Cancer Emulation Analysis with Deep Neural Network

使用深度神经网络进行癌症仿真分析

• 批准号: 10725293
• 负责人: Shuangge Ma
• 金额: $ 16.75万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10725293
• 关键词: Address; Architecture; Cancer Prognosis; Cardiovascular Diseases; Case Study; Clinical; Colonoscopy; Colorectal Cancer; Complement; Complex; Computer software; Computerized Medical Record; Data; Data Analyses; Databases; Development; Devices; Disease; Elderly; Ensure; Erlotinib; Excision; Fluorouracil; Future; Infrastructure; Literature; Lobectomy; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Medical Records; Medicare; Methodology; Methods; Modeling; Network-based; Non-Small-Cell Lung Carcinoma; Observational Study; Operative Surgical Procedures; Outcome; Paclitaxel; Performance; Pharmaceutical Preparations; Policy Research; Population; Procedures; Public Policy; Publishing; Pythons; Radical Prostatectomy; Reproducibility; Research Design; Resources; Survival Analysis; Techniques; Testing; United States Department of Veterans Affairs; advanced pancreatic cancer; cancer diagnosis; cancer survival; clinical practice; clinical trial analysis; clinically significant; comparative effectiveness; cooperative study; cost effective; data access; data warehouse; deep learning; deep neural network; design; effectiveness research; experience; flexibility; gemcitabine; insurance claims; neglect; programs; prototype; randomized, clinical trials; relative effectiveness; screening; simulation; software development; success; treatment effect; user friendly software

Project Summary To objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on cancer prognosis, rigorously designed and executed randomized clinical trials (RCTs) remain the gold standard. However, as exemplified in this application and many published studies, RCTs are not always feasible. Fortunately, the fast development of electronic medical records and insurance claims databases has made it possible to mine a large amount of observational data and efficiently complement RCTs. This strategy has been enthusiastically endorsed by multiple national organizations. Among the available observational data analysis techniques that aim to draw RCT-type conclusions, emulation has emerged as especially appealing, with its trial- like architecture, interpretability, and scalability. It has been applied to multiple cancers and other complex diseases and led to clinically significant findings. This study has two equally important aims. The first aim is to develop deep neural network (DNN)-based emulation analysis methods and software. Most of the existing emulation analyses are based on classic regression techniques. Compared to regression, DNN excels with superior model fitting and higher flexibility. Recently, our group was the first to develop a DNN-based emulation analysis approach and applied it to cardiovascular diseases. Advancing from this recent success, we will develop more interpretable and more stable DNNs tailored to RCT analysis. We will then further expand the analysis scope and conduct DNN-based analysis of a sequence of emulated trials. For both a single emulated trial and a sequence of trials, we will develop valid inference, which is essential for RCT analysis but has been neglected in most DNN studies. User- friendly software will be developed. This methodological development will substantially expand the scope of emulation analysis, deep learning, causal inference, observation data analysis, and medical record/insurance claims data analysis. The second aim is to develop and analyze two emulated trials. We will address the comparative effectiveness of (a) lobectomy and limited resection on lung cancer survival for the SEER-Medicare elderly population, and (b) radical prostatectomy and observation on localized prostate cancer survival for the VA population. The findings will be comprehensively and rigorously evaluated. To provide a more comprehensive picture, we will also analyze using multiple alternative methods and compare against existing RCTs and observational studies. With the significant methodological advancements and powerful data, our analysis will lead to more definitive findings, directly inform clinical practice, and serve as the prototype for future applications.

项目总结