Localized detection and classification of abnormalities on FFDM and tomosynthesis examinations rated under an FROC paradigm
- PMID: 21343521
- DOI: 10.2214/AJR.10.4760
Localized detection and classification of abnormalities on FFDM and tomosynthesis examinations rated under an FROC paradigm
Abstract
Objective: The purpose of our study was to assess diagnostic performance when retrospectively interpreting full-field digital mammography (FFDM) and breast tomosynthesis examinations under a free-response receiver operating characteristic (FROC) paradigm.
Materials and methods: We performed FROC analysis of a previously reported study in which eight experienced radiologists interpreted 125 examinations, including 35 with verified cancers. The FROC paradigm involves detecting, locating, and rating each suspected abnormality. Radiologists reviewed and rated both FFDM alone and a combined display mode of FFDM and digital breast tomosynthesis (DBT) (combined). Observer performance levels were assessed and compared with respect to the fraction of correctly identified abnormalities, the number of reported location-specific findings (both true and false), and their associated ratings. The analysis accounts for the number and locations of findings and the location-based ratings using a summary performance index (Λ), which is the FROC analog of the area between the receiver operating characteristic curve and the diagonal (chance) line.
Results: Under the FROC paradigm, each reader detected more true abnormalities associated with cancer, or a higher true-positive fraction, under the combined mode. In an analysis focused on both the number of findings and associated location-based ratings, each of the radiologists performed better under the combined mode compared with FFDM alone, with increases in Λ ranging from 5% to 34%. On average, under the combined mode radiologists achieved a 16% improvement in Λ compared with the FFDM alone mode (95% CI, 7-26%; p < 0.01).
Conclusion: We showed that DBT-based breast imaging in combination with FFDM could result in better performance under the FROC paradigm.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Medical
