Stagno Fabio, Russo Sabina, Murdaca Giuseppe, Mirabile Giuseppe, Alvaro Maria Eugenia, Nasso Maria Elisa, Zemzem Mohamed, Gangemi Sebastiano, Allegra Alessandro
Division of Hematology, Department of Human Pathology in Adulthood and Childhood "Gaetano Barresi", University of Messina, Via Consolare Valeria, 98125 Messina, Italy.
Department of Internal Medicine, University of Genova, 16126 Genova, Italy.
Int J Mol Sci. 2025 Mar 12;26(6):2535. doi: 10.3390/ijms26062535.
Chronic myeloid leukemia is a clonal hematologic disease characterized by the presence of the Philadelphia chromosome and the BCR::ABL1 fusion protein. Integrating different molecular, genetic, clinical, and laboratory data would improve the diagnostic, prognostic, and predictive sensitivity of chronic myeloid leukemia. However, without artificial intelligence support, managing such a vast volume of data would be impossible. Considering the advancements and growth in machine learning throughout the years, several models and algorithms have been proposed for the management of chronic myeloid leukemia. Here, we provide an overview of recent research that used specific algorithms on patients with chronic myeloid leukemia, highlighting the potential benefits of adopting machine learning in therapeutic contexts as well as its drawbacks. Our analysis demonstrated the great potential for advancing precision treatment in CML through the combination of clinical and genetic data, laboratory testing, and machine learning. We can use these powerful research instruments to unravel the molecular and spatial puzzles of CML by overcoming the current obstacles. A new age of patient-centered hematology care will be ushered in by this, opening the door for improved diagnosis accuracy, sophisticated risk assessment, and customized treatment plans.
慢性髓性白血病是一种克隆性血液系统疾病，其特征是存在费城染色体和BCR::ABL1融合蛋白。整合不同的分子、遗传、临床和实验室数据将提高慢性髓性白血病的诊断、预后和预测敏感性。然而，没有人工智能的支持，管理如此大量的数据是不可能的。考虑到这些年来机器学习的进步和发展，已经提出了几种用于管理慢性髓性白血病的模型和算法。在这里，我们概述了最近对慢性髓性白血病患者使用特定算法的研究，强调了在治疗背景下采用机器学习的潜在益处及其缺点。我们的分析表明，通过结合临床和遗传数据、实验室检测以及机器学习，在慢性髓性白血病中推进精准治疗具有巨大潜力。我们可以利用这些强大的研究工具，克服当前的障碍，解开慢性髓性白血病的分子和空间谜题。由此将迎来以患者为中心的血液学护理新时代，为提高诊断准确性、完善风险评估和定制治疗方案打开大门。
