Quantification of Liver Fibrosis with MRI and Deep Learning

项目总结/摘要 慢性肝病（CLD）是美国和整个美国的发病率和死亡率的常见原因。 世界2017年，CLD的年龄调整死亡率为10.9/100，000总人口， 仅在美国，脂肪肝的成本就高达2220亿美元。肝纤维化（LF）是最重要和唯一的 已知可预测CLD结局的组织学特征。目前评估LF的标准是活检， 成本高、容易产生采样误差、并且具有侵入性，患者接受度差。因此，有一个紧迫的未得到满足的问题， 需要无创的，高度准确和精确的诊断技术来检测和定量LF。 我们的总体目标是使用传统的非弹性成像技术应用深度学习（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 LF在儿童和成人CLD，使用活检衍生的组织学数据作为参考标准。在这个项目中， 我们将致力于实现以下具体目标。在目标1中，我们将开发和验证一个 DL框架精确分割肝脏和脾脏，以提取放射组（灰度信号强度 分布、形状和形态、体积测定和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， 数据驱动的DL训练程序）。这些特征允许检测 肝脏和脾脏结构异常/组织畸变。在目标2中，我们将开发和验证一个“集成” DL模型（LFNet），使用传统的积分预测活检衍生的LF分期和LF百分比 多模态MRI放射组学和深部特征、MRE数据以及临床数据。在目标3中，我们将开发和 使用常规多参数MRI验证DL模型（LSNet）以量化MRE衍生的肝脏硬度（LS） 放射学和深部特征以及临床数据。所提出的模型将帮助医生更准确地 通过1）从常规MR成像中量化LS而无需MRE来检测和跟踪CLD;以及更多 重要的是，2）预测组织学LF分期和LF百分比，而不需要活检，同时避免间- 放射科医生的可变性，减少放射科医生的工作量，并最终降低医疗保健成本。我们将验证 该模型使用来自各种扫描仪和站点的内部和独立外部数据。的技术 我们开发的新技术有望像DL一样， 彻底改变了其他领域。这项研究将对公共卫生产生重大影响，因为它将使医生和 研究人员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，从而可能改善患者的治疗效果，同时降低医疗保健费用。 成本我们开发的技术也可以很容易地扩展到其他重要的肝脏相关的预测。 临床结局，包括即将发生的并发症，如门静脉高压症，肝移植时间 移植/移植列表和死亡风险等。

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.