一个具有良好的预测效率的深度学习算法,用于骨髓瘤癌症特异性生存:一项回顾性研究
Yang Liu1, Lang Xie2, Dingxue Wang3
1Department of Orthopedics, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.
PloS one
|September 28, 2023
概括
这项研究开发了一种深度学习模型 (DeepSurv) 和一个Cox模型,用于预测骨肉瘤 (OSC) 患者的癌症特异性生存率 (CSS). 两种模型都显示出良好的预测效率,DeepSurv提供了一个用户友好的计算器.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 准确的预后对于有效的骨髓瘤 (OSC) 管理和治疗至关重要.
- 个性化治疗策略需要可靠地预测癌症特异性生存率 (CSS).
- 深度学习和传统的统计模型为提高预测准确性提供了潜力.
研究的目的:
- 预测癌症特异性存活率 (CSS) 在骨髓瘤 (OSC) 患者.
- 为了比较深度学习 (DeepSurv) 和Cox比例危险模型的有效性,用于OSC预后.
- 通过准确的生存预测,支持OSC患者的个性化治疗决策.
主要方法:
- 利用来自监测,流行病学和最终结果 (SEER) 数据库的3218名骨肉瘤患者 (2004-2017) 的数据.
- 随机分配患者进入培训 (70%) 和验证 (30%) 队列.
- 使用DeepSurv算法和Cox比例危险模型开发预测模型,使用C-index,IBS,RMSE和SME进行评估.
主要成果:
- 无论是DeepSurv还是Cox模型,在OSC患者中,CSS的预测性能都很强,C指数超过0.74.
- 根据验证指标,DeepSurv模型在预测生存率方面没有显著优于Cox模型.
- 总共有3218名患者被纳入,分为培训 (n=2252) 和验证 (n=966) 组.
结论:
- 基于DeepSurv算法的骨质肉瘤患者的CSS预测模型在验证后取得了令人满意的预测效率.
- 开发的DeepSurv模型提供了一个实用且方便的网页计算器,用于预测OSC的癌症特异性生存率.
- 这项研究强调了DeepSurv等先进算法的实用性,以及在瘤学中强大的预后建模的传统方法.
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