Factors affecting recall rate and false positive fraction in breast cancer screening with breast tomosynthesis - A statistical approach.

In this study, we investigate which factors affect the false positive fraction (FPF) for digital breast tomosynthesis (DBT) compared to digital mammography (DM) in a screening population by using classification and regression trees (C&RT) and binary marginal generalized linear models. The data was obtained from the Malmö Breast Tomosynthesis Screening Trial, which aimed to compare the performance of DBT to DM in breast cancer screening. By using data from the first half of the study population (7500 women), a tree with the recall probability for different groups was calculated. The effect of age and breast density on the FPF was estimated using a binary marginal generalized linear model. Our results show that breast density and breast cancer were the main factors influencing recall. The FPF is mainly affected by breast density and increases with breast density for DBT and DM. In conclusion, the results obtained with C&RT are easy to interpret and similar to those obtained using binary marginal generalized linear models. The FPF is approximately 40% higher for DBT compared to DM for all breast density categories

Mammographic calcifications undergoing percutaneous biopsy: outcome in women with and without a personal history of breast cancer

PURPOSE To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC). MATERIALS AND METHODS In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann-Whitney U tests and Chi-square tests. RESULTS Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50-65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P = .715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1-2 years (25%, 1 of 4), 2-5 years (20%, 2 of 10) and 5-10 years (0%, of 13) from the first breast cancer (P = .005). CONCLUSION PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation

Time to modernize breast cancer screening? : A mini-review of the Malmö Breast Tomosynthesis Screening Trial

The Malmö Breast Tomosynthesis Screening Trial is a large population based screening trial designed to evaluate the use of one-view breast tomosynthesis, with reduced compression force, as a stand-alone screening modality. Initial results show a substantially increased cancer detection rate compared to mammography and a still-acceptable elevation of the recall and false-positive rates

Breast tomosynthesis – new perspectives on breast cancer screening

Abstract: Mammography is currently the established method in breast cancer screening, although the sensitivity is known be affected by overlapping tissue concealing tumours. Breast tomosynthesis takes advantage of multiple exposures at different angles reducing the negative effect of obscuring tissue. The aim of this thesis was to investigate the use of tomosynthesis in breast imaging with a special focus on the role of tomosynthesis in screening. Papers I and II was based on 7,500 women comprising the first half of the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective population-based single arm study including randomly invited women 40–74 years old eligible for the screening programme in the city of Malmö. Women underwent one-view tomosynthesis with reduced compression force and mammography. The images were read and scored separately in a blinded double-reading procedure. The increase in cancer detection rate (6.3 to 8.9/1,000) and recall rate (2.6% to 3.8%) using tomosynthesis was significant (p<0.0001). The additional cancers detected were mainly invasive, with a tendency of downstaging (Paper I). Findings of stellate distortion simulating malignancy increased the false positive rate with tomosynthesis. The false positive rate was reduced over time, suggesting a learning curve (Paper II). Paper III was a human observer study designed to study the effect of different viewing procedures (free scroll volume browsing and initial cineloops at three different frame rates) and image orientations (vertical and horizontal) on reading performance. 40 three-dimensional simulations of breast malignancy were inserted in 55 normal tomosynthesis image volumes, creating four unique image sets, one for each viewing procedure. Detection accuracy was similar for the different reading conditions but reading time and visual attention were improved for horizontally oriented images. In Paper IV, 26 breast cancers subjected to discrepant detection in tomosynthesis and mammography in a previous reader study with 185 cases with normal and diseased breasts (95 cancer lesions), were analysed in a side-by-side review to assess factors related to lesion visibility and possible reasons for detection error. Breast cancers imaged with tomosynthesis were found to be more conspicuous with a higher proportion of discernable lesion periphery. Missing a cancer in tomosynthesis seemed to be related to interpretative error of visible lesions. In conclusion, one-view tomosynthesis, with reduced compression force, might be sufficient as a stand-alone screening modality