Generative-AI based system for accurate prediction of deceased donor liver-transplant (DDLT) outcome and viability

基于生成人工智能的系统，可准确预测已故供体肝移植 (DDLT) 的结果和生存能力

• 批准号: 10654166
• 负责人: Ayman S El-baz
• 金额: $ 46.41万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654166
• 关键词: Acceleration; Acute Liver Failure; Albumins; Blood; Clinical; Clinical Data; Clinical Markers; Collaborations; Computer software; Computer-Assisted Diagnosis; Connective Tissue; Consumption; Data; Diagnostic; Dyes; E-learning; Evaluation; Fibrosis; Freezing; Frozen Sections; Goals; Hematoxylin and Eosin Staining Method; Histology; Human; Implant; Kentucky; Laboratories; Learning; Liver; Malignant neoplasm of liver; Modeling; Organ; Organ Donor; Organ Viability; Outcome; Paraffin Embedding; Pathologist; Pathology; Patient-Focused Outcomes; Patients; Pattern; Phosphoric Monoester Hydrolases; Procedures; Process; Prospective Studies; Protocols documentation; Reporting; Retrospective Studies; Sampling; Slice; Slide; Software Validation; Stains; Surgeon; Survival Rate; System; Technology; Testing; Texture; Time; Tissue Sample; Tissue Stains; Tissues; Training; Transplant Recipients; Transplantation; Trichrome stain; Trichrome stain method; Universities; Validation; Waiting Lists; Water; blind; chronic liver disease; cost; deep learning; deep learning model; digital; digital pathology; experience; improved; in silico; liver allograft; liver transplantation; outcome prediction; post-transplant; predictive modeling; transplant centers; virtual; whole slide imaging

PROJECT SUMMARY There are 4.5 million US patients with chronic liver disease, acute liver failures, and liver cancer. At end stages, patients need to undergo deceased donor liver transplant (DDLT), where the whole liver is explanted from clinically dead donors. After liver-explant, the tissue is rapidly frozen, and a small sample from the donor organ is sliced and processed for rapid histopathological evaluation. Evaluation must be completed during the highly limited time the organ can remain viable for transplantation. The procedure of histopathological evaluation using “frozen sections” differs from standard “paraffin-embedded” one, which is time-consuming due to additional steps to extract water and other substances from tissue. Those steps limit how tissue can be stained with dyes to produce special-stains for histopathological evaluation for fibrosis and connective tissue. Masson’s Trichrome (MT) is not available to pathologists during DDLT. Thus, histopathological evaluation of frozen sections is highly challenging, which can lead to poor outcomes and rejection of viable marginal livers, which could be transplanted in patients. To overcome these limitations, the objective of this proposal is to develop and validate a Generative AI-based system that can produce in-silico “virtual” slides to assist pathologists and surgeons during DDLT. The proposed system will also include a Predictive-AI model for DDLT outcome. The generative module will use a transformation model trained on the texture patterns of fibrosis and connective tissue in input hematoxylin and eosin (HE) sections. The predictive module will i) extract features from both generated MT and input HE slides, ii) fuse them with recipient clinical data, and iii) use deep-learning based model to predict post-transplant outcome. The proposed system can significantly help organ donor organizations in the process of DDLT recipients’ selection while enhancing DDLT viability rates. Our preliminary study shows that the proposed system can produce diagnostic-enabled virtual slides that are validated by experienced pathologists. Also, the study shows our predictive model can perform automatic quantification of fibrosis in the virtual slides with accuracy of 86%. In this proposal (1) we will develop a deep learning-based software that can produce augmented MT layers on the digital HE slides of liver allograft’s frozen sections, and (2) we will extend the software to be capable of predicting the outcome of liver-transplant by learning histopathological and other clinical markers from recipients that predict post- transplant viability. The impacts of our technology are: a) efficient and accurate histopathological evaluation of liver allograft during DDLT, b) improved post-transplant outcome and survival rate, c) reduced operational cost in histology laboratories, and d) accelerated pathology workflows and digital pathology transformation.

项目摘要 美国有450万慢性肝病、急性肝衰竭和肝癌患者。结束时 阶段，患者需要接受死亡供体肝移植（DDLT），其中整个肝脏被移植 临床死亡的捐赠者肝脏移植后，组织被迅速冷冻，从供体中取出一小部分样本， 将器官切片并处理以进行快速组织病理学评价。评估必须在 非常有限的时间，器官可以保持移植的活力。组织病理学检查程序 使用“冷冻切片”的评估不同于标准的“石蜡包埋”评估， 涉及从组织中提取水和其它物质的附加步骤。这些步骤限制了组织可以如何 用染料染色以产生用于纤维化和结缔组织的组织病理学评价的特殊染色剂。 在DDLT期间，病理学家无法使用Masson三色（MT）。因此， 冷冻切片是非常具有挑战性的，这可能导致不良的结果和对有活力的边缘肝的排斥， 可以移植到病人身上为了克服这些限制，本提案的目标是 开发并验证一个基于生成人工智能的系统，该系统可以生成计算机“虚拟”幻灯片， 病理学家和外科医生进行DDLT。拟议的系统还将包括预测人工智能模型， DDLT结局。生成模块将使用在以下纹理模式上训练的变换模型： 纤维化和结缔组织在输入苏木精和伊红（HE）切片。预测模块将i） 从生成的MT和输入的HE载玻片两者中提取特征，ii）将它们与接受者临床数据融合，以及iii） 使用基于深度学习的模型来预测移植后的结果。拟议的系统可以大大帮助 器官捐献组织参与DDLT受者的选择过程，同时提高DDLT存活率。 我们的初步研究表明，该系统可以产生诊断功能的虚拟幻灯片， 由经验丰富的病理学家验证。此外，研究表明，我们的预测模型可以执行 自动量化虚拟切片中的纤维化，准确率为86%。在本建议（1）中，我们将 开发一种基于深度学习的软件，可以在数字HE幻灯片上生成增强的MT层， 肝移植的冷冻切片，（2）我们将扩展软件，能够预测的结果， 通过学习组织病理学和其他临床标志物， 移植存活率 我们的技术的影响是：a）在移植过程中有效和准确的肝移植组织病理学评价 DDLT，B）改善移植后结局和生存率，C）降低组织学手术成本 实验室，以及d）加速病理学工作流程和数字病理学转型。