Department of Radiology, Harvard Medical School, Boston, MA 02115, USA.
Radiology. 2012 Aug;264(2):397-405. doi: 10.1148/radiol.12111822. Epub 2012 Jun 5.
To develop and validate an informatics toolkit that extracts anatomy-specific computed tomography (CT) radiation exposure metrics (volume CT dose index and dose-length product) from existing digital image archives through optical character recognition of CT dose report screen captures (dose screens) combined with Digital Imaging and Communications in Medicine attributes.
This institutional review board-approved HIPAA-compliant study was performed in a large urban health care delivery network. Data were drawn from a random sample of CT encounters that occurred between 2000 and 2010; images from these encounters were contained within the enterprise image archive, which encompassed images obtained at an adult academic tertiary referral hospital and its affiliated sites, including a cancer center, a community hospital, and outpatient imaging centers, as well as images imported from other facilities. Software was validated by using 150 randomly selected encounters for each major CT scanner manufacturer, with outcome measures of dose screen retrieval rate (proportion of correctly located dose screens) and anatomic assignment precision (proportion of extracted exposure data with correctly assigned anatomic region, such as head, chest, or abdomen and pelvis). The 95% binomial confidence intervals (CIs) were calculated for discrete proportions, and CIs were derived from the standard error of the mean for continuous variables. After validation, the informatics toolkit was used to populate an exposure repository from a cohort of 54 549 CT encounters; of which 29 948 had available dose screens.
Validation yielded a dose screen retrieval rate of 99% (597 of 605 CT encounters; 95% CI: 98%, 100%) and an anatomic assignment precision of 94% (summed DLP fraction correct 563 in 600 CT encounters; 95% CI: 92%, 96%). Patient safety applications of the resulting data repository include benchmarking between institutions, CT protocol quality control and optimization, and cumulative patient- and anatomy-specific radiation exposure monitoring.
Large-scale anatomy-specific radiation exposure data repositories can be created with high fidelity from existing digital image archives by using open-source informatics tools.
开发并验证一种信息学工具包，该工具包通过光学字符识别（OCR）对 CT 剂量报告屏幕截图（剂量屏幕）进行处理，结合数字成像和通信在医学中的属性，从现有的数字图像档案中提取特定解剖结构的计算机断层扫描（CT）辐射暴露指标（容积 CT 剂量指数和剂量长度乘积）。
本研究经机构审查委员会批准，符合 HIPAA 规定，在大型城市医疗服务网络中进行。数据来自 2000 年至 2010 年间随机抽取的 CT 检查，这些检查的图像包含在企业图像档案中，其中包括在成人学术三级转诊医院及其附属站点（包括癌症中心、社区医院和门诊影像中心）获得的图像，以及从其他医疗机构导入的图像。使用每个主要 CT 扫描仪制造商的 150 个随机选择的检查来验证软件，评估指标包括剂量屏幕检索率（正确定位剂量屏幕的比例）和解剖分配精度（提取的暴露数据中正确分配解剖区域的比例，如头、胸或腹部和骨盆）。二项式离散概率的 95%置信区间（CI）通过计算得出，连续变量的 CI 通过均值的标准误差推导得出。验证后，信息学工具包用于从 54549 次 CT 检查的队列中填充暴露存储库；其中 29948 次有可用的剂量屏幕。
验证得到了 99%的剂量屏幕检索率（605 次 CT 检查中有 597 次；95%CI：98%，100%）和 94%的解剖分配精度（600 次 CT 检查中正确的 DLP 分数之和为 563；95%CI：92%，96%）。由此产生的数据存储库的患者安全应用包括机构间的基准比较、CT 协议质量控制和优化以及累积的患者和解剖特异性辐射暴露监测。
通过使用开源信息学工具，可以从现有的数字图像档案中创建高保真度的大规模解剖结构特定的辐射暴露数据存储库。
