https://scholars.lib.ntu.edu.tw/handle/123456789/641594
標題: | Validation of the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes in chronic myelomonocytic leukaemia: A novel approach for improved risk stratification | 作者: | Mosquera Orgueira, Adrian Perez Encinas, Manuel Mateo Diaz Varela, Nicolas Wang, Yu-Hung Mora, Elvira Diaz-Beya, Marina Montoro, Maria Julia Pomares Marin, Helena Ramos Ortega, Fernando Tormo, Mar Jerez, Andres Nomdedeu, Josep de Miguel Sanchez, Carlos Arenillas, Leonor Carcel, Paula Cedena Romero, Maria Teresa Xicoy Cirici, Blanca Rivero Arango, Eugenia Del Orbe Barreto, Rafael Andrés Benlloch, Luis Lin, Chien-Chin HWEI-FANG TIEN Pérez Míguez, Carlos Crucitti, Davide Díez Campelo, María Valcárcel, David |
關鍵字: | AIPSS-MDS; CMML; MDS; artificial intelligence; leukaemia; prognosis | 公開日期: | 27-二月-2024 | 來源出版物: | British journal of haematology | 摘要: | Chronic myelomonocytic leukaemia (CMML) is a rare haematological disorder characterized by monocytosis and dysplastic changes in myeloid cell lineages. Accurate risk stratification is essential for guiding treatment decisions and assessing prognosis. This study aimed to validate the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS) in CMML and to assess its performance compared with traditional scores using data from a Spanish registry (n = 1343) and a Taiwanese hospital (n = 75). In the Spanish cohort, the AIPSS-MDS accurately predicted overall survival (OS) and leukaemia-free survival (LFS), outperforming the Revised-IPSS score. Similarly, in the Taiwanese cohort, the AIPSS-MDS demonstrated accurate predictions for OS and LFS, showing superiority over the IPSS score and performing better than the CPSS and molecular CPSS scores in differentiating patient outcomes. The consistent performance of the AIPSS-MDS across both cohorts highlights its generalizability. Its adoption as a valuable tool for personalized treatment decision-making in CMML enables clinicians to identify high-risk patients who may benefit from different therapeutic interventions. Future studies should explore the integration of genetic information into the AIPSS-MDS to further refine risk stratification in CMML and improve patient outcomes. |
URI: | https://www.scopus.com/record/display.uri?eid=2-s2.0-85186562544&doi=10.1111%2fbjh.19341&origin=inward&txGid=32d097b1b9cd53fa26e3f572a1766ece https://scholars.lib.ntu.edu.tw/handle/123456789/641594 |
ISSN: | 00071048 | DOI: | 10.1111/bjh.19341 |
顯示於: | 醫學系 |
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