Development and Assessment of Decision Supporting System for Renal studies

项目概要/摘要 快速扩大的知识库、培训差距、经验有限带来的压力 不断增加的时间限制给诊断放射科医生及其患者带来了困境。怎么可以 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 观察者间解释的变异性使得诊断较少依赖于解释 放射科医生能否更准确地反映是否存在潜在疾病？我们的长期 目标是 (1) 制定通用统计方法，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之间的变异性；(2) 开发一个新的通用统计推断框架 可以确定我们的 DSS 的表现是否相当于专家或专家小组的表现。 我们的近期目标和概念验证是出于开发 DSS 来改进 转诊进行核医学肾脏扫描的肾脏病患者的护理，这是许多放射科医生缺乏的领域 培训和经验。通过注射放射性示踪剂 99mTc MAG3 和 当示踪剂被肾脏从血液中去除时，在 20-30 分钟内连续对示踪剂进行成像， 沿着输尿管进入膀胱。当怀疑阻塞时，患者通常会接受强效的治疗。 额外 20 分钟获得肾脏的利尿和连续图像。放射科医生通常使用 肾脏时间活动曲线（肾图）上的一些特定点可帮助解释该研究。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 以结构化格式进行 MAG3 肾脏扫描。我们不是在肾图上使用一些孤立的特征，而是 提议开发一种潜在的类建模方法来预测肾梗阻，该方法联合建模 由肾脏图像和专家评级以及其他数据产生的肾图曲线数据（功能数据[13,49]） 相关的临床变量（目标 1）。将开发扩展来处理存在于中的丢失数据 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DSS 统计推断框架，可以确定我们的 DSS 的性能是否相当于 专家或专家小组。该方法是为分类和连续评级而开发的 疾病状况（目标 2）。我们计划使用独立数据样本验证 DSS（目标 3）。 [我们计划 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 在目标 4 下将 DSS 应用到临床环境的可行性]。虽然旨在直接造福于 肾脏扫描的解释、DSS 和待开发的统计方法解决了常见和 图像解释中的基本问题，特别是需要整合数据来恢复图像的情况 有关疾病的信息。

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.