-
AI to improve cancer care
15.01.2025
Screenshot from Oslo cancer cluster's website
Project proposal submitted
The first weeks of 2025 has involved late night discussions, long-distance video meetings, and detailed manuscript editing for the application team of NEXTMAP.
On January 15th 2025, the detailed project proposal was submitted to the Norwegian Research Council.
Alongside ICGI, one of the partners in the project is Oslo Cancer Cluster, and their article from the first proposal submittance describes the initial purpose of the project.
-
Prognostic and therapeutic implication of molecular classification including L1CAM expression in high-risk endometrial cancer.
16.11.2024
Graphical abstract for the article Prognostic and therapeutic implication of molecular classification including L1CAM expression in high-risk endometrial cancer.
Our article published in the January issue of the journal Gynecologic Oncology, sheds new light on the role of L1CAM, in high-risk endometrial cancer. The article is the result of a collaboration between the Institute of Cancer Genetics and Informatics and the Department of Surgical Oncology, Section for Gynecological Oncology, at Oslo University Hospital, supported by a grant from the Norwegian Cancer Society.
Highlights
- Clearer role of molecular classification and L1CAM in high-risk endometrial cancer.
- ProMisE independently predicted time to recurrence, not cancer-specific survival.
- Patients with POLE mutated tumors had an excellent prognosis.
- L1CAM overexpression was a strong, independent marker for recurrence and survival.
- L1CAM overexpression was related to distant recurrences for the p53 and NSMP group.
Since L1CAM is an additional adverse factor in the p53 abnormal and NSMP groups. These groups need special attention in studies intensifying adjuvant treatment.
The team aims to improve the prognoses and treatment methods for patients with endometrial cancer. The article was opublished online in November 2025.
Find the full-text article through this link.
-
The European Congress of Pathology 2024
26.09.2024
The delegation from ICGI at the European Congress of Pathology in Florenze, Italy From left: Ljiljana Vlatcovic, Maria Isaksen, Audun Ljone Henriksen, and Manohar Pradhan.
"TLS-positive patients had a lower risk of recurrence, especially in tumors with MMR deficiency", said our skilled pathologist, Dr Manohar Pradhan, when he presented research on the prognostic value of tertiary lymphoid structures (TLS) in endometrial carcinoma. The study involves 1,228 patients at Oslo University Hospital, and is a collaboration with the Department of Gynecological Oncology, OUS.
Dr. Pradhans's session was one of 184 featured at the 36th European Congress of Pathology (ECP), which attracted over 5,700 participants from 100 countries. Among them, a delegation from our institute appreciated the opportunity to contribute to and learn from the ongoing advancements within the field of pathology. ECP was arranged in Florence 7 - 11 September 2024.
We look forward to seeing how insights presented at this year's ECP will contribute to future research and clinical practice.
.
-
Inclusion of several analyzes is beneficial for prostate cancer patients in active surveillance
15.07.2024
Dr. Karolina Cyll at the Department of Cancer Genetics and Informatics (IKI) at Oslo University Hospital and Professor Erik Haug at Vestfold Hospital thank 558 people from Vestfold for their contribution to cancer research. Permission to use data is at the heart of their research paper published in the renowned medical journal British Journal of Cancer (BJC) in July 2024.
.
Photo from @pexels.com.
Prostate cancer is one of the most common forms of cancer among men worldwide, and around 5,000 Norwegians are diagnosed with this disease each year.
Identify, at an earlier stage, patients who have en increased risk of developing agressive disease
Haug and Cyll show that in addition to PSA and other conventional analyses, it is possible to identify those patients who have an increased risk of developing aggressive disease earlier if DNA ploidy analysis and PTEN status are included in the monitoring protocol. By following this advice, active treatment can be initiated earlier for almost half of the patients who eventually end up needing treatment according to current recommendations.
Read the article (in Norwegian), through this link.
.
-
Dr. Ole-Johan Skrede Successfully Defends his Doctoral Thesis
26.04.2024
We extend our congratulations to Dr. Ole-Johan Skrede for successfully defending his doctoral thesis titled "Selected Studies on the Application of Histological Image Analysis in Cancer Diagnostics Using Deep Learning" on Friday, April 26, 2024. The dissertation took place at the Department of Informatics, Faculty of Mathematics and Natural Sciences, in the namesake's Ole-Johan Dahle’s House.
