Interactive web application for plotting personalized prognosis prediction curves in allogeneic hematopoietic cell transplantation using machine learning
Interactive web application for plotting personalized prognosis prediction curves in allogeneic hematopoietic cell transplantation using machine learning
复制标题
使用机器学习绘制同种异体造血细胞移植中个性化预后预测曲线的交互式网络应用程序
DOI：
10.1097/tp.0000000000003357
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发表时间：
2020
期刊：
影响因子：
6.2
通讯作者：
Nakamae Hirohisa
中科院分区：
文献类型：
--
作者：
Okamura Hiroshi;Nakamae Mika;Koh Shiro;Nanno Satoru;Nakashima Yasuhiro;Koh Hideo;Nakane Takahiko;Hirose Asao;Hino Masayuki;Nakamae Hirohisa
背景：异基因造血细胞移植（allo-HCT）是恶性血液病的治愈性治疗选择。移植临床医生在临床实践中根据以前对类似患者的研究经验估计患者特异性预后。然而，这种方法并不能提供客观的数据。本研究的主要目的是开发一种工具，能够提供准确的个性化预后预测allo-HCT后，在客观的mathematics.Methods.We开发了一个交互式的网络应用程序工具，图形用户界面能够绘制个性化的生存和累积发病率预测曲线allo-HCT调整后的8个患者特异性因素，这被称为预后预测因子，并评估其预测性能。使用在我们机构接受allo-HCT的患者数据的随机生存森林模型被应用于开发此application.Results.我们成功地使用我们的网络应用程序（https：//predicted-os-after-transplantation）交互显示了1年总生存率、无进展生存率、复发/进展和非复发死亡率（NRM）的个性化预后预测曲线。shinyapps. io/RSF_model/）。为了评估其预测性能，整个队列（363例）根据患者的移植日期按时间顺序分为训练队列（70%）和测试队列（30%）。受试者1年总生存率、无进展生存率、复发/进展率和非复发死亡率的工作特征曲线下面积分别为0.70、0.72、0.73和0.77，respectively.The new web application can allow transplant clinical to inform a new allo-HCT candidate of the objective personalized prognostic prediction and facilitate decision-making.
Background.Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative treatment option for malignant hematological disorders. Transplant clinicians estimate patient-specific prognosis empirically in clinical practice based on previous studies on similar patients. However, this approach does not provide objective data. The present study primarily aimed to develop a tool capable of providing accurate personalized prognosis prediction after allo-HCT in an objective manner.Methods.We developed an interactive web application tool with a graphical user interface capable of plotting the personalized survival and cumulative incidence prediction curves after allo-HCT adjusted by 8 patient-specific factors, which are known as prognostic predictors, and assessed their predictive performances. A random survival forest model using the data of patients who underwent allo-HCT at our institution was applied to develop this application.Results.We succeeded in showing the personalized prognosis prediction curves of 1-year overall survival, progression-free survival, relapse/progression, and nonrelapse mortality (NRM) interactively using our web application (https://predicted-os-after-transplantation. shinyapps. io/RSF_model/). To assess its predictive performance, the entire cohort (363 cases) was split into a training cohort (70%) and a test cohort (30%) time-sequentially based on the patients’ transplant dates. The areas under the receiver-operating characteristic curves for 1-year overall survival, progression-free survival, relapse/progression, and nonrelapse mortality in test cohort were 0.70, 0.72, 0.73, and 0.77, respectively.Conclusions.The new web application could allow transplant clinicians to inform a new allo-HCT candidate of the objective personalized prognosis prediction and facilitate decision-making.