학술논문

AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib
Document Type
article
Source
Cancer Reports, Vol 6, Iss 10, Pp n/a-n/a (2023)
Subject
AIPSS‐MF
machine learning
myelofibrosis
RR6
ruxolitinib
standard prognostic score
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Language
English
ISSN
2573-8348
Abstract
Abstract Background In 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. Aims We 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 results At 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). Conclusion The 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.