Evaluation of LIBRA Software for Fully Automated Mammographic Density                     Assessment in Breast Cancer Risk Prediction

Gastounioti, Aimilia; Kasi, Christine Damases; Scott, Christopher G.; Brandt, Kathleen R.; Jensen, Matthew R.; Hruska, Carrie B.; Wu, Fang; Norman, Aaron D.; Conant, Emily F.; Winham, Stacey J.; Kerlikowske, Karla; Kontos, Despina; Vachon, Celine M.

doi:10.1148/radiol.2020192509

Cited by 27 publications

(30 citation statements)

References 28 publications

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“…The importance of volumetric density estimates in further refining breast cancer risk assessment has been shown in previous studies (10,20), where breast cancer associations were highest for Volpara VPD% compared with semi-and fully automated APD% measures. However, both studies evaluated modelapproximated VPD% derived from raw DM, not from DBT volumes, as we did here.…”

Section: Discussionmentioning

confidence: 82%

“…Area-based density evaluation from DM.-The publicly available LIBRA software (version 1.0.4) (7), previously used in various research studies of breast density (20)(21)(22)(23), can calculate area-based density from either raw or processed DM images (8). Briefly, LIBRA partitions the breast region into density clusters of similar gray-level intensity, which are then aggregated into the final dense tissue segmentation.…”

Section: Breast Density Evaluation From Dbt and Dmmentioning

confidence: 99%

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Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis

et al. 2021

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B reast cancer risk assessment has become increasingly important in personalized breast cancer screening strategies (1), and numerous studies have consistently validated the strong relationship between increased breast density and breast cancer risk (2)(3)(4). The most commonly used breast density assessment method is visual assessment wherein breast density is categorized based on the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS), which is a subjective process (5). Most fully automated methods for breast density assessment, including ImageJ-based research methods (6) and the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software tool (7,8), provide area-based density metrics from two-dimensional (2D) digital mammography (DM) images (8-11). However, DM is rapidly being replaced by digital breast tomosynthesis (DBT), approved by the United States Food and Drug Administration in 2011 (12). In many facilities, DBT technology is also used to reconstruct "synthetic 2D images," which may make conventional DM obsolete (13). Therefore, in view of the rapid clinical conversion to DBT imaging, fully automated methods to measure breast density from DBT are becoming increasingly essential.In DBT, quasi-three-dimensional breast image volumes are reconstructed from a series of low-dose, 2D raw projection images acquired across a limited angle (14). By providing a quasi-three-dimensional image set, DBT offers the ability to quantify dense breast tissue volumetrically, which in turn may allow for more accurate breast density measures and improved breast cancer risk estimations (15). Attempts have been made by Food and Drug Administration-cleared commercial software vendors, such as Background: While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored.Purpose: To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods:This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age-and ethnicity-matched controls (September 19, 2011-January 6, 2015. Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index.Results: A total of 132 women diagnosed with breast cancer (mean age 6 standard...

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Section: Discussionmentioning

confidence: 82%

Section: Breast Density Evaluation From Dbt and Dmmentioning

confidence: 99%

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis

et al. 2021

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“…It analyzes full-eld digital mammography images based on gray-level values and segments them into dense and non-dense areas [31]. LIBRA has been validated by Gastouni et al [32], among others. Studies have revealed an association between dense LIBRA area and breast cancer [32].…”

Section: Mammographic Images and Image Analysis Systemsmentioning

confidence: 99%

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

Rejmer

Dihge

Bendahl

et al. 2023

Preprint

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Background: Cancer detection systems using artificial intelligence (AI) are a rapidly growing research area, in breast cancer. Sentinel lymph node biopsy (SLNB) is the recommended surgical axillary staging method in patients with clinically node-negative breast cancer, although approximately 75–80% have a negative sentinel lymph node (N0) status in the final pathology report. Previous prediction models for N0 status included variables only available postoperatively, thus defeating their purpose. Therefore, we aimed to investigate whether two AI systems, Transpara (Screenpoint Medical) and Laboratory for Individualized Breast Radiodensity Assessment (LIBRA), on mammographic images can be used to improve a previous prediction model for N0 status using only preoperatively available variables. To our knowledge, this is the first preoperative prediction model for N0 status combining AI detection on mammographic images with clinicopathological variables. Methods: This retrospective cohort study included 755 women with primary breast cancer treated at Lund University Hospital between 2009 and 2012. Mammographic images were analyzed using Transpara and LIBRA. Preoperative clinicopathological and radiological variables were used in a multivariable logistic regression analysis to predict N0 status with multiple imputation. The area under the receiver operating curve (AUC) was used to assess model performance and a nomogram was developed. The agreement between preoperative radiological and postoperative pathological tumor size was assessed using correlation. Results: We proposed a preoperative prediction model for N0 status using AI detection on mammographic images and clinicopathological variables, with an AUC of 0.695 (confidence interval: 0.653–0.736). Applying the model, SLNB could be putatively omitted in 23.8% of patients if a false-negative rate of 10% was accepted. The mean difference between radiologic and pathologic tumor size was 0.4 mm and the corresponding Pearson correlation coefficient 0.62. Conclusion: To our knowledge, the prediction model proposed in this manuscript is the first preoperative prediction model for N0 status using AI on mammographic images and routine preoperative patients and tumor characteristics. The correlation between tumor size measurements suggests that radiologic tumor size can replace pathologic size as a predictor of N0 status. Applying this model may enable safe omission of SLNB in 23.8% of patients.

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“…The lack of a standardized and easily implemented MD assessment continues to be a major impediment to widespread application of MD in clinical settings, but is the subject of robust research and technological development. Recent development of fully-automated methods to produce continuous and reliable MD measurement, and notable efforts to elucidate risk-relevant mammographic information that go beyond conventional MD measures (e.g., degree of brightness or spatial distribution of mammographic features), promise to offer more readily feasible and meaningful integration of MD in clinical risk stratification (8)(9)(10).…”

Section: MDmentioning

confidence: 99%

Less Is More—Ways to Move Forward for Improved Breast Cancer Risk Stratification

Tehranifar

Wei

Terry

2021

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Breast cancer risk models increasingly are including mammographic density (MD) and polygenic risk scores (PRS) to improve identification of higher-risk women who may benefit from genetic screening, earlier and supplemental breast screening, chemoprevention, and other targeted interventions. Here, we present additional considerations for improved clinical use of risk prediction models with MD, PRS, and questionnaire-based risk factors. These considerations include whether changing risk factor patterns, including MD, can improve risk prediction and management, and whether PRS could help inform breast cancer screening without MD measures and prior to the age at initiation of population-based mammography. We further argue that it may be time to reconsider issues around breast cancer risk models that may warrant a more comprehensive head-to-head comparison with other methods for risk factor assessment and risk prediction, including emerging artificial intelligence methods. With the increasing recognition of limitations of any single mathematical model, no matter how simplified, we are at an important juncture for consideration of these different approaches for improved risk stratification in geographically and ethnically diverse populations.See related article by Rosner et al., p. 600

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Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction

Cited by 27 publications

References 28 publications

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

Less Is More—Ways to Move Forward for Improved Breast Cancer Risk Stratification

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