Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset

Sacchetto, Daniela; Morra, Lia; Agliozzo, Silvano; Bernardi, Daniele; Björklund, Tomas; Brancato, Beniamino; Bravetti, P; Carbonaro, Luca A.; Correale, Loredana; Fantò, Carmen; Favettini, Elisabetta; Martincich, Laura; Milanesio, L; Mombelloni, Sara; Monetti, Francesco; Morrone, D; Pellegrini, Marco; Pesce, Bárbara; Petrillo, Antonella; Saguatti, Gianni; Stevanin, C.; Trimboli, Rubina Manuela; Tuttobene, Paola; Valentini, Marvi; Marra, Vincenzo; Frigerio, Alfonso; Bert, Alberto; Sardanelli, Francesco

doi:10.1007/s00330-015-3784-2

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…This was, however, out of scope for this study. Second, two previous studies investigating BI-RADS agreement had several radiologists reading the images in the density analyses, which, of course, would have been preferable (11 [11] and 21 radiologists [12]). However, five radiologists is still a realistic number of readers in a single-centre study.…”

Section: Discussionmentioning

confidence: 99%

“…The most often used clinical classification of mammographic density is the qualitative Breast Imaging-Reporting and Data System (BI-RADS) [5]. Although afflicted with substantial interobserver variability (kappa 0.43–0.79) [6–12], mammographic density as classified by BI-RADS has consistently been associated with an increased risk of breast cancer [1, 13]. However, the latest BI-RADS 5th Edition aims to capture the risk of masking of tumors by dense breast tissue, more than the risk of developing breast cancer [5].…”

Section: Introductionmentioning

confidence: 99%

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Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists’ qualitative classification

et al. 2016

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ObjectivesBreast Imaging-Reporting and Data System (BI-RADS) mammographic density categories are associated with considerable interobserver variability. Automated methods of measuring volumetric breast density may reduce variability and be valuable in risk and mammographic screening stratification. Our objective was to assess agreement of mammographic density by a volumetric method with the radiologists’ classification.MethodsEight thousand seven hundred and eighty-two examinations from the Malmö Breast Tomosynthesis Screening Trial were classified according to BI-RADS, 4th Edition. Volumetric breast density was assessed using automated software for 8433 examinations. Agreement between volumetric breast density and BI-RADS was descriptively analyzed. Agreement between radiologists and between categorical volumetric density and BI-RADS was calculated, rendering kappa values.ResultsThe observed agreement between BI-RADS scores of different radiologists was 80.9 % [kappa 0.77 (0.76–0.79)]. A spread of volumetric breast density for each BI-RADS category was seen. The observed agreement between categorical volumetric density and BI-RADS scores was 57.1 % [kappa 0.55 (0.53-0.56)].ConclusionsThere was moderate agreement between volumetric density and BI-RADS scores from European radiologists indicating that radiologists evaluate mammographic density differently than software. The automated method may be a robust and valuable tool; however, differences in interpretation between radiologists and software require further investigation.Key Points• Agreement between qualitative and software density measurements has not been frequently studied. • There was substantial agreement between different radiologists´ qualitative density assessments. • There was moderate agreement between software and radiologists’ density assessments. • Differences in interpretation between software and radiologists require further investigation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists’ qualitative classification

et al. 2016

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“…Breast density, defined as the percentage of FGT within the breast, is an important aspect of breast cancer diagnosis, as dense breasts are associated with an increased risk of breast cancer and reduced mammography sensitivity [ 81 ]. Given the high interrater variability associated with visual assessment [ 81 ], automatic breast density estimation has been widely investigated, most commonly based on mammography [ 82 ] and, to a lesser extent, on MRI [ 34 , 83 , 84 ]. A possible way to estimate breast density is to classify each voxel as either fat or FGT and thus estimate the percentage of volume occupied by the latter.…”

Section: Perspectives Of Ai and Deep Learning In Breast Mrimentioning

confidence: 99%

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Meyer-Bäse

Morra

Meyer-Bäse

et al. 2020

Contrast Media & Molecular Imaging

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Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

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“…Breast density can be evaluated using either human-or computer-based methods. Human-derived breast density is usually assessed by means of categorical variables, for instance BIRADS breast density [13], but is unavoidably affected by inter-and intra-observer variability [14,15]. There are several computerbased methods using semi-automatic or fully automatic algorithms, capable of computing breast density as a percentage of breast area or breast volume [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Quantitative breast density in Contrast-Enhanced Mammography

Gennaro

Hill

Bezzon

et al. 2021

Preprint

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Background: Breast density is an independent risk factor for breast cancer, and cancer detection in mammography is reduced in dense breasts. Quantitative tools are available to measure breast density from digital mammography (DM) or tomosynthesis (DBT). Contrast-enhanced mammography (CEM) is an emerging breast imaging technique, consisting of the acquisition of an image pair (low-energy, LE, and high-energy, HE) for each mammography view. LE-CEM images have been demonstrated to be visually equivalent to a standard mammogram, thereby, CEM examinations do not require additional mammography to complete the clinical information. In this study, volumetric breast density (VBD) measured in LE-CEM was compared with VBD obtained from DM/DBT images.Methods: Between Mar 2019 and Dec 2020 222 women were enrolled in a prospective clinical trial aiming to compare clinical performance of CEM with breast MRI in a population of women at intermediate and high risk for breast cancer. In this observational cohort study, 150 women enrolled in this trial having at least a DM/DBT study performed before/after CEM were selected. CEM and previous/subsequent DM/DBT images were processed by an automatic algorithm to calculate VBD for each view. VBD from LE-CEM and DM/DBT views were compared using a paired Wilcoxon test. P < 0.05 was considered indicative of a statistically significant difference. A multivariate regression model was applied to analyze the relationship between paired VBD differences and multiple independent variables certainly or potentially affecting VBD.Results: Mean age of women included in this study was 51.0±8.4 years. Median VBD was comparable for LE-CEM and previous/subsequent DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (P = 0.5855). VBD differences between LE-CEM and DM were associated to significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance i.e. variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761).Conclusions: Volumetric breast density can be obtained from low-energy contrast-enhanced spectral mammography and is not significantly different from volumetric breast density measured from standard mammograms.

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Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset

Abstract: Individual BD assessment differs from PMR with κ as low as 0.483. An ABDE correctly classified 92 % of mammograms with almost perfect agreement (κ = 0.831). An ABDE can be a valid alternative to subjective BD assessment.

Cited by 15 publications

References 17 publications

Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists’ qualitative classification

Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists’ qualitative classification

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Quantitative breast density in Contrast-Enhanced Mammography

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