.
From left: Xing Cai, Supervisor Fritz Albregtsen, Anne Solberg, Ole-Johan Skrede, Anders Lundevold and Paul J van Diest Foto: Petter Bjørklund/UiT
.
Dr. Skrede's research focuses on advancing cancer diagnostics through the application of deep learning techniques to analyze histological images. One significant outcome of his work is the development of a method to estimate the prognosis of patients who have undergone colorectal cancer surgery. This innovative approach involves digital microscope image analysis to identify cancerous regions and assess their severity. By training deep learning models on tissue sections from approximately 2,500 patients, Dr. Skrede and his colleagues have developed a deep learning model that enhances the accuracy of prognosis predictions, leading to better stratification of patients to adjuvant chemotherapy after surgery. The research team has rigorously evaluated this methodology on over 1,000 patients to demonstrate its validity and usefulness in clinical practice. Notably, the new method allows identification of substantially more patients that could be spared from unnecessary adjuvant therapy.
.
Dr. Skrede's doctoral thesis comprises three papers published in high-impact journals, significantly contributing to medical research and the recent DoMore project, an ICT Lighthous Project supported by the Research Council of Norway. The papers highlight the integration of deep learning with traditional pathological markers to optimize treatment for patients suffering from colorectal cancer, the possibility to automatically segmented any type of tumor, perhaps even rare types not included in the model development, as well as laying the foundation for better design of deep learning studies in cancer diagnostics and beyond.
.
Before defending his thesis, Dr. Skrede presented a trial lecture at the same venue, on the subject: “Foundation models in cancer research”.
.
The adjudication committee
- Professor Paul J van Diest, Department of Pathology, University Medical Center Utrecht, the Netherlands
- Professor emeritus Arvid Lundervold, Department of Biomedicine, University of Bergen, Norway
- Professor Anne Solberg, Department of Informatics, University of Oslo, Norway
Supervisors
.
Ole-Johan Skrede's supervisors throughout his doctoral journey have been Professor Emeritus Fritz Albregtsen at the Department of Informatics, UiO, Norway, and the late Professor Håvard E. Danielsen, at the Institute for Cancer Genetics and Informatics (ICGI), Oslo University Hospital, Norway.
.
We are so grateful Ole-Johan will continue his research at ICGI, being an important contributor to many of our most prestigious projects.
We extend our gratitude to the committee members for their invaluable insights and to Professor Xing Cai for chairing the defense
.
-
Establishing guidelines for prediction models in medical deep learning is essential
15.01.2024
The increase in scientific publications on deep learning for cancer diagnostics in recent years is impressive, but the conversion of promising prototypes into automated systems for medical utilisation is still moderate. In a recent issue of the scientific journal "Nature Machine Intelligence", Paula Dhiman and colleagues published a comment highlighting the importance of planning evaluations of deep learning systems in advance by predefining study protocols.
Andreas Kleppe, Ole-Johan Skrede and Knut Liestøl from the Institute for Cancer Genetics and Informatics at Oslo University Hospital acclaim the recent initiative by Dhiman and colleagues, and have now published a response to this comment in the January issue of "Nature Machine Intelligence".
Challenges in validations of prediction models
Prototypes for medical deep learning systems frequently claim to perform comparable with or better than clinicians. Even among the best studies evaluating external cohorts, few predefine the primary analysis, which can lead to over-optimistic results due to adaptations of the system, patient selection, or analysis methodology. The lack of stringent evaluation of external data and the development or evaluation of systems on narrow or inappropriate data for the intended medical setting are significant concerns. This over-promising will erode trust in the technology, and may hinder its adoption in the medical clinic. More concerning is the utilisation of prediction models that have not been properly tested, which may result in harm to patients due decisions made based on ill-founded evidence.
Recommended guidelines
In an article published in Nature Reviews Cancer in 2021, "Designing deep learning studies in cancer diagnostics", Kleppe et al. defined a list of recommended protocol items for external cohort evaluation of a deep learning system (PIECES). Among other recommendations, PIECES advocates explicit specification of the primary analysis and any pre-planned secondary analyses that authors wish to commit themselves to report on, and requests that authors describe precisely how the proposed system was developed and how its performance will be assessed.
