Quantitative Assessment of Mammographic Breast Density: Relationship with Breast Cancer Risk

Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms

BACKGROUND AND OBJECTIVES

In the last decade, there has been a growing interest in applying artificial intelligence (AI) systems to breast cancer assessment, including breast density evaluation. However, few models have been developed to integrate textual mammographic reports and mammographic images. Our aims are (1) to generate a natural language processing (NLP)-based AI system, (2) to evaluate an external image-based software, and (3) to develop a multimodal system, using the late fusion approach, by integrating image and text inferences for the automatic classification of breast density according to the American College of Radiology (ACR) guidelines in mammograms and radiological reports.

METHODS

We first compared different NLP models, three based on n-gram term frequency - inverse document frequency and two transformer-based architectures, using 1533 unstructured mammogram reports as a training set and 303 reports as a test set. Subsequently, we evaluated an external image-based software using 303 mammogram images. Finally, we assessed our multimodal system taking into account both text and mammogram images.

RESULTS

Our best NLP model achieved 88 % accuracy, while the external software and the multimodal system achieved 75 % and 80 % accuracy, respectively, in classifying ACR breast densities.

CONCLUSION

Although our multimodal system outperforms the image-based tool, it currently does not improve the results offered by the NLP model for ACR breast density classification. Nevertheless, the promising results observed here open the possibility to more comprehensive studies regarding the utilization of multimodal tools in the assessment of breast density.

Cross-correlation adjustment full-waveform inversion with source encoding in ultrasound computed tomography

Full-waveform inversion (FWI) is one of the leading-edge techniques in ultrasound computed tomography (USCT). FWI reconstructs the images of sound speed by iteratively minimizing the difference between the predicted and measured signals. The challenges of FWI are to improve its stability and reduce its computational cost. In this paper, a new USCT algorithm based on cross-correlation adjustment FWI with source encoding (CCAFWI-SE) is proposed. In this algorithm, the gradient is adjusted using the intermediate signals as the inversion target rather than the measured signals during iteration. The intermediate signals are generated using the travel time difference calculated by cross-correlation. In the case of conventional FWI failure, using the proposed algorithm, the estimated sound speed can converge toward the ground truth. To reduce the computational cost, an intermittent update strategy is implemented. This strategy only requires one time for the calculation of the travel time difference per stage, so that the source encoding can be used. Simulation and laboratory experiments are implemented to validate this approach. The experiment results show it has successfully recovered the sound speed model, while conventional FWI failed when the initial model greatly differed from the ground truth. This verifies that our approach improves the stability of the reconstruction in USCT. In practice, additional computational costs can be reduced by combining our approach with existing methods. The proposed approach increases the robustness of the FWI and expands its application.

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis

BACKGROUND

Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated.

PURPOSE

To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories.

MATERIAL AND METHODS

A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates.

RESULTS

Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%.

CONCLUSION

The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

Patient Experience of Women With Dense Breasts Undergoing Screening Contrast-Enhanced Mammography

OBJECTIVE

We investigated patient experience with screening contrast-enhanced mammography (CEM) to determine whether a general population of women with dense breasts would accept CEM in a screening setting.

METHODS

In this institutional review board-approved prospective study, patients with heterogeneous and extremely dense breasts on their mammogram were invited to undergo screening CEM and complete pre-CEM and post-CEM surveys. On the pre-CEM survey, patients were asked about their attitudes regarding supplemental screening in general. On the post-CEM survey, patients were asked about their experience undergoing screening CEM, including causes and severity of any discomfort and whether they would consider undergoing screening CEM again in the future or recommend it to a friend.

RESULTS

One hundred sixty-three women were surveyed before and after screening CEM. Most patients, 97.5% (159/163), reported minimal or no unpleasantness associated with undergoing screening CEM. In addition, 91.4% (149/163) said they would probably or very likely undergo screening CEM in the future if it cost the same as a traditional screening mammogram, and 95.1% (155/163) said they would probably or very likely recommend screening CEM to a friend. Patients in this study, who were all willing to undergo CEM, more frequently reported a family history of breast cancer than a comparison cohort of women with dense breasts (58.2% vs 47.1%, P = .027).

