Institute news

• EUnetCCC workshop at OUS

  27.02.2025

  

  EUnetCCC WP8 Task Lead Team Workshop is taking place on 27th-28th February 2025 at Oslo University Hospital, Norway. 

  ICGI is contributing significantly to the EUnetCCC project

  This is a major EU initiative to enhance collaboration among European Comprehensive Cancer Centres (CCCs). Oslo University Hospital (OUS) leads Work Package 8, involving 31 countries, 96 institutions, and 60 patient care facilities.

  Rajni Kumar is leading this effort at ICGI as the technical project manager and developer. This week, web developer Marcin Soja joined her in their quest to develop web solutions to support collaborative frameworks and toolkits for the network. Welcome, Marcin!

  The workshop themes are a range of initiatives, including:

  • Enhancing personalized cancer prevention strategies
  • Advancing precision cancer diagnostics
  • Improving the use of clinical data for research & monitoring
  • Developing comprehensive survivorship programs for cancer patients
  • Strengthening governance, organization, and leadership in cancer care

  The project is aiming to ensure that by 2030, 90% of eligible patients in Europe will have access to high-quality care at Comprehensive Cancer Centres.

  Read our project description for the EUnetCCC project.

• How to become a world leader in AI and cancer treatment

  11.02.2025

  

  The Institute for Cancer Genetics and Informatics is pleased to contribute to the ongoing discussion about leveraging AI for improved cancer treatment. Our Interim Director, Tarjei Sveinsgjerd Hveem, recently co-authored an article highlighting Norway's unique health data resources.

  Combining our extensive health registries with advanced AI models is the goal of a proposed project called NEXTMAP, which aims to enhance cancer prevention, diagnostics, and treatment. This collaborative effort brings together experienced researchers, companies, and hospitals, pooling a wealth of expertise in cancer research and informatics.

  We're grateful to be part of this initiative and ICGI remains committed to supporting this important work by building on the valuable experience of all partners involved.

  Read the article in norwegian on dagensmedisin.no, or

  in english on Oslo Cancer Cluster's website

• AI to improve cancer care

  15.01.2025

  

  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

  

  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

  25.09.2024

  

  "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. Additionally Audun Ljone's poster "Improving histopathological screening of colorectal polyps using deep-learning" was on display at the e-poster terminals at the confence venue. 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.

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• 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. .

  

  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.

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• 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. .

  

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  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

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• 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

  

  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