[基于人工智能的鼻咽癌患者死亡风险预测模型的构建与评估]
[Construction and evaluation of an artificial intelligence-based risk prediction model for death in patients with nasopharyngeal cancer].
作者信息
Zhang H, Lu J, Jiang C, Fang M
机构信息
Department of Human Anatomy, Bengbu Medical College, Bengbu 233030, China.
Anhui Provincial Key Laboratory of Digital Medicine and Smart Health, Bengbu Medical College, Bengbu 233030, China.
出版信息
Nan Fang Yi Ke Da Xue Xue Bao. 2023 Feb 20;43(2):271-279. doi: 10.12122/j.issn.1673-4254.2023.02.16.
OBJECTIVE
To screen the risk factors for death in patients with nasopharyngeal carcinoma (NPC) using artificial intelligence (AI) technology and establish a risk prediction model.
METHODS
The clinical data of NPC patients obtained from SEER database (1973-2015). The patients were randomly divided into model building and verification group at a 7∶3 ratio. Based on the data in the model building group, R software was used to identify the risk factors for death in NPC patients using 4 AI algorithms, namely eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Least absolute shrinkage and selection operator (LASSO) and random forest (RF), and a risk prediction model was constructed based on the risk factor identified. The C-Index, decision curve analysis (DCA), receiver operating characteristic (ROC) curve and calibration curve (CC) were used for internal validation of the model; the data in the validation group and clinical data of 96 NPC patients (collected from First Affiliated Hospital of Bengbu Medical College) were used for internal and external validation of the model.
RESULTS
The clinical data of a total of 2116 NPC patients were included (1484 in model building group and 632 in verification group). Risk factor screening showed that age, race, gender, stage M, stage T, and stage N were all risk factors of death in NPC patients. The risk prediction model for NPC-related death constructed based on these factors had a C-index of 0.76 for internal evaluation, an AUC of 0.74 and a net benefit rate of DCA of 9%-93%. The C-index of the model in internal verification was 0.740 with an AUC of 0.749 and a net benefit rate of DCA of 3%-89%, suggesting a high consistency of the two calibration curves. In external verification, the C-index of this model was 0.943 with a net benefit rate of DCA of 3%-97% and an AUC of 0.851, and the predicted value was consistent with the actual value.
CONCLUSIONS
Gender, age, race and TNM stage are risk factors of death of NPC patients, and the risk prediction model based on these factors can accurately predict the risks of death in NPC patients.
目的
利用人工智能（AI）技术筛选鼻咽癌（NPC）患者的死亡危险因素，并建立风险预测模型。
方法
从SEER数据库（1973 - 2015年）获取NPC患者的临床数据。患者按7∶3的比例随机分为模型构建组和验证组。基于模型构建组的数据，使用R软件通过4种AI算法，即极端梯度提升（XGBoost）、决策树（DT）、最小绝对收缩和选择算子（LASSO）以及随机森林（RF），识别NPC患者的死亡危险因素，并基于所识别的危险因素构建风险预测模型。采用C指数、决策曲线分析（DCA）、受试者工作特征（ROC）曲线和校准曲线（CC）对模型进行内部验证；验证组的数据以及96例NPC患者（从蚌埠医学院第一附属医院收集）的临床数据用于模型的内部和外部验证。
结果
共纳入2116例NPC患者的临床数据（模型构建组1484例，验证组632例）。危险因素筛选显示，年龄、种族、性别、M分期、T分期和N分期均为NPC患者的死亡危险因素。基于这些因素构建的NPC相关死亡风险预测模型内部评估的C指数为0.76，AUC为0.74，DCA的净获益率为9% - 93%。模型在内部验证中的C指数为0.740，AUC为0.749，DCA的净获益率为... 显示两条校准曲线具有高度一致性。在外部验证中，该模型的C指数为0.943，DCA的净获益率为3% - 97%，AUC为0.851，预测值与实际值一致。
结论
性别、年龄、种族和TNM分期是NPC患者死亡的危险因素，基于这些因素的风险预测模型能够准确预测NPC患者的死亡风险。 （注：原文中“DCA的净获益率为...”处，原文可能有遗漏信息未完整呈现）
Zotero 插件