Dashboard
  • Data extraction from registries to populate patient timeline – previous disease incidents. Demo.
  • Open EHR Archetypes definition for structured report to cancer registry, saving valuable clinician time and improving valuable data gathering.
  • Statistics for patients like me: Application showing live cancer registry outcome data based on different clinical interventions for similar patient cases, clinical decision support. Prototype.
  • Patient similarity tool: Advanced clustering techniques for multi criteria similarity.
  • Clinical reporting of molecular diagnostics:
    • Genomics-based reports from somatic sequencing.
    • Clinical decision support module for tumor board meeting dashboard

Vegar Dagenborg

OUS gastrokirurgi

+47 906 20 820

Send email

Relevant Projects

Illustration - a dense pattern with dots and lines connected

A pipeline and tool for personalised patient classification based on integration of multiple patient data

We are developing a novel pipeline, algorithm and computational tool, to assist clinicians in assigning the best therapy to individual patients by integrating in a novel and transparent way multiple type of data from patients

Extraction of text from health records with NLP and ML

Extraction of text from health records with NLP and ML

This project began with the ambitious aim of reducing the workload for clinicians treating patients with colorectal cancer.
Regulation of CDS Software

Regulation of CDS Software

The amount of health data used in patients’ diagnosis or treatment is growing beyond a manageable level for individual clinicians. Clinical decision support (CDS) software can help to access and analyse these data and thereby assist

Comprehensive clinical report from molecular diagnostics in cancer

Comprehensive clinical report from molecular diagnostics in cancer

Cancer is a genetic disease, as DNA is altered in each tumor. The changes differ greatly between different tumors and over time in a tumor. Many changes can occur in each tumor, and these changes may represent targets for therapy, or serve
Boy - with simplified mesh of his face illustrated.

Big data management for the precise treatment of three patient groups

This report is produced as part of the BigMed project to document and distribute knowledge accumulated in the first phase of the project, identifying the challenges and obstacles the project aims to address.