Development and Assessment of Decision Supporting System for Renal studies

项目概要/摘要 迅速扩大的知识基础、培训方面的差距、有限的经验 并且不断增加的时间限制使诊断放射科医师和他们的病人陷入两难境地。怎么能 放射科医生始终提供高质量的诊断解释？放射科医生如何减少内部和 观察者之间的解释差异，使得诊断较少依赖于解释 放射科医生和更准确地反映存在或不存在的基础疾病？我们的长期 目标是（1）制定一个通用的统计方法，以制定和实施一个 决策支持系统（DSS），以帮助医生在放射诊断中做出明智的决定， 减少观察者内和观察者间的变异性，以及（2）开发一个新的通用统计推断框架， 可以确定我们的DSS的性能是否等同于专家或专家小组的性能。 我们的近期目标和概念验证的动机是需要开发一个DSS，以提高 肾科病人的护理转介核医学肾扫描，一个领域，许多放射科医生缺乏 训练和经验。通过注射放射性示踪剂99 mTc MAG 3和 当示踪剂被肾脏从血液中去除时，在20-30分钟的时间内顺序地对示踪剂成像， 通过输尿管进入膀胱当怀疑阻塞时，患者通常会接受强效的 利尿剂和连续图像在肾脏获得额外的20分钟。放射科医生通常使用 肾脏时间活动曲线（肾图）上的一些特定点，以帮助解释研究。我们 建议将临床数据与自动图像分析相结合，以提供全面的解释 结构化格式的MAG 3肾脏扫描。我们不使用肾图上的一些孤立特征， 我建议开发一种用于预测肾梗阻的潜在类建模方法， 肾图曲线数据（功能数据[13，49]）来自肾脏图像和专家评级以及其他 相关临床变量（目标1）。将开发扩展程序，用于处理 这种类型的研究。为了评估新开发的DSS，我们建议开发一个新的通用的 统计推理框架，可以确定我们的DSS的性能是否相当于 专家或专家小组。该方法是开发的分类和连续评级的 疾病状态（目标2）。我们计划用一个独立的数据样本来验证DSS（目标3）。[We计划 与（a）核医学住院医师和（B）放射学住院医师进行两项试点研究，以确定 在目的4下将DSS应用于临床环境的可行性]。虽然目的是为了直接造福于 肾扫描的解释，DSS和统计方法的发展，解决共同的， 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.