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.

项目总结/摘要 进行性纤维化间质性肺疾病（PF-ILD）是一组以自身免疫功能增强为特征的疾病。 持续性纤维化，呼吸困难进行性恶化，肺功能进行性下降，对 免疫调节治疗和高死亡率。由于高度可变的下降率和不良预后， 准确的PF-ILD患者个体化预后预测对于治疗决策至关重要 和病人的管理。然而，尚未建立基于预后的正式分期系统 对于PF-ILD。这是因为，尽管进行了许多尝试，但现有的已开发的预后生物标志物中没有一个具有 已被发现是足够准确的建立这样一个分期系统PF-ILD。临床上有用的分期 PF-ILD系统将支持许多重要的临床用例，例如确定时间和获益 目前可用但昂贵的治疗和干预措施，确定可以治疗的患者 安全延迟，以避免潜在的药物不良反应和成本，并在临床试验中确定新的治疗方法。 定量高分辨率计算机断层扫描（HRCT）图像最近已成为最 在PF-ILD患者中提供准确和可重复的生物标志物的有前途的方法，但目前的HRCT 生物标志物对PF-ILD患者的预测性能仍然很一般，为64-77%， 用一致性指数来衡量。因此，存在对预后生物标志物的未满足的临床需求，所述预后生物标志物将 预测PF-ILD患者的死亡率和疾病进展的准确性较高。人工智能（AI）， 特别是深度学习，可以用来实现这样一种预后生物标志物。特别是，一个条件 生成对抗网络（cGAN）最近被证明优于传统的生存分析方法 在生存预测中，但是没有这样的基于cGAN的方法来从患者中进行预后预测。 患者的图像数据。在这个项目中，我们建议开发一个无监督的基于图像的3D cGAN模型 直接从患者的HRCT图像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，该模型将联合收割机 现有的生物标志物与基于图像的3D cGAN模型进行准确的预后预测 PF-ILD患者。我们假设集成的AI模型将产生高性能（一致性 指数≥92%）预测PF-ILD患者的死亡率和疾病进展。成功发展 所提出的综合人工智能模型将显着提高目前最先进的准确性， 预测PF-ILD患者的死亡率和疾病进展，从而最终使 有可能建立一个正式的分期系统，以便对PF-ILD患者进行有效管理。