Survival prediction in patients with progressive fibrosing interstitial lung disease

进行性纤维化间质性肺病患者的生存预测

• 批准号: 10644030
• 负责人: HIROYUKI YOSHIDA
• 金额: $ 42万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644030
• 关键词: 3-Dimensional; Adverse drug effect; Artificial Intelligence; Biological Markers; Chest; Clinical; Clinical Trials; Data; Databases; Decision Making; Development; Disease; Disease Progression; Dyspnea; Evaluation; Fibrosis; Goals; High Resolution Computed Tomography; Image; Intervention; Learning; Logistic Regressions; Measures; Methods; Modeling; Network-based; Patient imaging; Patients; Performance; Prognosis; Prognostic Marker; Reproducibility; Scheme; Staging System; Survival Analysis; System; Testing; Therapeutic; Time; Training; comparative; cost; deep learning; design; fibrotic interstitial lung disease; generative adversarial network; imaging Segmentation; immunomodulatory therapies; improved; indexing; model development; mortality; network models; novel; novel therapeutics; personalized predictions; predictive modeling; prognostic; prognostic model; prognostic performance; pulmonary function; response; supervised learning; survival prediction

Project Summary/Abstract Progressive fibrosing interstitial lung disease (PF-ILD) is a group of diseases characterized by increasing self- sustaining fibrosis, progressive worsening of dyspnea, progressive decline in lung function, limited response to immunomodulatory therapies, and high mortality. Due to the highly variable rates of decline and poor prognosis, accurate individualized prognostic prediction of patients with PF-ILD is crucial for therapeutic decision making and management of the patients. However, no formal staging system based on prognosis has been established for PF-ILD. This is because, despite many attempts, none of the developed existing prognostic biomarkers have been found to be accurate enough for establishing such a staging system for PF-ILD. A clinically useful staging system for PF-ILD would enable many important clinical use cases, such as determining the timing and benefits of the currently available but costly therapies and interventions, identifying patients where treatment can be safely delayed to avoid potential adverse drug effects and costs, and identifying new therapies in clinical trials. Quantitative high-resolution computed tomography (HRCT) images have recently emerged as the most promising approach for providing accurate and reproducible biomarkers in PF-ILD patients, but current HRCT biomarkers have still yielded only mediocre predictive performances of 64-77% for patients with PF-ILD, as measured by the concordance index. Thus, there is an unmet clinical need for a prognostic biomarker that would predict the mortality and disease progression in PF-ILD patients at a high accuracy. Artificial intelligence (AI), especially deep learning, could be used to realize such a prognostic biomarker. In particular, a conditional generative adversarial network (cGAN) was recently shown to outperform traditional survival analysis methods in survival prediction, but there are no such cGAN-based methods to perform prognostic prediction from the image data of patients. In this project, we propose to develop an unsupervised image-based 3D cGAN model that would automatically estimate the distribution of the survival time directly from the HRCT images of patients for prognostic prediction. Our goal is to develop an integrated AI survival prediction model that will combine existing biomarkers with the image-based 3D cGAN model for performing accurate prognostic prediction in patients with PF-ILD. We hypothesize that the integrated AI model will yield a high performance (concordance index of ≥92%) in predicting the mortality and disease progression in PF-ILD patients. Successful development of the proposed integrated AI model will significantly improve the accuracy of the current state-of-the-art in the prognostic prediction of the mortality and disease progression in patients with PF-ILD, thereby ultimately making it possible to establish a formal staging system for enabling effective management of the patients with PF-ILD.

项目摘要/摘要 进行性纤维纤维间质肺疾病（PF-ILD）是一组疾病，其特征是增加自我 维持纤维化，呼吸困难的进行性担忧，肺功能的进行性下降，对 免疫调节疗法和高死亡率。由于下降速度高度变化和预后不良， PF-ILD患者的准确个性化预测对热决策至关重要 和患者的管理。但是，尚未建立基于预后的正式分期系统 用于PF-ild。这是因为，任务许多尝试，没有一个发达的现有预后生物标志物没有 发现我们足够准确，可以建立这样的PF-ILD的分期系统。临床上有用的分期 PF-ILD系统将使许多重要的临床用例都可以确定时间和福利 在当前可用但昂贵的疗法和干预措施中，确定可以治疗的患者 安全延迟以避免潜在的不良药物影响和成本，并在临床试验中识别新疗法。 定量高分辨率计算机断层扫描（HRCT）图像最近成为最多的 在PF-ILD患者中提供准确且可重复的生物标志物的有前途的方法，但是当前的HRCT 生物标志物仍然仅产生PF-ILD患者64-77％的MediaCre预测性能，AS 通过一致性指数衡量。那是对预后生物标志物的未满足的临床需求 以高精度预测PF-ild患者的死亡率和疾病进展。人工智能（AI）， 特别是深度学习，可以用来实现这种预后的生物标志物。特别是有条件的 最近显示，通用对抗网络（CGAN）胜过传统生存分析方法 在生存预测中，但是没有这样的基于CGAN的方法可以从 患者的图像数据。在这个项目中，我们建议开发一个基于图像的3D CGAN模型 这将直接从患者的HRCT图像中自动估计生存时间的分布 用于预后预测。我们的目标是开发一个集成的AI生存预测模型，该模型将结合起来 现有的生物标志物具有基于图像的3D CGAN模型，用于执行准确的预后预测 PF-ILD患者。我们假设集成的AI模型将产生高性能（一致性） 预测PF-ILD患者的死亡率和疾病进展的指数≥92％）。成功发展 在拟议的集成AI模型中，将显着提高当前最新技术的准确性 PF-ILD患者的死亡率和疾病进展的预后预测，从而最终使 可以建立一个正式的分期系统，以实现PF-ILD患者的有效管理。