Since the PIECES article was published, many publications have cited it in support of the need for predefined analyses and external cohort validation, and some have explicitly followed the guidelines.
By implementing these guidelines, medical utilization of deep learning systems can be enhanced, by the way of proper evaluation and translation of promising prototypes into verified systems in clinical practise. Kleppe and colleagues additionally suggest incentives that may increase the uptake of the practice — for example, through endorsement from investors, funders and publishers.
-
The world’s first clinical study using AI on tissue sections to guide the choice of therapy for real patients
15.01.2024
Andreas Kleppe and Tarjei Sveinsgjerd Hveem will conduct the clinical study
A Norwegian study led by researchers at the Institute for Cancer Genetics and Informatics at Oslo University Hospital aims to determine whether AI can help doctors decide which patients need chemotherapy after colorectal cancer surgery. The study will involve about 2,000 patients from Norway, United Kingdom and other countries, and will test whether AI can assist clinicians in providing more personalized treatment.
The AI method, developed by the institute and called Histotyping (video below), works by analyzing digital images of biopsies processed into tissue sections. Specialized doctors in diagnostics and interpretations of changes caused by disease, pathologists, analyze the H&E-stained sections to determine the patient's prognosis and more. AI has been shown to provide supplemental information so that the combination of assessments by AI and pathologists is better than each of them are individually. The new study aims to show that this combination leads to more personalized treatment and benefits the patients.
The study's main investigator, Andreas Kleppe, believes that AI can help many colorectal cancer patients avoid unnecessary chemotherapy. This is the first clinical study to use AI in this way.
Just a few days into the new year, one of Norway’s main newspapers, Aftenposten, wanted to learn more about our study. Several members of our staff were captured by the photographer "in action", as Andreas Kleppe and Tarjei S. Hveem discussed the project with the journalist. The article can be read (in Norwegian) on aftenposten.no.
A video demonstrating Histotyping
-
Personalizing treatment for colorectal cancer patients by combining tissue-based biomarkers and ctDNA
01.12.2023
Combining artificial intelligence-generated digital pathology tools, conventional histopathological assessment and circulating tumor DNA (ctDNA) analysis can improve treatment stratification of patients with colorectal cancer after surgery. Kerr and colleagues outline this novel paradigm for personalized adjuvant treatment of colorectal cancer in a new publication in Nature Reviews Clinical Oncology.
Cancer recurrence is estimated to occur in 80% of patients with colorectal cancer (CRC) within 3 years after surgery. The selection of adjuvant therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. The benefits of adjuvant therapy are relatively marginal, and it is clear that there is a need for better methods for selecting patients who will benefit the most from the treatment whilst sparing those who will not derive benefit.
-"The better we understand the likelihood of cancer recurrence, the better we can tailor our adjuvant therapy, providing a more truly personalized treatment", emphasizes David Kerr, Professor at the University of Oxford and former president of the European Society for Medical Oncology (ESMO)
Liquid biopsies detecting ctDNA have been shown to have clinical utility for early detection of recurrence through surveillance and thus have the potential to personalize the management of CRC patients. However, the analysis of ctDNA is costly, and the initial assessment of a patient's status usually occurs at least four weeks following curative surgery and two weeks after completing systemic therapy. This delay is due to the persistence of elevated levels of cell-free DNA for several weeks post-treatment. Given the uncertain consequences of delaying potential chemotherapy and the fact that some patients may not show detectable ctDNA at their initial follow-up assessment, we propose using tissue-based biomarkers to facilitate an early pre-selection of treatment.
Improved patient management
Current clinicopathological markers are insufficient to stratify patients with early-stage CRC accurately. In 2020, Skrede et al. demonstrated how artificial intelligence (AI) can be used to predict CRC patient outcome in a study in The Lancet (Skrede et al., The Lancet 2020). The AI marker, named DoMore-v1-CRC, predicts the likelihood of cancer-specific death directly from images of routine histopathology sections. Building on these findings, the marker has since then been integrated with established clinicopathological markers to provide a clinical decision support system (CDSS) for guiding the choice of adjuvant chemotherapy in stage II and III CRC without residual disease after surgery (Kleppe et al., Lancet Oncology 2022).
