Weiss Julie E, Goodrich Martha, Harris Kimberly A, Chicoine Rachael E, Synnestvedt Marie B, Pyle Steve J, Chen Jane S, Herschorn Sally D, Beaber Elisabeth F, Haas Jennifer S, Tosteson Anna N A, Onega Tracy
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
J Am Coll Radiol. 2017 Feb;14(2):198-207.e2. doi: 10.1016/j.jacr.2016.08.017. Epub 2016 Oct 13.
To assess indication for examination for four breast imaging modalities and describe the complexity and heterogeneity of data sources and ascertainment methods.
Indication was evaluated among the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) breast cancer research centers (PRCs). Indication data were reported overall and separately for four breast imaging modalities: digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and magnetic resonance imaging (MRI).
The breast PRCs contributed 236,262 women with 607,735 breast imaging records from 31 radiology facilities. We found a high degree of heterogeneity for indication within and across six data sources. Structured codes within a data source were used most often to identify indication for mammography (59% DM, 85% DBT) and text analytics for US (45%) and MRI (44%). Indication could not be identified for 17% of US and 26% of MRI compared with 2% of mammography examinations (1% DM, 3% DBT).
Multiple and diverse data sources, heterogeneity of ascertainment methods, and nonstandardization of codes within and across data systems for determining indication were found. Consideration of data sources and standardized methodology for determining indication is needed to assure accurate measurement of cancer screening rates and performance in clinical practice and research.
评估四种乳腺成像模态的检查指征,并描述数据源和确定方法的复杂性及异质性。
在基于人群的个性化方案优化筛查乳腺癌研究中心(PROSPR)中评估指征。分别报告了四种乳腺成像模态(数字乳腺钼靶摄影(DM)、数字乳腺断层合成(DBT)、超声(US)和磁共振成像(MRI))的总体指征数据。
乳腺研究中心提供了来自31个放射科设施的236,262名女性的607,735条乳腺成像记录。我们发现六个数据源内部和之间的指征存在高度异质性。数据源中的结构化编码最常用于识别钼靶摄影的指征(DM为59%,DBT为85%),而超声(45%)和MRI(44%)的指征则通过文本分析来识别。与2%的钼靶摄影检查(DM为1%,DBT为3%)相比,17%的超声检查和26%的MRI检查无法识别指征。
发现了多种不同的数据源、确定方法的异质性以及数据系统内部和之间用于确定指征的编码的不标准化。为确保在临床实践和研究中准确测量癌症筛查率和性能,需要考虑数据源和用于确定指征的标准化方法。