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  • Jorgensen, Rasmus Rask KraghAalborg Univ Hosp, Clin Canc Res Ctr, Dept Hematol, Aalborg, Denmark.;Aalborg Univ, Dept Clin Med, Aalborg, Denmark. (författare)

Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma

  • Artikel/kapitelEngelska2024

Förlag, utgivningsår, omfång ...

  • Lippincott Williams & Wilkins,2024
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:uu-535867
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-535867URI
  • https://doi.org/10.1200/CCI.23.00255DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:238608215URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • PurposePatients 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 MethodsThis study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort).ResultsIn total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis.ConclusionThe new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Bergstroem, FannyKarolinska Inst, Dept Med Solna, Clin Epidemiol Div, Stockholm, Sweden. (författare)
  • Eloranta, SandraKarolinska Institutet,Karolinska Inst, Dept Med Solna, Clin Epidemiol Div, Stockholm, Sweden. (författare)
  • Severinsen, Marianne TangAalborg Univ Hosp, Clin Canc Res Ctr, Dept Hematol, Aalborg, Denmark.;Aalborg Univ, Dept Clin Med, Aalborg, Denmark. (författare)
  • Smeland, Knut BjoroOslo Univ Hosp, Dept Oncol, Oslo, Norway. (författare)
  • Fossa, AlexanderOslo Univ Hosp, Dept Oncol, Oslo, Norway. (författare)
  • Christensen, Jacob HaaberOdense Univ Hosp, Dept Hematol, Odense, Denmark. (författare)
  • Hutchings, MartinRigshospitalet, Dept Hematol, Copenhagen, Denmark.;Univ Copenhagen, Dept Clin Med, Copenhagen, Denmark. (författare)
  • Bo Dahl-Sorensen, RasmusZealand Univ Hosp, Dept Hematol, Roskilde, Denmark. (författare)
  • Kamper, PeterAarhus Univ Hosp, Dept Hematol, Aarhus, Denmark. (författare)
  • Glimelius, Ingrid,1975-Karolinska Institutet,Uppsala universitet,Cancerprecisionsmedicin,Karolinska Inst, Dept Med Solna, Clin Epidemiol Div, Stockholm, Sweden.(Swepub:uu)inggl846 (författare)
  • Smedby, KarinKarolinska Inst, Dept Med Solna, Clin Epidemiol Div, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Hematol, Stockholm, Sweden. (författare)
  • Parsons, SusanTufts Med Ctr, Inst Clin Res & Hlth Policy Studies, Dept Med, Boston, MA USA. (författare)
  • Rodday, Angie MaeTufts Med Ctr, Inst Clin Res & Hlth Policy Studies, Dept Med, Boston, MA USA. (författare)
  • Maurer, MatthewMayo Clin, Dept Qualitat Hlth Sci, Rochester, MN USA. (författare)
  • Evens, AndrewRutgers Canc Inst New Jersey, Div Blood Disorders, New Brunswick, NJ USA. (författare)
  • El-Galaly, TarecAalborg Univ Hosp, Clin Canc Res Ctr, Dept Hematol, Aalborg, Denmark.;Aalborg Univ, Dept Clin Med, Aalborg, Denmark. (författare)
  • Jakobsen, Lasse HjortAalborg Univ Hosp, Clin Canc Res Ctr, Dept Hematol, Aalborg, Denmark.;Aalborg Univ, Dept Math Sci, Aalborg, Denmark. (författare)
  • Aalborg Univ Hosp, Clin Canc Res Ctr, Dept Hematol, Aalborg, Denmark.;Aalborg Univ, Dept Clin Med, Aalborg, Denmark.Karolinska Inst, Dept Med Solna, Clin Epidemiol Div, Stockholm, Sweden. (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:JCO Clinical Cancer Informatics: Lippincott Williams & Wilkins82473-4276

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