Compared to conventional risk stratification for adjuvant therapy, the proposed CDSS identifies a much larger group of patients with an excellent prognosis that are likely to have similar survival with and without adjuvant chemotherapy and can, therefore, be spared the severe side effects of the treatment.
Since the CDSS's recommendation can be determined within a few days after surgery, patients identified as high-risk can begin treatment soon after surgery. In addition, the CDSS would identify additional strong candidates for adjuvant chemotherapy among those who are ctDNA negative at first assessment. Patients classified as low risk by the CDSS would then enter a ctDNA monitoring program and receive treatment upon ctDNA detection, if any.
- "I believe that integrating tissue and blood-borne prognostic biomarkers, as we suggest in this article, does make sense in regard to a more personalized treatment", says Professor Kerr. With this combined approach, more than half the patients with high-risk stage II and III CRC can be spared from adjuvant treatment, as they are very unlikely to benefit from it. This novel paradigm will reduce the economic cost and personnel requirements and improve patient management by more truly personalized treatment – which is ultimately the goal!

Illustration of patient management using the combination of tissue-based biomarkers and ctDNA
-
The Institute for Cancer Genetics and Informatics receives funding from the Norwegian Cancer Society
27.11.2023
Research funding from the Norwegian Cancer Society is raised by the Norwegian public and is an important contribution to Norwegian cancer research environments. The number of applications for the annual call for proposals was again numerous this year, and the competition is strong. Our project aims to use artificial intelligence (AI) to improve risk stratification in patients with colorectal cancer that has spread to the liver. We are grateful that the Norwegian Cancer Society has chosen to support our work and ambitions, which will enable the Institute to continue its work to guide in tailoring treatment options for patients with colorectal cancer.
About the project
An ageing population comes with an increase in cancer incidence. Despite the many advancements in diagnosis, surgical technique, screening, and molecular characterisation, colorectal cancer (CRC) remains a major global health problem, being the second most common cancer and the second most common cause of cancer death in Norway. About 20% of CRC patients are diagnosed with distant metastasis at primary diagnosis, and an additional 25% develop distant metastasis after surgery for localised colorectal cancer. Treatment of colorectal liver metastasis (CLRM) is inconsistent, but resection and chemotherapy are the standard treatment methods in patients who are eligible for surgery. Among patients undergoing liver resection, approximately 40% develop recurrences within one year after surgery, illustrating the need for better tools to identify the proper treatment for each patient.
Artificial intelligence (AI) radically transforms our society, including healthcare and medical diagnostics. Deep learning (DL) is a subfield of AI that is well-suited to perform complex visual recognition tasks and has proven particularly useful in medical image analysis. Based on long-term experience in digital pathology, the Institute for Cancer Genetics and Informatics (ICGI) at Oslo University Hospital has, over the last 8 years, built a competent computing environment for DL in medical image analysis. Deep learning has been used to predict patient outcomes from Whole Slide Images (WSIs) of routine haematoxylin and eosin (H&E)-stained tissue sections from cancers and similar methodology will be utilised in the current project. The project aims to develop deep learning models for predicting recurrence and survival in patients with colorectal liver metastases treated with surgery, to tailor adjuvant treatment and surveillance programmes which in turn will improve survival and quality of life. By linking these predictions with a characterisation of cells and tissue, including morphology and cell types, the project aims to reveal biological mechanisms involved in metastasis and poor patient outcomes. Overall, the project's objectives are to improve risk stratification and identify patients who will benefit from aggressive treatment or those who should not undergo surgery based on their frailty and treatment prospects.
-
Welcome to the 5th CRC Network Meeting in Oxford (UK), 30 - 31. March 2023
27.02.2023
The themes at this years annual conference are on the biology and management of colorectal cancer. This is the 5th CRC Network meeting to be held in Queen’s College, Oxford.
We bring together scholars who enjoy the intimate atmosphere of an Oxford College, enabling discussion and potential collaboration between all attendees.
The CRC Network meeting is free, and open to all interested in advances in research and treatment options for patients suffering from colorectal cancer. Due to limited capacity, registration is required.
For more information, go to the website crcnetwork.net