Doing more to provide faster and safer cancer prognostics

DoMore! is a five-year-long project within cancer research, developing new diagnostic methods based on artificial intelligence. The purpose has been to utilise new technology to reduce over-and undertreatment of cancer by providing a new system for cancer prognosis. The lack of objective and precise methods for cancer prognosis is the most crucial cause behind the prevalence of over-and under treatment of cancer patients worldwide.

Explore the results of the DoMore!-project: Results

DoMore! project organisation

The DoMore! project is split into 7 work packages defining the steps necessary for completion of the project. Each Work package (WP) has a dedicated team reporting to the project administration. This approach has contributed to the projects success.

WP1: Data Production

Production of images that will be used in methods development (WP-2-6) and in the test and validation studies in WP7.

Click for more details on the Data Production WP

Fully automatisation of the analysis pipeline for the use of deep learning and big data to develop new grading systems for colorectal and lung cancer.

See details of the Histotyping Work Package

Developing reliable, fully automatic methods for tumor delineation and nuclear segmentation in tissue sections.

Click here for more information

Finding biomarkers based on analysis of nuclei in tissue sections. Further developed research tools for practical use in routine pathology.

Read more about the Work Package here

Establishing solutions that ensure efficient data flow of quality-controlled images.

Read more about the Work Package here

Developing a decision support system for prognostication of cancer patients using machine learning techniques applied to large datasets. Health economy and commercialisation.

Click here for more information on this Work Package

Test and validation studies, scientific publications and dissemination.

Click here for more information on this Work Package

Project scope

The DoMore! project was in 2016 selected as one of the Norwegian Research Council’s Lighthouse projects aiming to solve large societal challenges using cutting-edge technology. Understanding how a tumour will develop is essential for the proper treatment of cancer patients. A range of artificial intelligence products based on the DoMore!-research will be available to assist clinicians in the near future.

AI for prediction

Using deep convolutional neural networks has enabled more accurate prognostication of cancer.

The challenge of heterogeneity in cancers

Heterogeneity is one of the key research interests for the DoMore!-project.

Why do more?

As the amount of cancer patients rises, we need more advanced tools to give patients more reliable diagnostics and prognostics.

We need better tools to accurately assess the patient's outcome. Many cancer patients receive more treatment than they need.

Overtreated patients are susceptible to severe side effects and reduced quality of life. This is a burden to patients and expensive to society.

The shortage of pathologists only adds to the increasing difficulty to keep up with patient demand. The manual laboratory procedures needed to render a prognosis are time-consuming and subjective.The DoMore! project provides digital tools, to increase efficiency in pathology and to provide a more accurate prognosis to cancer patients. This will make the process of choice of treatment faster and safer, for both patients and clinicians.

Better tools for better treatment of cancer

The interactive graphics linked above show prostate cancer in 3D and demonstrate heterogeneity as it is observed in sections.

A tumor can contain several regions of different and unevenly distributed aberrations that may or may not lead to cancer. This is called heterogeneity, and is a great challenge for prognosis of cancer. Prognostication and sampling are also challenges being explored in the DoMore! project.


We have a team of national and international experts within many different fields, including digital image analysis, tumor pathology, cancer surgery and oncology.

The DoMore! project is funded by the Norwegian Research Council's ICT Lighthouse project grant