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使用多种机器学习和深度学习技术预测患有骨髓瘤和尤文瘤的儿科和青少年群体的发病率趋势,整体存活率和转移:名图和网页

Chengyuan Zhou¹, Han Li¹, Hao Zeng¹

¹Department of Oncology, The Affiliated Hospital of Southwest Medical University, 25 TAIPING Street, Luzhou City, 646000, Sichuan Province, China.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

|September 27, 2024

概括

机器学习和深度学习模型预测了儿科骨髓瘤 (OSC) 和尤英骨髓瘤的转移.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
计算生物学计算生物学

背景情况:

骨肉瘤 (OSC) 和尤文肉瘤 (EWS) 是一种罕见的骨癌,影响儿童和青少年群体.
准确预测转移对于有效的治疗规划和改善患者结果至关重要.
传统的预后方法可能无法完全捕捉这些癌症中转移风险的复杂性.

研究的目的:

分析儿科和青少年骨髓瘤 (OSC) 和尤金肉瘤 (EWS) 的发生率和整体存活率 (OS).
开发和评估机器学习 (ML) 和深度学习 (DL) 模型,用于预测OSC和EWS中的转移.
创建一个可访问的临床工具来预测生存结果.

主要方法:

利用了2004-2020年间2465名OSC和1373名EWS患者 (年龄在0-19岁) 的数据.
采用ML技术 (Lasso,Ridge,弹性网,随机森林) 和DL模型 (TensorFlow,Keras) 进行转移预测.
通过网格搜索,交叉验证,变量重要性的SHAP值验证模型,并开发了一个在线名录.

主要成果:

观察到2004年至2020年间,OSC和EWS的年龄调整发病率呈显著上升趋势.
与随机森林相比,深度学习模型在稳定损失和准确性方面表现优异.
开发的诺米图准确地预测了生存率,有效地区分了高风险和低风险的患者群体.

结论:

增加的发病率需要持续的研究和加强儿科OSC和EWS的治疗策略.
ML和DL模型为预测远程转移和改善年轻患者的预后提供了有价值的工具.
在线名谱增强了临床实用性,提供了一个可访问的方法来预测生存结果.

关键词:

尤文的肉瘤是什么?机器学习是机器学习.骨质肉瘤骨质肉瘤是什么

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