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.

项目概要/摘要 进行性纤维化间质性肺疾病（PF-ILD）是一组以自身纤维化程度增加为特征的疾病。 持续纤维化、呼吸困难进行性恶化、肺功能进行性下降、对药物的反应有限 免疫调节疗法和高死亡率 由于衰退率的高度可变和预后不良， PF-ILD 患者准确的个体化预后预测对于治疗决策至关重要 然而，尚未建立基于预后的正式分期系统。 这是因为，尽管进行了许多尝试，但现有的预后生物标志物均未达到预期效果。 已发现对于建立 PF-ILD 的这种分期系统足够准确，这是一种临床上有用的分期。 PF-ILD 系统将实现许多重要的临床用例，例如确定时机和益处 当前可用但昂贵的治疗和干预措施，确定可以接受治疗的患者 安全地延迟以避免潜在的药物不良反应和费用，并在临床试验中确定新疗法。 定量高分辨率计算机断层扫描 (HRCT) 图像最近已成为最 为 PF-ILD 患者提供准确且可重复的生物标志物的有前景的方法，但目前的 HRCT 生物标志物对 PF-ILD 患者的预测效果仍然平庸，为 64-77%， 因此，对于预后生物标志物的临床需求尚未得到满足。 高精度预测 PF-ILD 患者的死亡率和疾病进展。 特别是深度学习，可以用来实现这样的预后生物标志物，特别是条件性生物标志物。 生成对抗网络（cGAN）最近被证明优于传统的生存分析方法 生存预测，但没有这样的基于 cGAN 的方法来执行预后预测 在这个项目中，我们建议开发一种基于无监督图像的 3D cGAN 模型。 直接根据患者的 HRCT 图像自动估计生存时间的分布 我们的目标是开发一个集成的人工智能生存预测模型，它将结合起来。 现有的生物标志物与基于图像的 3D cGAN 模型相结合，可在以下情况中执行准确的预后预测 我们相信集成的 AI 模型将产生高性能（一致性）。 指数≥92%）在预测 PF-ILD 患者的死亡率和疾病进展方面取得了成功。 所提出的集成人工智能模型将显着提高当前最先进的精度 对 PF-ILD 患者的死亡率和疾病进展进行预后预测，从而最终使 建立正式的分期系统以有效管理 PF-ILD 患者成为可能。