Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis.

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.

MRI only detected lesions: Can contrast enhanced mammography guided biopsy be an alternative method: Initial clinical findings.

OBJECTIVE: This study aims to analyze our initial findings regarding CEM-guided stereotactic vacuum-assisted biopsy for MRI-only detected lesions and compare biopsy times by MRI-guided biopsy. MATERIALS AND METHODS: In this retrospective analysis, CEM-guided biopsies of MRI-only detected breast lesions from December 2021 to June 2023were included. Patient demographics, breast density, lesion size, background parenchymal enhancement on CEM, lesion positioning, procedure duration, and number of scout views were documented. Initially, seven patients had CEM imaging before biopsy; for later cases, CEM scout views were used for simultaneous lesion depiction and targeting. RESULTS: Two cases were excluded from the initial 28 patients with 29 lesions resulting in a total of 27 lesions in 26 women (mean age:44.96 years). Lesion sizes ranged from 4.5 to 41 mm, with two as masses and the remaining as non-mass enhancements. Histopathological results identified nine malignancies (33.3 %, 9/27), including invasive cancers (55.6 %, 5/9) and DCIS (44.4 %, 4/9). The biopsy PPV rate was 33.3 %. Benign lesions comprised 66.7 %, with 22.2 % high-risk lesions. The biopsy success rate was 93.1 % (27/29), and minor complications occurred in seven cases (25.9 %, 7/27), mainly small hematomas and one vasovagal reaction (3.7 %, 1/27). Median number of scout views required was 2, with no significant differences between cases with or without prior CEM (P = 0.8). Median duration time for biopsy was 14 min, significantly shorter than MRI-guided bx at the same institution (P < 0.001) by 24 min with predominantly upright positioning of the patient (88.9 %) and horizontal approach of the needle (92.6 %). CONCLUSION: This study showed that CEM-guided biopsy is a feasible and safe alternative method and a faster solution for MRI-only detected enhancing lesions and can be accurately performed without the need for prior CEM imaging.

Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program.

OBJECTIVES: We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. MATERIALS AND METHODS: The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. RESULTS: The AI software achieved an AUC of 89.6% (86.1-93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. CONCLUSION: This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. CLINICAL RELEVANCE STATEMENT: The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. KEY POINTS: • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading.