Abstract：
BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual\'s cancer risk, it proposes thresholds to stratify them into very-low-risk (<1%), low-risk (1-<5%), intermediate-risk (5-<20%), and high-risk (≥20%) groups.
OBJECTIVE: To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms.
METHODS: Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed.
METHODS: The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined.
CONCLUSIONS: There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups.
CONCLUSIONS: The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer.
RESULTS: We previously developed a calculator that predicts patients\' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly.
OBJECTIVE: To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms.
METHODS: Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed.
METHODS: The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined.
CONCLUSIONS: There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups.
CONCLUSIONS: The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer.
RESULTS: We previously developed a calculator that predicts patients\' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly.
摘要：
背景：IDENTIFY研究开发了一种模型，可以使用来自大型多中心的患者特征来预测尿路癌症，转诊为血尿的国际患者队列。除了计算一个人的癌症风险，它提出了将它们分层为非常低风险(<1%)的阈值，低风险(1-<5%)，中等风险(5-<20%)，高危人群（≥20%）。
目的：外部验证IDENTIFY血尿风险计算器，并将传统回归与机器学习算法进行比较。
方法：收集新出现血尿的二级护理患者的前瞻性数据。收集了IDENTIFY风险计算器中包含的患者变量的数据，癌症结果，和TNM分期。使用机器学习方法来评估是否存在比传统回归方法开发的模型更好的模型。
方法：用于检测尿路癌的受试者工作特征曲线下面积（AUC），校准系数,大型校准(CITL)，和Brier得分确定。
结论：在验证队列中有3582名患者。开发和验证队列匹配良好。验证队列的鉴定风险计算器的AUC为0.78。仅在尿路上皮癌流行国家的亚分析中，这一数字就提高到0.80，校准斜率为1.04，CITL为0.24，Brier评分为0.14。最好的机器学习模型是随机森林，在验证队列中实现了0.76的AUC。在验证队列中，没有将癌症分层为极低风险组。大多数癌症被分为中危和高危人群，高风险人群中的癌症更具侵略性。
结论：在外部验证中，IDENTIFY风险计算器在预测血尿患者的癌症方面表现良好。泌尿科医师可以使用此工具更好地指导患者患癌症的风险，将诊断资源优先用于适当的患者，并避免在癌症风险非常低的人群中进行不必要的侵入性手术。
结果：我们以前开发了一种计算器，可以预测患者尿液中有血液时的癌症风险，基于他们的个人特征。我们已经验证了这个风险计算器，通过对一组单独的患者进行测试，以确保其按预期工作。大多数被发现患有癌症的患者往往属于高风险人群，并且患有更具侵略性的癌症类型，风险更高。临床医生可以使用此工具根据计算器快速跟踪高风险患者，并对其进行更彻底的调查。
目的：外部验证IDENTIFY血尿风险计算器，并将传统回归与机器学习算法进行比较。
方法：收集新出现血尿的二级护理患者的前瞻性数据。收集了IDENTIFY风险计算器中包含的患者变量的数据，癌症结果，和TNM分期。使用机器学习方法来评估是否存在比传统回归方法开发的模型更好的模型。
方法：用于检测尿路癌的受试者工作特征曲线下面积（AUC），校准系数,大型校准(CITL)，和Brier得分确定。
结论：在验证队列中有3582名患者。开发和验证队列匹配良好。验证队列的鉴定风险计算器的AUC为0.78。仅在尿路上皮癌流行国家的亚分析中，这一数字就提高到0.80，校准斜率为1.04，CITL为0.24，Brier评分为0.14。最好的机器学习模型是随机森林，在验证队列中实现了0.76的AUC。在验证队列中，没有将癌症分层为极低风险组。大多数癌症被分为中危和高危人群，高风险人群中的癌症更具侵略性。
结论：在外部验证中，IDENTIFY风险计算器在预测血尿患者的癌症方面表现良好。泌尿科医师可以使用此工具更好地指导患者患癌症的风险，将诊断资源优先用于适当的患者，并避免在癌症风险非常低的人群中进行不必要的侵入性手术。
结果：我们以前开发了一种计算器，可以预测患者尿液中有血液时的癌症风险，基于他们的个人特征。我们已经验证了这个风险计算器，通过对一组单独的患者进行测试，以确保其按预期工作。大多数被发现患有癌症的患者往往属于高风险人群，并且患有更具侵略性的癌症类型，风险更高。临床医生可以使用此工具根据计算器快速跟踪高风险患者，并对其进行更彻底的调查。
