KaSARi: a national framework for standardized, automated, and predictive radiotherapy in Finland

Authors

  • Jan Seppälä Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland https://orcid.org/0000-0002-0922-329X
  • Maria Tengström Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland https://orcid.org/0000-0002-4024-1904
  • Henri Korkalainen Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland; Department of Techincal Physics, University of Eastern Finland, Kuopio, Finland https://orcid.org/0000-0002-4686-6112
  • Tuomas Virén Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland https://orcid.org/0000-0002-7685-7911
  • Juuso TJ Honkanen Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland https://orcid.org/0000-0002-9150-9253
  • Juha Nikkinen Department of Radiotherapy, Research Unit of Health Sciences and Technology, Oulu University Hospital, University of Oulu, Oulu, Finland https://orcid.org/0000-0001-6355-3444
  • Kaisa Lehtiö Department of Radiotherapy, Research Unit of Health Sciences and Technology, Oulu University Hospital, University of Oulu, Oulu, Finland
  • Annika Ålgars Department of Oncology and Radiotherapy, Turku University Hospital and University of Turku, Turku, Finland https://orcid.org/0000-0002-9841-1881
  • Jani Keyriläinen Department of Oncology and Radiotherapy, Turku University Hospital and University of Turku, Turku, Finland https://orcid.org/0000-0001-7504-4460
  • Sami Suilamo Department of Oncology and Radiotherapy, Turku University Hospital and University of Turku, Turku, Finland
  • Heidi Nurmi Department of Oncology and Radiotherapy, Turku University Hospital and University of Turku, Turku, Finland https://orcid.org/0009-0009-0008-6522
  • Tanja Skyttä Department of Oncology, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland
  • Eeva Boman Department of Oncology, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland; Department of Clinical Physiology, Nuclear Medicine and Medical Physics, Tampere University Hospital, Wellbeing Services County of Pirkanmaa,Tampere, Finland https://orcid.org/0000-0001-6492-2475
  • Tero Vatanen Cancer Centre, North Carelia Central Hospital, Joensuu, Finland
  • Liisa Sailas Cancer Centre, North Carelia Central Hospital, Joensuu, Finland
  • Ulla-Mari Arkko Cancer Centre, North Carelia Central Hospital, Joensuu, Finland
  • Kristiina Vuolukka Department of Radiotherapy and Oncology, Wellbeing Services County of Central Finland, Jyväskylä, Finland https://orcid.org/0000-0001-9067-0566
  • Tuomas Koivumäki Department of Radiotherapy and Oncology, Wellbeing Services County of Central Finland, Jyväskylä, Finland https://orcid.org/0000-0001-8673-7000
  • Tiina Metsäharju Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland
  • Timo Voivalin Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland
  • Janne Heikkilä Department of Radiotherapy, Kuopio University Hospital, Kuopio, Finland https://orcid.org/0000-0003-1767-5779

DOI:

https://doi.org/10.2340/1651-226X.2026.45682

Keywords:

Radiotherapy, Real-world data, Artificial Intelligence, Toxicity, Quality of Life

Abstract

Background and purpose: National radiotherapy (RT) data infrastructures are emerging to support treatment quality assurance and outcome research. However, prospective nationwide integration of full DICOM-RT data with toxicity and patient-reported outcome measures (PROMs) remains uncommon. KaSARi (Key advances in Standardizing, Automating, and predicting Risks in RT) was established to create national research infrastructure for prospective RT data collection and integration in Finland.
Materials and methods: KaSARi is a prospective, consent-based multicenter cohort infrastructure involving Finnish university and central hospitals. All adult patients receiving RT are eligible. DICOM-RT datasets from treatment planning and oncology information systems are linked to clinician-graded toxicity, PROMs and relevant clinical variables. The infrastructure builds on previously validated multi-institutional DICOM workflows and follows FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Predefined work packages support data harmonization, scalable data processing and enable downstream applications such as AI-based segmentation, dose prediction, and outcome modeling.
Results: Patient recruitment started in May 2025. By February 2026, 221 patients had been enrolled at the coordinating center, initially including breast cancer patients and subsequently expanding to all RT indications. Recruitment at a second center began in November 2025, with 37 patients enrolled. The infrastructure is projected to include approximately 29,000 patients over 15 years. Integration of dosimetric, clinical and PROM data is progressing through a phased national implementation as additional centers join the network.
Interpretation: KaSARi represents a national infrastructure prospectively integrating full DICOM-RT datasets with clinician-graded toxicity and PROMs across all RT indications within a unified research protocol. The study provides a foundation for nationwide outcome modeling, harmonization of RT practices and development and validation of data-driven AI-based methods.

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Published

2026-05-18

How to Cite

Seppälä, J., Tengström, M., Korkalainen, H., Virén, T., Honkanen, J. T., Nikkinen, J., … Heikkilä , J. (2026). KaSARi: a national framework for standardized, automated, and predictive radiotherapy in Finland . Acta Oncologica, 65, 430–436. https://doi.org/10.2340/1651-226X.2026.45682

Issue

Vol. 65 (2026): Acta Oncologica

Section

Original article

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Copyright (c) 2026 Jan Seppälä, Maria Tengström, Henri Korkalainen, Tuomas Virén, Juuso TJ Honkanen, Juha Nikkinen, Kaisa Lehtiö, Annika Ålgars, Jani Keyriläinen, Sami Suilamo, Heidi Nurmi, Tanja Skyttä, Eeva Boman , Tero Vatanen, Liisa Sailas , Ulla-Mari Arkko, Kristiina Vuolukka, Tuomas Koivumäki, Tiina Metsäharju, Timo Voivalin , Janne Heikkilä

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This work is licensed under a Creative Commons Attribution 4.0 International License.