Purpose

To 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.

Methods

Interviews 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*).

Results

Twelve 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.

Conclusions

By 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.