High risk score of breast cancer by artificial intelligence (AI) on screening mammograms: a review of negative and cancer cases

Abstract

OBJECTIVES: To investigate mammographic features associated with high artificial intelligence (AI) risk scores as provided by two AI models applied to screening mammograms. MATERIALS AND METHODS: This retrospective study included 130,031 screening mammograms from 42,371 women attending BreastScreen Norway, 2008-2018. Two AI models (A and B) developed for cancer detection on screening mammograms were applied. An informed radiological review was conducted for mammograms within the highest 5% of AI risk scores by both models in two study samples: (1) High AI risk score, but no breast cancer detected within 6 years (n = 120), and (2) High AI risk score in mammograms with screen-detected cancers (n = 120). Mammographic density (BI-RADS a-d), features (mass, spiculated mass, asymmetry, architectural distortion, calcification alone, and density with calcification), and radiologists' interpretation scores (1-5) were analyzed descriptively. RESULTS: Mammographic density was higher in sample 1 compared to sample 2 (BI-RADS d: 11% vs 3%, respectively). In sample 1, calcifications alone were the most frequent AI-marked feature (model A: 72%; model B: 68%), predominantly with amorphous morphology and a cluster distribution, and 76% were interpreted as benign by the radiologists (interpretation score 1). In sample 2, a spiculated mass was the most frequent mammographic feature among the screen-detected cancers (29%). CONCLUSION: Mammograms assigned high AI risk scores exhibit distinct features depending on screening outcome. Systematic characterization of these features may help refine AI thresholds, improve specificity, reduce AI false-positive findings, and decrease the recall rate in breast cancer screening. KEY POINTS: Question Knowledge about mammographic features associated with high AI risk scores is essential for distinguishing cancer from non-cancer cases. Findings Calcifications were the dominant feature in non-cancers in screening mammograms with high AI risk score, whereas spiculated mass was the most frequent feature among cancers. Clinical relevance Calcifications in non-cancer screening mammograms with a high AI risk score were frequently interpreted as benign or probably benign by radiologists. This knowledge may help refine AI thresholds and thereby improve specificity and reduce false-positive results in mammographic screening.

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