Quantification of Liver Fibrosis with MRI and Deep Learning

通过 MRI 和深度学习量化肝纤维化

基本信息

批准号：
10096229
负责人：
Lili He
金额：
$ 62.83万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10096229
关键词：
Address Adult Age American Appearance Artificial Intelligence Bilirubin Biopsy Body mass index Cessation of life Characteristics Child Childhood Chronic Disease Clinical Clinical Data Complex Computer-Assisted Diagnosis Crowding Data Data Set Databases Death Rate Decision Making Descriptor Detection Diabetes Mellitus Diagnosis Disease Evaluation Eye Fatty Liver Financial Hardship Goals Health Care Costs Health Expenditures Healthcare Histologic Human Image Individual Institution Laboratories Length of Stay Liver Liver Fibrosis Liver diseases Magnetic Resonance Elastography Magnetic Resonance Imaging Maps Mathematics Measurable Measures Medical Methods Modeling Morbidity - disease rate Morphology Neural Network Simulation Operative Surgical Procedures Outcome Patient-Focused Outcomes Patients Pattern Performance Physicians Population Portal Hypertension Procedures Property Public Health Reference Standards Research Research Personnel Residual state Risk Risk Factors Running Sampling Errors Series Severity of illness Shapes Signal Transduction Site Spleen Staging Structural defect Techniques Testing Texture Time Tissues Training Transplantation Vendor Viral hepatitis Visual Workload accurate diagnosis accurate diagnostics chronic liver disease classification algorithm clinical risk convolutional neural network cost deep learning deep neural network diagnostic technologies elastography hospital readmission hospitalization rates improved improved outcome learning strategy life time cost liver biopsy liver transplantation mortality mortality risk multimodality multitask non-linear transformation outcome prediction personalized diagnostics personalized medicine prognostic prognostic model radiologist radiomics response sex

项目摘要

Project Summary/Abstract Chronic liver disease (CLD) is a common cause of morbidity and mortality in the U.S. and throughout the world. In 2017, CLD had an age-adjusted death rate of 10.9/100,000 total population and an estimated lifetime cost of fatty liver disease alone in the U.S. of ~$222 billion. Liver fibrosis (LF) is the most important and only histologic feature known to predict outcomes from CLD. The current standard for assessing LF is biopsy, which is costly, prone to sampling error, and invasive with poor patient acceptance. Thus, there is an urgent unmet need for noninvasive, highly accurate and precise diagnostic technologies for detection and quantification of LF. Our overarching objective is to apply Deep Learning (DL) methods using conventional non-elastographic magnetic resonance (MR) images, MR elastography (MRE), and clinical data to accurately detect and measure LF in children and adults with CLD, using biopsy-derived histologic data as the reference standard. In this project, we will dedicate our efforts to accomplishing the following specific aims. In Aim 1, we will develop and validate a DL framework to accurately segment liver and spleen in order to extract radiomic (gray-scale signal intensity distribution, shape and morphology, volumetry, and inter-voxel signal intensity pattern and texture) and deep features (complex abstractions of patterns non-linearly constructed throughout the transformation estimated by data-driven DL training procedures) from conventional multiparametric MRI. These features allow detection of liver and spleen structural abnormalities/tissue aberrations. In Aim 2, we will develop and validate an “ensemble” DL model (LFNet) to predict biopsy-derived LF stage and LF percentage using the integration of conventional multimodal MRI radiomic and deep features, MRE data, as well as clinical data. In Aim 3, we will develop and validate a DL model (LSNet) to quantify MRE-derived liver stiffness (LS) using conventional multiparametric MRI radiomic and deep features as well as clinical data. The proposed models will help physicians to more accurately detect and follow CLD by 1) quantifying LS from conventional MR imaging without the need for MRE; and, more importantly, 2) predicting histologic LF stage and LF percentage without the need for biopsy, while avoiding inter- radiologist variability, reducing radiologist workload, and ultimately reducing healthcare costs. We will validate the models using both internal and independent external data from various scanners and sites. The techniques we develop are expected to improve medical diagnosis and prognostication in the same way as DL has revolutionized other fields. This study will significantly impact public health because it will allow physicians and researchers to more accurately diagnose and quantify CLD and LF as well as permit more frequent assessments in a noninvasive, patient-centric manner, thus potentially improving patient outcomes while lowering healthcare costs. The techniques we develop also can be readily extended for the prediction of other important liver-related clinical outcomes, including impending complications such as portal hypertension, time to liver transplant/transplant listing, and mortality risk, among others.

项目概要/摘要慢性肝病 (CLD) 是美国乃至全世界发病率和死亡率的常见原因 2017 年，CLD 的年龄调整死亡率为 10.9/100,000 总人口和估计寿命。在美国，仅脂肪肝疾病就造成约 2220 亿美元的损失，肝纤维化 (LF) 是最重要且唯一的。已知可预测 CLD 结果的组织学特征目前评估 LF 的标准是活检。成本高昂，容易出现采样错误，且具有侵入性，患者接受度较差，因此，这是一个紧迫的未满足的问题。需要无创、高度准确和精确的诊断技术来检测和量化 LF。我们的首要目标是使用传统的非弹性成像技术来应用深度学习 (DL) 方法磁共振 (MR) 图像、MR 弹性成像 (MRE) 和临床数据，以准确检测和测量患有 CLD 的儿童和成人的 LF，使用活检组织学数据作为参考标准。我们将致力于实现以下具体目标：在目标 1 中，我们将制定并验证一个目标。深度学习框架可准确分割肝脏和脾脏，以提取放射组学（灰度信号强度）分布、形状和形态、体积、体素间信号强度模式和纹理）和深度特征（在整个转换过程中非线性构建的模式的复杂抽象，估计为来自传统多参数 MRI 的数据驱动的深度学习训练程序）。肝脏和脾脏结构异常/组织畸变在目标 2 中，我们将开发并验证一个“整体”。 DL 模型 (LFNet) 使用传统的集成来预测活检衍生的 LF 分期和 LF 百分比在目标 3 中，我们将开发和利用多模态 MRI 放射组学和深层特征、MRE 数据以及临床数据。验证 DL 模型 (LSNet)，以使用传统多参数 MRI 量化 MRE 衍生的肝脏硬度 (LS) 所提出的模型将帮助医生更准确地分析放射组学和深层特征以及临床数据。通过以下方式检测和跟踪 CLD：1) 量化传统 MR 成像中的 LS，无需 MRE；重要的是，2）无需活检即可预测组织学 LF 分期和 LF 百分比，同时避免中间放射科医生的可变性，减少放射科医生的工作量，并最终降低医疗成本。这些模型使用来自各种扫描仪和站点的内部数据和独立的外部数据。我们开发的药物有望像 DL 一样改善医学诊断和预后这项研究将极大地影响公共健康，因为它将允许医生和研究人员可以更准确地诊断和量化 CLD 和 LF，并允许更频繁的评估以无创、以患者为中心的方式，从而有可能改善患者的治疗结果，同时降低医疗费用我们开发的技术还可以很容易地扩展到其他重要的肝脏相关的预测。临床结果，包括即将发生的并发症，如门静脉高压、肝硬化时间移植/移植清单、死亡风险等。