Artificial Intelligence Detection of Interval Breast Cancers on False-negative Screening Mammograms: A Retrospective Lesion-level Analysis.

Rationale

AND

Objectives

Interval breast cancers (IBCs) occur after false-negative (FN) screening mammograms and can be diagnostically challenging. This study classified IBCs by retrospective visibility, evaluated an artificial intelligence (AI) tool's ability to detect IBCs, and identified factors associated with detection. MATERIALS AND 

Methods

This retrospective study of 211,517 screening mammograms (2013-2020) included 96 IBCs diagnosed within a year of a negative/benign exam. Two radiologists independently classified IBC retrospective visibility; a third resolved discordant cases. Cohen's kappa assessed inter-reader agreement. A Food and Drug Administration-approved AI tool scored FN mammograms at the lesion level. Associations with AI detection were analyzed using chi-square/Fisher's exact tests and logistic regression.

Results

AI detected 40% (38/96) of IBCs, including 81% (34/42) retrospectively visible and 7% (4/54) not visible (p < 0.005). Interreader agreement was substantial (kappa = 0.69). Nearly all misses (91%, 10/11), all minimal actionable findings (100%, 13/13), and most minimal nonactionable findings (61%, 11/18) were detected by AI. True interval (0%, 0/14), mammographically occult (10%, 4/39), and technical misses (0%, 0/1) were rarely detected. On univariate analysis, AI detection was significantly associated with retrospective visibility (odds ratio [OR] 53.1, p < 0.001), symptomatic presentation (OR 8.1, p = 0.007), ultrasound-guided biopsy (OR 5.7, p = 0.027), and higher T stage (OR 3.4, p = 0.005).

Conclusion

AI detected most retrospectively visible IBCs but had limited performance for cancers not visible. AI detection was associated with visibility, presentation, biopsy modality, and T stage. While AI offers a safety net for visible cancers, supplemental screening remains necessary for mammographically occult and true interval cancers.