通过比较治疗获益改善个体治疗：宫颈癌人工智能生存分析系统。
Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma.
机构信息
Department of Gynaecology, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China.
Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China.
出版信息
J Transl Med. 2022 Jun 28;20(1):293. doi: 10.1186/s12967-022-03491-8.
PURPOSE
The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments.
METHODS
Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients.
RESULTS
Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study.
CONCLUSION
The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/ .
目的
本研究旨在构建一种新的癌症人工智能生存分析系统，以预测接受不同治疗的宫颈癌患者的个体死亡风险曲线。
方法
从监测、流行病学和最终结果数据库中下载研究数据集（n=14946）。使用加速失效时间算法、多任务逻辑回归算法和 Cox 比例风险回归算法为宫颈癌患者的癌症特异性生存建立预测模型。
结果
多变量 Cox 回归确定了分期、PM、化疗、年龄、PT 和放疗/手术是宫颈癌患者的独立影响因素。在模型数据集，Cox 模型的一致性指数为 12 个月、36 个月和 60 个月分别为 0.860、0.849 和 0.848，而在验证数据集分别为 0.881、0.845 和 0.841。在模型数据集，加速失效时间模型的一致性指数为 12 个月、36 个月和 60 个月分别为 0.861、0.852 和 0.851，而在验证数据集分别为 0.882、0.847 和 0.846。多任务逻辑回归模型的一致性指数为 12 个月、36 个月和 60 个月分别为 0.860、0.863 和 0.861，而在验证数据集分别为 0.880、0.860 和 0.861。Brier 评分表明，这三种预测模型对宫颈癌患者具有良好的诊断准确性。本研究缺乏独立的外部验证研究。
结论
本研究开发了一种新的癌症人工智能生存分析系统，该系统基于三种不同的人工智能算法为宫颈癌患者提供个体死亡风险预测曲线。癌症人工智能生存分析系统可以在四个阶段为特定时间点提供死亡率百分比，并探索不同治疗方案下的实际治疗效果，这有助于患者确定最佳的个体化治疗方案。癌症人工智能生存分析系统可在以下网址获得：https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/。
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