Myelofibrosis and Survival Prognostic Models: A Journey between Past and Future.
Andrea DuminucoAntonella NardoGaetano GiuffridaSalvatore LeottaUros MarkovicCesarina GiallongoDaniele TibulloAlessandra RomanoFrancesco Di RaimondoGiuseppe Alberto Maria PalumboPublished in: Journal of clinical medicine (2023)
Among the myeloproliferative diseases, myelofibrosis is a widely heterogeneous entity characterized by a highly variable prognosis. In this context, several prognostic models have been proposed to categorize these patients appropriately. Identifying who deserves more invasive treatments, such as bone marrow transplantation, is a critical clinical need. Age, complete blood count (above all, hemoglobin value), constitutional symptoms, driver mutations, and blast cells have always represented the milestones of the leading models still used worldwide (IPSS, DIPSS, MYSEC-PM). Recently, the advent of new diagnostic techniques (among all, next-generation sequencing) and the extensive use of JAK inhibitor drugs have allowed the development and validation of new models (MIPSS-70 and version 2.0, GIPSS, RR6), which are continuously updated. Finally, the new frontier of artificial intelligence promises to build models capable of drawing an overall survival perspective for each patient. This review aims to collect and summarize the existing standard prognostic models in myelofibrosis and examine the setting where each of these finds its best application.
Keyphrases
- artificial intelligence
- bone marrow
- machine learning
- deep learning
- newly diagnosed
- mesenchymal stem cells
- air pollution
- induced apoptosis
- ejection fraction
- case report
- chronic kidney disease
- end stage renal disease
- oxidative stress
- cell proliferation
- risk assessment
- patient reported
- stem cells
- peripheral blood
- drug induced
- current status
- free survival