预测模型用于早期骨髓增殖性新生瘤的早期诊断,未另有说明. 来自机器学习研究的证据
Anna Scattone1, Andrea Lupo2, Concetta Saponaro1
1Pathology Department, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy.
Frontiers in oncology
|February 11, 2026
概括
人工智能 (AI) 在使用骨髓活检图像诊断骨髓扩散性瘤 (MPNs) 方面表现有前途. 一个人工智能模型在分类MPN亚型时实现了60%的准确性,帮助了血液学家.
科学领域:
- 血液学 血液学 血液学
- 计算病理学计算病理学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 在诊断血液疾病方面越来越有价值.
- 骨髓活检 (BMBs) 的整体幻灯片成像 (WSI) 提供了高分辨率的视图,以改善解释.
- 人工智能的采用可能会增强疑似早期骨髓增殖性瘤 (MPNs) 的BMB分析.
研究的目的:
- 开发和评估基于人工智能的MPN亚型的分类模型,使用BMB的WSI.
- 评估模型在区分前纤维细胞原发性髓纤维化 (pre-PMF),真多细胞血症 (PV) 和基本血小板血 (ET) 的准确性.
- 为了测试模型的性能在MPN未另有说明 (MPN-NOS) 的情况下.
主要方法:
- 分析了88名MPN患者的H&E染色的BMB的回顾性数据集.
- 人工智能被用来计算细胞性,细胞密度和巨核细胞形态特征.
- 一个支持矢量机 (SVM) 分类器被训练并使用5倍交叉验证进行验证.
主要成果:
- 人工智能模型实现了0.70.的平均多类曲线下面面积 (AUC).
- 该模型在分类MPN亚型时表现出0.60的准确性.
- 该模型与病理学家对MPN-NOS病例的分类一致61.5%的时间.
结论:
- 这项研究是迈向MPN诊断自动化工具的初步步骤.
- 人工智能辅助的BMB分析可能有助于血液学家和病理学家.
- 进一步开发人工智能工具可能会改善MPN患者的临床管理.
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