Doing more to provide faster and safer cancer prognostics

In order to best treat cancer, we must first understand how the disease will develop. The research in DoMore! will enhance our ability to predict the development of a patient’s cancer, and thereby give the right treatment.

Predicting a cancer’s development

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. We are using digital tools, to increase efficiency in pathology and to provide a more accurate prognosis to cancer patients. This will make the process behind the choice of treatment faster and safer.

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 it is a great challenge for prognosis of cancer.

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

Understanding cancer development

Three conditions make one significant reason for why it is difficult to know how a patient's cancer will develop.


We don't know enough about how to divide between a dangerous and less dangerous tumor. Cancers can follow multiple paths. Predicting the further development of the disease is complicated. Some will progress to metastases and death while others will remain indolent.

The pathologists who are currently doing the work need several years of specialisation. Health systems around the world experience a major shortage of this specialist group. Today´s prognostication is based on subjective assessments. If the conclusion is incorrect, it may cause additional costs, unnecessary side effects and, at worst, the death of the patient.


A single tumour can contain multiple regions of different and unevenly distributed aberrations that might or might not lead to cancer.

We seek a broader understanding of cancer heterogeneity in the enormous reserves of patient data stored in hospital data basis. Patterns in this data can tell us a lot about how a patient's cancer develops.


A single tumour can contain multiple regions of different and unevenly distributed aberrations that might or might not lead to cancer.

Sampling for most markers of cancer is performed on a too small fraction of a single block of tissue. Sample size plays a significant role in accurate cancer prognostics, especially when we take cancer heterogeneity into account.

Using prostate cancer as an example, the sample examined is a mere 1:1000 of a tumour. This small sample size means there is a higher risk of missing the cells that go on to kill the patient. Grading the sample and giving a correct prognosis depends heavily on the pathologist’s expertise. Pathologists assessing the same sample can arrive at vastly different results.

Big data and deep learning

Development of new methods for faster and more secure prognosis is the key to a more precise treatment.

We are using new tools and developing methods in cancer types where pathology has met its limits. This will reduce human error and remove subjective analyses. Patients will benefit through more precise prognosis and more targeted treatment.

Recent years' advances in computing and processing have made it possible to explore far greater amounts of data than before.

We are using big data and deep learning to establish more robust grading systems. Our computers will be able to retrieve and treat far more information about a tumour than pathologists can do with today's methods.


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

The project is led by Professor Håvard E. Danielsen, Director of Institute for Cancer Genetics and Informatics (ICGI), Oslo University Hospital (OUS).

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