使用深度学习预测皮肤恶性黑色素瘤患者的生存率:一项回顾性队列研究
Siyu Cai1, Wei Li2, Cong Deng3
1Dermatology Department, General Hospital of Western Theater Command PLA, No. 270, Rongdu Avenue, Chengdu, 610083, Sichuan, China.
Journal of cancer research and clinical oncology
|September 27, 2023
概括
一个新的深度学习生存模型,DeepCMM,准确地预测皮肤恶性黑色素瘤 (CMM) 患者的整体存活率. 该工具有助于对CMM预后的临床决策.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 人工智能在医学中的应用
背景情况:
- 皮肤性恶性黑色素瘤 (CMM) 的预后不好,特别是在转移性阶段.
- 准确的预后预测对于指导CMM的临床管理至关重要.
研究的目的:
- 开发和验证一种深度学习生存模型,用于预测CMM患者的整体存活率.
- 评估模型在不同患者队伍中的表现.
主要方法:
- 利用监测,流行病学和最终结果数据库来获取CMM患者数据.
- 开发了一个深度学习生存模型 (DeepCMM),在2010-2013年队列中进行训练.
- 在内部对2014年的数据进行验证,并在外部对2015年的数据进行验证.
主要成果:
- 在训练队伍中,DeepCMM在接收器运行特征曲线 (AUC) 下的面积为0.8270 .
- 该模型表现得一致,验证队列中的AUC为0.8274,测试队列中的AUC为0.8303.
- DeepCMM成功地被包装成一个用户友好的软件,用于临床应用.
结论:
- 深CMM模型为皮肤恶性黑色素瘤患者的生存提供了可靠的预测.
- 这种深度学习方法可以显著帮助CMM的预后评估.
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