AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Cancer Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cnr2.1881 |
| _version_ | 1797650754807267328 |
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| author | Andrea Duminuco Adrian Mosquera‐Orgueira Antonella Nardo Francesco Di Raimondo Giuseppe Alberto Palumbo |
| author_facet | Andrea Duminuco Adrian Mosquera‐Orgueira Antonella Nardo Francesco Di Raimondo Giuseppe Alberto Palumbo |
| author_sort | Andrea Duminuco |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T16:06:01Z |
| format | Article |
| id | doaj.art-54ba6c76e1b441e48b949a27405de65e |
| institution | Directory Open Access Journal |
| issn | 2573-8348 |
| language | English |
| last_indexed | 2024-03-11T16:06:01Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Reports |
| spelling | doaj.art-54ba6c76e1b441e48b949a27405de65e2023-10-25T03:24:42ZengWileyCancer Reports2573-83482023-10-01610n/an/a10.1002/cnr2.1881AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinibAndrea Duminuco0Adrian Mosquera‐Orgueira1Antonella Nardo2Francesco Di Raimondo3Giuseppe Alberto Palumbo4Hematology with BMT Unit, A.O.U. “G. Rodolico‐San Marco” Catania ItalyHospital Clínico Universitario Santiago de Compostela SpainHematology with BMT Unit, A.O.U. “G. Rodolico‐San Marco” Catania ItalyHematology with BMT Unit, A.O.U. “G. Rodolico‐San Marco” Catania ItalyHematology with BMT Unit, A.O.U. “G. Rodolico‐San Marco” Catania ItalyAbstract 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.https://doi.org/10.1002/cnr2.1881AIPSS‐MFmachine learningmyelofibrosisRR6ruxolitinibstandard prognostic score |
| spellingShingle | Andrea Duminuco Adrian Mosquera‐Orgueira Antonella Nardo Francesco Di Raimondo Giuseppe Alberto Palumbo AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib Cancer Reports AIPSS‐MF machine learning myelofibrosis RR6 ruxolitinib standard prognostic score |
| title | AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib |
| title_full | AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib |
| title_fullStr | AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib |
| title_full_unstemmed | AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib |
| title_short | AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib |
| title_sort | aipss mf machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib |
| topic | AIPSS‐MF machine learning myelofibrosis RR6 ruxolitinib standard prognostic score |
| url | https://doi.org/10.1002/cnr2.1881 |
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