Development and Assessment of Decision Supporting System for Renal studies

项目摘要/摘要 迅速扩大的知识基础、培训中的差距、有限的经验带来的压力 不断升级的时间限制给诊断放射科医生和他们的病人带来了两难境地。怎么能 放射科医生始终如一地提供高质量的诊断解释？放射科医生如何减少内部和外部 口译中的观察者间可变性，以便诊断较少依赖于口译 放射科医生，并更准确地反映潜在疾病的存在或不存在？我们的长期合作 目标是：(1)制定一种通用的统计方法，以制定和实施 决策支持系统(DSS)，帮助医生在放射诊断中做出明智的决策，并 减少观察者内部和观察者间的可变性；以及(2)开发新的通用统计推断框架，以 可以确定我们的决策支持系统的表现是否等同于一名专家或一组专家的表现。 我们的直接目标和概念验证的动机是需要开发一个决策支持系统来改善 肾脏疾病患者的护理转诊为核医学肾脏扫描，这是许多放射科医生缺乏的领域 无论是培训还是经验。肾脏扫描是通过注射放射性示踪剂99mTcMAG3和 当示踪剂被肾脏从血液中移除时，在20-30分钟内顺序成像 顺着输尿管进入膀胱。当怀疑梗阻时，患者通常会接受有效的 肾脏上的利尿剂和序列图像额外获得20分钟。放射科医生通常使用 肾脏时间活动曲线(肾图)上的几个特定点有助于解释这项研究。我们 建议将临床数据与自动图像分析相结合，以提供对 MAG3肾脏扫描采用结构化格式。我们没有在肾图上使用一些孤立的特征，而是 建议开发一种用于预测肾梗阻的潜在类建模方法，该方法联合建模 肾图曲线数据(功能数据[13，49])由肾脏图像和专家评分以及其他 相关临床变量(目标1)。将开发扩展模块来处理中存在的丢失数据 这种类型的研究。为了评估新开发的决策支持系统，我们建议开发一个新的通用 统计推理框架，可以确定我们的DSS的性能是否等同于 专家或专家小组。该方法是为分类评级和连续评级而开发的 疾病状况(目标2)。我们计划用一个独立的数据样本来验证决策支持系统(目标3)。[我们计划 与(A)核医学住院医生和(B)放射科住院医生进行两项试点研究，以确定 在目标4下将决策支持系统应用于临床环境的可行性]。虽然旨在直接造福于 对肾脏扫描的解释、将制定的决策支持系统和统计方法涉及共同和 图像解释中的基本问题，特别是在需要整合数据以恢复 关于这种疾病的信息。

Project Summary/Abstract The pressures imposed by a rapidly expanding knowledge base, gaps in training, limited experience and escalating time constraints create a dilemma for diagnostic radiologists and their patients. How can radiologists consistently provide quality diagnostic interpretations? How can radiologists reduce intra- and interobserver variability in interpretation such that the diagnosis is less dependent on the interpreting radiologist and more accurately reflects the presence or absence of the underlying disease? Our long-term objectives are (1) to develop a general statistical methodology for the development and implementation of a decision supporting system (DSS) to help physicians to make informed decisions in radiologic diagnosis and to reduce intra- and interobserver variability and (2) to develop a new general statistical inferential framework that can determine if the performance of our DSS is equivalent to that of an expert or a panel of experts. Our immediate goal and proof of concept is motivated by the need to develop a DSS to improve the care of nephrology patients referred for a nuclear medicine renal scans, an area where many radiologists lack both training and experience. A renal scan is obtained by injecting a radioactive tracer, 99mTc MAG3 and sequentially imaging that tracer over a 20-30 min period as it is removed from the blood by the kidneys and passes down the ureters into the bladder. When obstruction is suspected, the patient often receives a potent diuretic and sequential images over the kidney are obtained for an additional 20 min. Radiologists typically use a few specific points on the kidney time activity curves (renogram) to assist in interpretation of the study. We propose to integrate clinical data with automated image analysis to provide a comprehensive interpretation of MAG3 renal scans in a structured format. Rather than using a few isolated features on the renogram, we propose to develop a latent class modeling approach for predicting kidney obstruction that jointly models renogram curve data (functional data [13,49]) resulting from renal images and expert ratings as well as other relevant clinical variables (Aim 1). Extensions will be developed for handling missing data that are present in this type of studies. In order to evaluate the newly developed DSS, we propose to develop a new general statistical inferential framework that can determine if the performance of our DSS is equivalent to that of an expert or a panel of experts. The methodology is developed for both categorical and continuous ratings of the disease status (Aim 2). We plan to validate the DSS with an independent data sample (Aim 3). [We plan to conduct two pilot studies with (a) nuclear medicine residents and (b) radiology residents to determine the feasibility of applying DSS to clinical setting under Aim 4]. While intended to be of direct benefit to the interpretation of renal scans, the DSS and statistical methodology to be developed address common and fundamental issues in image interpretation, especially where the integration of data is needed to recover the information about the disease.