Development of a Prognostic App (iCanPredict) to Predict Survival for Chinese Women With Breast Cancer: Retrospective Study.
Development of a Prognostic App (iCanPredict) to Predict Survival for Chinese Women With Breast Cancer: Retrospective Study.
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DOI：
10.2196/35768
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发表时间：
2022-03-09
影响因子：
7.4
通讯作者：
Zhu J
中科院分区：
文献类型：
--
作者：
Ma Z;Huang S;Wu X;Huang Y;Chan SW;Lin Y;Zheng X;Zhu J
准确预测生存期对于医生和乳腺癌患者至关重要，以便做出适当治疗的临床决策。目前可用的生存预测工具是基于从特定人群中获得的人口统计学和临床数据开发的，可能低估或高估中国乳腺癌女性的生存率。本研究旨在开发和验证一种预测中国乳腺癌女性总生存率的预后应用程序。收集2009年1月至2017年12月厦门市2家医院接受手术和辅助治疗的乳腺癌女性患者的9年（2009年1月至2017年12月）临床数据，并与厦门市疾病预防控制中心的死亡数据进行匹配。将所有样本随机分为（7：3比例）用于模型构建的训练集和用于模型外部验证的测试集。采用多因素考克斯回归分析建立生存预测模型。通过受试者工作特征曲线（ROC）和Brier评分评价模型的性能。最后，通过在应用程序后台线程中运行生存预测模型，为中国乳腺癌女性开发了名为iCanPredict的预后应用程序。共纳入1592份样本进行数据分析。训练集包括1114个人，测试集包括478个人。诊断时的年龄、临床分期、分子分类、手术类型、腋窝淋巴结清扫、化疗和内分泌治疗被纳入模型，其中诊断时的年龄（风险比[HR] 1.031，95% CI 1.011-1.051; P=.002），临床分期（HR 3.044，95%CI 2.347-3.928; P<0.001）和内分泌治疗（HR 0.592，95%CI 0.384-0.914; P= 0.02）显著影响乳腺癌妇女的生存率。手术类型（P= 0.81）和其他4个变量（分子分类[P= 0.91]、乳房再造[P= 0.36]、腋窝淋巴结清扫[P= 0.32]和化疗[P= 0.84]）无显著性。训练集的ROC曲线显示，该模型在预测1年（曲线下面积[AUC] 0.802，95% CI 0.713-0.892）、5年（AUC 0.813，95% CI 0.760-0.865）和10年（AUC 0.740，95% CI 0.672-0.808）总生存期方面表现出良好的区分度。诊断后1年、5年和10年的Brier评分在训练集中分别为0.005、0.055和0.103，均小于0.25，表明具有良好的预测能力。测试集外部验证模型鉴别和校准。在iCanPredict应用程序中，当医生或女性输入女性的临床信息及其手术和辅助治疗的选择时，将呈现相应的10年生存预测。该生存预测模型提供了良好的模型鉴别和校准。iCanPredict是中国第一个为乳腺癌女性提供生存预测的同类工具。iCanPredict将提高女性对不同手术的相似生存率以及坚持内分泌治疗的重要性的认识，最终帮助女性做出关于乳腺癌治疗的明智决定。
Accurate prediction of survival is crucial for both physicians and women with breast cancer to enable clinical decision making on appropriate treatments. The currently available survival prediction tools were developed based on demographic and clinical data obtained from specific populations and may underestimate or overestimate the survival of women with breast cancer in China. This study aims to develop and validate a prognostic app to predict the overall survival of women with breast cancer in China. Nine-year (January 2009-December 2017) clinical data of women with breast cancer who received surgery and adjuvant therapy from 2 hospitals in Xiamen were collected and matched against the death data from the Xiamen Center of Disease Control and Prevention. All samples were randomly divided (7:3 ratio) into a training set for model construction and a test set for model external validation. Multivariable Cox regression analysis was used to construct a survival prediction model. The model performance was evaluated by receiver operating characteristic (ROC) curve and Brier score. Finally, by running the survival prediction model in the app background thread, the prognostic app, called iCanPredict, was developed for women with breast cancer in China. A total of 1592 samples were included for data analysis. The training set comprised 1114 individuals and the test set comprised 478 individuals. Age at diagnosis, clinical stage, molecular classification, operative type, axillary lymph node dissection, chemotherapy, and endocrine therapy were incorporated into the model, where age at diagnosis (hazard ratio [HR] 1.031, 95% CI 1.011-1.051; P=.002), clinical stage (HR 3.044, 95% CI 2.347-3.928; P<.001), and endocrine therapy (HR 0.592, 95% CI 0.384-0.914; P=.02) significantly influenced the survival of women with breast cancer. The operative type (P=.81) and the other 4 variables (molecular classification [P=.91], breast reconstruction [P=.36], axillary lymph node dissection [P=.32], and chemotherapy [P=.84]) were not significant. The ROC curve of the training set showed that the model exhibited good discrimination for predicting 1- (area under the curve [AUC] 0.802, 95% CI 0.713-0.892), 5- (AUC 0.813, 95% CI 0.760-0.865), and 10-year (AUC 0.740, 95% CI 0.672-0.808) overall survival. The Brier scores at 1, 5, and 10 years after diagnosis were 0.005, 0.055, and 0.103 in the training set, respectively, and were less than 0.25, indicating good predictive ability. The test set externally validated model discrimination and calibration. In the iCanPredict app, when physicians or women input women’s clinical information and their choice of surgery and adjuvant therapy, the corresponding 10-year survival prediction will be presented. This survival prediction model provided good model discrimination and calibration. iCanPredict is the first tool of its kind in China to provide survival predictions to women with breast cancer. iCanPredict will increase women’s awareness of the similar survival rate of different surgeries and the importance of adherence to endocrine therapy, ultimately helping women to make informed decisions regarding treatment for breast cancer.
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影响因子：
168.9
作者：
Davies, Christina;Pan, Hongchao;Godwin, Jon;Gray, Richard;Arriagada, Rodrigo;Raina, Vinod;Abraham, Mirta;Medeiros Alencar, Victor Hugo;Badran, Atef;Bonfill, Xavier;Bradbury, Joan;Clarke, Michael;Collins, Rory;Davis, Susan R.;Delmestri, Antonella;Forbes, John F.;Haddad, Peiman;Hou, Ming-Feng;Inbar, Moshe;Khaled, Hussein;Kielanowska, Joanna;Kwan, Wing-Hong;Mathew, Beela S.;Mittra, Indraneel;Mueller, Bettina;Nicolucci, Antonio;Peralta, Octavio;Pernas, Fany;Petruzelka, Lubos;Pienkowski, Tadeusz;Radhika, Ramachandran;Rajan, Balakrishnan;Rubach, Maryna T.;Tort, Sera;Urrutia, Gerard;Valentini, Miriam;Wang, Yaochen;Peto, Richard
通讯作者：
Peto, Richard
影响因子：
45.3
作者：
Ravdin, PM;Siminoff, LA;Parker, HL
通讯作者：
Parker, HL
影响因子：
254.7
作者：
Giuliano, Armando E.;Connolly, James L.;Hortobagyi, Gabriel N.
通讯作者：
Hortobagyi, Gabriel N.
影响因子：
16.9
作者：
Kummerow, Kristy L.;Du, Liping;Hooks, Mary A.
通讯作者：
Hooks, Mary A.
影响因子：
51.1
作者：
Dieras, Veronique;Han, Hyo S.;Arun, Banu K.
通讯作者：
Arun, Banu K.