CONCLUSION

Patients from a general population of women with dense breasts reported a positive experience undergoing screening CEM, suggesting screening CEM might be well received by this patient population, particularly if the cost was comparable with traditional screening mammography.

Application value of MRI-guided wire localization to the non-palpable breast lesions only shown in Breast MRI

Introduction

Magnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women.

Methods

A total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed.

Results

All of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P<0.05). All the lesions were assessed according to BI-RADS category of MRI (C4A=77, C4B=40, C4C=12, C5=2). The malignancy rates were as follows: 16.88% for 4A lesions (13/77), 37.50% for 4B lesions (15/40), 75.00% for 4C lesions (9/12) and 100% for 5 lesions (2/2). There was a significant correlation between the BI-RADS category and the incidence of benign-to-malignant lesions (P<0.001).

Conclusion

MRI-guided wire localization can assist to remove suspected breast lesions early, safely and accurately. This technique makes up for the deficiency of X-ray and ultrasound, improves the accuracy of diagnosis and resection therapy in intraductal carcinoma and early invasive carcinoma, and helps to improve the the prognosis of breast cancer.

Mammographic density changes after neoadjuvant chemotherapy in triple-negative breast cancer: Association with treatment and survival outcome

PURPOSE

To investigate the association of mammographic breast density with treatment and survival outcomes in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).

METHODS

This retrospective study evaluated 306 women with TNBC who underwent NAC followed by surgery between 2010 and 2019. The baseline density and the density changes after NAC were evaluated. Qualitative breast density (a-d) was evaluated using the Breast Imaging Reporting and Data System. Quantitative breast density (%) was evaluated using fully automated software (the Laboratory for Individualized Breast Radiodensity Assessment) in the contralateral breast. Multivariable logistic regression analysis was used to evaluate the association between breast density and pathologic complete response (pCR), stratified by menopausal status. Cox proportional hazard regression analysis was used to evaluate the association among breast density, the development of contralateral breast cancer, and the development of locoregional recurrence and/or distant metastasis.

RESULTS

Contralateral density reduction ≥10 % was independently associated with pCR in premenopausal women (odds ratio [OR], 2.5; p = 0.022) but not in postmenopausal women (OR, 0.9; p = 0.823). During a mean follow-up of 65 months, 10 (3 %) women developed contralateral breast cancer, and 68 (22 %) women developed locoregional recurrences and/or distant metastases. Contralateral density reduction ≥10 % showed no association with the occurrence of contralateral breast cancer (hazard ratio [HR], 3.1; p = 0.308) or with locoregional recurrence and/or distant metastasis (HR, 1.1; p = 0.794).

CONCLUSION

In premenopausal women, a contralateral breast density reduction of ≥10 % after NAC was independently associated with pCR, although it did not translate into improved outcomes.

Liquid Chromatography Tandem Mass Spectrometric Method for Quantification of Margetuximab in Rat Plasma and Application to a Pharmacokinetic Study

Margetuximab was approved for the treatment of advanced HER2+ breast cancer. A feasible analytical technique that can measure this drug was obligatory. In light of this, a novel and thoroughly validated liquid chromatographic (LC)-tandem mass spectrometric (MS/MS) approach was developed for the quantification of margetuximab in rat plasma. The liquid-liquid extraction method was used to extract the analyte from rat plasma. The analyte was separated using acetonitrile and formic acid buffer (30:70) as a mobile phase on Waters, alliance e-2695 model HPLC having Symmetry C18 column, 150 mm × 4.6 mm, 3.5-µm column. The overall runtime was 6 min at a flow rate of 1.0 ml/min. The method showed significant sensitivity and acceptable linearity over the concentration range of 6-120 ng/ml. Accuracy was within 98.51-99.92%. The intraday precision ranged between 0.41 and 8.98% CV. Also, the findings of pharmacokinetic parameters such as Cmax, tmax, AUC0-∞, AUC0-t, and half-life results of margetuximab showed that the technique was helpful for accurately measuring drug concentrations in rat plasma. The method that was developed was useful and effective for quantifying margetuximab.

Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient's Age and Breast Quadrant

OBJECTIVES

Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age.

METHODS

The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method.

RESULTS

The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast.

CONCLUSIONS

The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features-BTV, MGV, and PBD, as a function of the patient's age.

Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China

BACKGROUND

Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China.

METHODS

This is a population-based screening study. 12518 women aged 45-64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the ORtrend (95%CI) and Ptrend by treating MD as an ordinal variable.

RESULTS

Older age (ORtrend = 0.81, 95%CI: 0.79-0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71-0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41-0.54, 3 + vs. 0-1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38-0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27-1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20-1.75, 19-24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02-1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21-1.43).

CONCLUSIONS

In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China.

Pearls and Pitfalls of Interpretation of Automated Breast US

Dense breast tissue is an independent risk factor for breast cancer and reduces the sensitivity of mammography. Patients with dense breast tissue are more likely to present with interval cancers and higher-stage disease. Successful breast cancer screening outcomes rely on detection of early-stage breast cancers; therefore, several supplemental screening modalities have been developed to improve cancer detection in dense breast tissue. US is the most widely used supplemental screening modality worldwide and has been proven to demonstrate additional mammographically occult cancers that are predominantly invasive and node negative. According to the American College of Radiology, intermediate-risk women with dense breast tissue may benefit from adjunctive screening US due to the limitations of mammography. Several studies have demonstrated handheld US (HHUS) and automated breast US (AUS) to be comparable in the screening setting. The advantages of AUS over HHUS include lack of operator dependence and a formal training requirement, image reproducibility, and ability for temporal comparison. However, AUS exhibits unique features that can result in high false-positive rates and long interpretation times for new users. Familiarity with the common appearance of benign mammographic findings and artifacts, technical challenges, and unique AUS features is essential for fast, efficient, and accurate interpretation. The goals of this article are to (a) examine the role of AUS as a supplemental screening modality and (b) review the pearls and pitfalls of AUS interpretation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Low frequency 3D transmission ultrasound tomography: technical details and clinical implications

A novel 3D ultrasound tomographic (3D UT) method (called volography) that creates a speed of sound (SOS) map and a reflection modality that is co-registered are reviewed and shown to be artifact free even in the presence of high contrast and thus shown to be applicable for breast, orthopedic and pediatric clinical use cases. The 3D UT images are almost isotropic with mm resolution and the reflection image is compounded over 360 degrees to create sub-mm resolution in plane.

METHODS

The physics of ultrasound scattering requires 3D modeling and the concomitant high computational cost is ameliorated with a bespoke algorithm (paraxial approximation - discussed here) and Nvidia GPUs. The resulting reconstruction times are tabulated for clinical relevance. The resulting SOS map is used to create a refraction corrected reflection image at ∼3.6 MHz center frequency. The transmission data are highly redundant, collected over 360 degrees and at 2 mm levels by true matrix receiver arrays yielding 3D data. The high resolution SOS and attenuation maps and reflection images are used in a segmentation algorithm that optimally utilizes this information to segment out glandular, ductal, connective tissue, fat and skin. These volumes are used to estimate breast density, an important correlate to cancer.

RESULTS

Multiple SOS images of breast, knee and segmentations of breast glandular and ductal tissue are shown. Spearman rho is calculated between our volumetric breast density estimates and Volpara™ from mammograms, as 0.9332. Multiple timing results are shown and indicate the variability of the reconstruction times with breast size and type but are ∼30 minutes for average size breast. The timing results with the 3D algorithm indicate ∼60 minute reconstruction times for pediatrics with two Nvidia GPUs. Characteristic variations of the glandular and ductal volumes over time are shown. The SOS from QT images are compared with literature values. The results of a multi-reader multi-case (MRMC) study are shown that compares the 3D UT with full field digital mammography and resulted in an average increase in ROC AUC of 10%. Orthopedic (knee) 3D UT images compared with MRI indicate regions of zero signal in the MRI are clearly displayed in the QT image. Explicit representation of the acoustic field is shown, indicating its 3D nature. An image of in vivo breast with the chest muscle is shown and speed of sound agreement with literature values are tabulated. Reference is made to a recently published paper validating pediatric imaging.

