Rasmus Rask Kragh Jørgensen, Fanny Bergström, Sandra Eloranta, Marianne Tang Severinsen, Knut Bjøro Smeland, Alexander Fosså, Jacob Haaber Christensen, Martin Hutchings, Rasmus Bo Dahl-Sørensen, Peter Kamper, Ingrid Glimelius, Karin E Smedby, Susan K Parsons, Angie Mae Rodday, Matthew J Maurer, Andrew M Evens, Tarec C El-Galaly, Lasse Hjort Jakobsen
PURPOSE: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS: This study used patient data from the Danish National Lymphoma Register for model development (development cohort)...
April 2024: JCO Clinical Cancer Informatics