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Comparative Study
. 2017 Feb;14(2):198-207.e2.
doi: 10.1016/j.jacr.2016.08.017. Epub 2016 Oct 13.

Challenges With Identifying Indication for Examination in Breast Imaging as a Key Clinical Attribute in Practice, Research, and Policy

Julie E Weiss  1 , Martha Goodrich  2 , Kimberly A Harris  3 , Rachael E Chicoine  4 , Marie B Synnestvedt  5 , Steve J Pyle  6 , Jane S Chen  3 , Sally D Herschorn  7 , Elisabeth F Beaber  8 , Jennifer S Haas  9 , Anna N A Tosteson  10 , Tracy Onega  11 ; PROSPR (Population-based Research Optimizing Screening through Personalized Regimens) consortium
Affiliations
Comparative Study

Challenges With Identifying Indication for Examination in Breast Imaging as a Key Clinical Attribute in Practice, Research, and Policy

Julie E Weiss et al. J Am Coll Radiol. 2017 Feb.

Abstract

Purpose: To assess indication for examination for four breast imaging modalities and describe the complexity and heterogeneity of data sources and ascertainment methods.

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).

Results: 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).

Conclusions: 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.

Keywords: Indication for examination; data source; digital breast tomosynthesis; digital mammography; magnetic resonance imaging; ultrasound.

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Conflict of interest statement

All authors have no conflicts of interest related to the material presented in this manuscript.

Figures

Fig 1
Fig 1
Indication for exam data flow among the breast cancer research centers affiliated with the Population-based Research Optimizing Screening for Personalized Regimens (PROSPR) consortium
Fig 2
Fig 2
Points of indication for exam determination and extraction Abbreviations: IFE= indication for exam; DM = digital mammography; DBT = digital breast tomosynthesis; US = ultrasound; MRI = magnetic resonance imaging.
Fig 3
Fig 3
Percent of indication for exam extracted by ascertainment method for each breast imaging modality Abbreviations: IFE = indication for exam; SCR = screening; DX = diagnostic; UK = unknown. Legend: Code Classification indicates an exam code was used to determine the indication for a given exam. Text analytics includes manual processes or programs that utilize simple key word searches or natural language processing to determine the indicaton. Temporal algorithm incorporates when the image was performed relative to key events.
See this image and copyright information in PMC

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