Abstract
PurposeTo develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. MethodsInterviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). ResultsTwelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. ConclusionsBy following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.
Highlights
Optimal performance of digital mammography (DM) screening is crucial for detecting breast cancer at an early stage
The aim of this study was to develop a candidate instru ment to assess image quality in digital mammography from the perspective of the radiologists, by identifying clinically relevant features in DM images that are affected by lower image quality
If a code reflected a feature that is not normally affected by lower image quality or that could be covered by another tentative item, that specific code was not converted into a tentative item
Summary
Optimal performance of digital mammography (DM) screening is crucial for detecting breast cancer at an early stage. The per formance of DM systems, including their corresponding image process ing, needs to be tested and optimised, taking into account the issues that. Affect image interpretation and breast cancer detection. Exam ples of such an evaluation procedure are type testing described in the Supplement to the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis [7] and FDA-required Equipment Evaluation testing for DM systems [8]. Two comple mentary evaluation phases are performed: a physics-based assessment, and a clinical assessment, derived from objective and subjective evalu ations, respectively
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