Abstract：
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.
摘要：
慢性粒单核细胞白血病（CMML）是一种罕见的血液系统疾病，其特征是骨髓细胞谱系中的单核细胞增多和增生异常。准确的风险分层对于指导治疗决策和评估预后至关重要。这项研究旨在验证CMML中骨髓增生异常综合征的人工智能预后评分系统（AIPSS-MDS），并使用西班牙注册表（n=1343）和台湾医院（n=75）的数据评估其与传统评分相比的表现。在西班牙队列中，AIPSS-MDS准确预测总生存期(OS)和无白血病生存期(LFS)，优于修订版IPSS评分。同样，在台湾队列中，AIPSS-MDS展示了对OS和LFS的准确预测，显示优于IPSS评分,并且在区分患者结局方面优于CPSS和分子CPSS评分.AIPSS-MDS在两个队列中的一致表现突出了其普遍性。它作为CMML中个性化治疗决策的有价值的工具，使临床医生能够识别可能受益于不同治疗干预措施的高风险患者。未来的研究应探索将遗传信息整合到AIPSS-MDS中，以进一步完善CMML的风险分层并改善患者预后。
