AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib

Abstract

BackgroundIn myelofibrosis (MF), new model scores are continuously proposed to improve the ability to better identify patients with the worst outcomes. In this context, the Artificial Intelligence Prognostic Scoring System for Myelofibrosis (AIPSS‐MF), and the Response to Ruxolitinib after 6 months (RR6) during the ruxolitinib (RUX) treatment, could play a pivotal role in stratifying these patients.AimsWe aimed to validate AIPSS‐MF in patients with MF who started RUX treatment, compared to the standard prognostic scores at the diagnosis and the RR6 scores after 6 months of treatment.Methods and resultsAt diagnosis, the AIPSS‐MF performs better than the widely used IPSS for primary myelofibrosis (C‐index 0.636 vs. 0.596) and MYSEC‐PM for secondary (C‐index 0.616 vs. 0.593). During RUX treatment, we confirmed the leading role of RR6 in predicting an inadequate response by these patients to JAKi therapy compared to AIPSS‐MF (0.682 vs. 0.571).ConclusionThe new AIPSS‐MF prognostic score confirms that it can adequately stratify this subgroup of patients already at diagnosis better than standard models, laying the foundations for new prognostic models developed tailored to the patient based on artificial intelligence.

How artificial intelligence revolutionizes the world of multiple myeloma

“…Large amounts of data have been generated in clinical trials, registries, and real-world cohorts, which can be processed and analyzed using artificial intelligence (AI) algorithms (3). These are integrative approaches that use individual data from datasets and can suggest the optimal therapy for a specific patient, as has been demonstrated in other models of neoplastic (7)(8)(9) or non-neoplastic diseases (10,11).…”

Integrating AIPSS‐MF and molecular predictors: A comparative analysis of prognostic models for myelofibrosis

“…In this study, we employed a machine learning (ML) method to develop the AIPSS‐MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis; available at https://geneticsoncohematology.com/MF/ ). 11 , 12 This model, which relies on eight clinical variables (age, sex, hemoglobin, leukocytes, platelets, peripheral blasts, constitutional symptoms, and leukoerythroblastosis), evaluated at MF diagnosis, demonstrated a robust capability to predict OS and leukemia‐free survival (LFS). Notably, its predictive accuracy surpassed that of established prognostic models like the IPSS for PMF patients and the MYSEC‐PM for SMF patients.…”