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使用人工智能模型预测喉状细胞癌患者的存活率

Nayeon Choi¹, Junghyun Kim², Heejun Yi¹

¹Department of Otorhinolaryngology, Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.

Scientific reports

|June 15, 2023

概括

人工智能 (AI) 通过分析超越TNM分期的临床因素,准确地预测喉状状细胞癌 (LSCC) 的生存率. 深度神经网络 (DNN) 为个性化的患者结果提供了卓越的准确性.

科学领域:

在瘤学瘤学.
人工智能在医学中的应用
生物统计学生物统计学

背景情况:

传统的TNM分期缺乏喉皮状细胞癌 (LSCC) 的个性化生存预测.
临床因素,如表现状况,年龄,性别和吸烟状况显著影响患者的生存率.
需要更精确的方法来预测LSCC患者的整体存活率.

研究的目的:

利用人工智能 (AI) 来分析各种临床因素,以准确预测LSCC患者的存活率.
为了比较不同的AI模型的预测性能,包括深度神经网络 (DNN),随机生存森林 (RSF) 和Cox比例危险 (COX-PH) 模型.

主要方法:

分析了2002年至2020年期间接受治疗的1026名LSCC患者的队列.
收集了临床数据,包括年龄,性别,吸烟,酒精消费,ECOG性能状态,瘤位置,TNM阶段和治疗方法.
用多分类和回归的深度神经网络 (DNN),RSF和COX-PH模型用于整体生存预测,使用五倍交叉验证进行验证.

主要成果:

使用多分类模型的DNN实现了最高的预测功率 (C指数:0.859 ± 0.018),显示了预测和验证生存曲线之间的强烈一致.
经过回归的DNN也表现出强的性能 (C指数:0.893 ± 0.017),表现优于传统的分期方法.
仅使用T/N分期的DNN模型表现出最差的生存预测准确度.

结论:

纳入各种临床因素对于准确预测LSCC患者的存活率至关重要.
具有多类分析的深度神经网络 (DNN) 是提高LSCC生存预测准确性的有效方法.
人工智能驱动的生存预测有可能改善LSCC的瘤结果.

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