Halilaj Iva, Oberije Cary, Chatterjee Avishek, van Wijk Yvonka, Rad Nastaran Mohammadian, Galganebanduge Prabash, Lavrova Elizaveta, Primakov Sergey, Widaatalla Yousif, Wind Anke, Lambin Philippe
The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The Netherlands.
Health Innovation Ventures, 6229 EV Maastricht, The Netherlands.
Biomedicines. 2022 Oct 23;10(11):2679. doi: 10.3390/biomedicines10112679.
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models.
(1) 背景：主要目标是开发一个原型应用程序，作为关于肿瘤学的预测和预后模型的精选子集的开源存储库，并为所纳入的模型提供一个用户友好的界面以进行在线计算。该应用程序的重点是为医生和健康专业人员提供针对不同预期治疗方案的患者特定信息，包括治疗计划、生存率和副作用。(2) 方法：主要使用的模型是我们研究小组过去开发的那些模型。通过在PubMed和Medline数据库中进行文献检索，从多个针对相同癌症类型但关注其他结果的模型中完成了这一选择。所有选定的模型都是公开可用的，并且已经按照TRIPOD（个体预后或诊断预测模型研究的透明报告）3型或2b型进行了验证。(3) 结果：该开源存储库目前包含来自不同研究小组的18个模型，这些模型在来自不同国家的数据集上进行了评估。模型类型包括逻辑回归、Cox回归和递归划分分析（决策树）。(4) 结论：开发了一个应用程序，使医生能够使用经过内部/外部验证的模型，通过用户友好的患者特定预测来补充他们的临床判断。此外，这个平台使研究人员能够展示他们的工作，增加其模型的使用和曝光度。