CONCLUSIONS

The high Spearman rho indicates a monotonic (not necessarily linear) relation between our method and industry gold standard Volpara™ density. The acoustic field verifies the need for 3D modeling. The MRMC study, the orthopedic images, breast density study, and references, all indicate the clinical utility of the SOS and reflection images. The QT image of the knee shows its ability to monitor tissue the MRI cannot. The included references and images herein indicate the proof of concept for 3D UT as a viable and valuable clinical adjunct in pediatric and orthopedic situations in addition to the breast imaging.

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

Factors Associated With Breast Cancer Screening Behaviors Among Women With Dense Breasts

OBJECTIVE

We sought to identify patient factors associated with patient-reported screening behaviors in women with dense breasts.

METHODS

An IRB-approved survey study of women with dense breasts presenting for annual screening mammography at an outpatient imaging center was previously conducted from March 2017 to February 2018. The survey included questions regarding mammographic screening frequency and recent participation in supplemental screening. These survey data were combined post hoc with clinical and demographic data and socioeconomic data imputed from census data. Logistic regression was used to identify patient factors associated with reported screening behaviors.

RESULTS

Surveys were completed by 508 women (median age, 59.0 years; range, 31.0-86.0 years) with dense breasts. Multivariable analysis demonstrated an independent association of undergoing mammographic screening annually with a history of discussing breast density with a doctor (adjusted odds ratio [AOR], 2.60; P = 0.019). Undergoing supplemental screening in the previous three years was independently associated with younger age (AOR, 1.59; P = 0.004), strong family history of breast cancer (AOR, 3.84; P = 0.027), higher perceived personal risk for breast cancer (AOR, 3.47; P = 0.004), and increased concern about radiation associated with screening examinations (AOR, 3.31; P = 0.006).

CONCLUSION

Women with dense breasts who had discussed breast density with a doctor were more likely to report undergoing annual screening mammography, while younger women and women with a strong family history of breast cancer, higher perceived personal risk for breast cancer, or greater concern about radiation were more likely to report recently undergoing supplemental screening.

Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions

Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.

Prediction of breast cancer risk by automated volumetric breast density measurement

PURPOSE

Dense breast (DB) is recognized as a breast cancer (BC) risk factor. Although DB is common in Japanese women, the incidence of BC is lower than in Caucasians. We evaluated whether DB is a risk factor or whether there are other risk factors for BC in Japanese women.

MATERIALS AND METHODS

We retrospectively analyzed 635 BC patients and 999 controls who received a mammography at our hospital between February 2019 and March 2021. Volumetric breast density percentage (VBD%), breast volume (BV), and fibroglandular volume (FGV) were measured using Volpara™, an automated, three-dimensional image analysis program. A VBD% of 7.5% or higher was classified as DB. The association between the VBD%, BV, and FGV, and BC risk were assessed using logistic regression.

RESULTS

Of the BC group and the control group, 77% and 79% had DB. The stratified FGV was positively associated with BC risk (odds ratio: 2.84; 95% confidence interval 1.58-5.12; P < 0.001). No significant association was found between either the VBD% or BV and BC risk.

CONCLUSION

The proportion of Japanese women with DB was high, suggesting that DB might not be significantly associated with BC risk. However, our results also suggested that the FGV may be related to BC risk in Japanese women.

Artificial Intellgence in the Era of Precision Oncological Imaging

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

A Prospective Comparative Evaluation of Handheld Ultrasound Examination (HHUS) or Automated Ultrasound Examination (ABVS) in Women with Dense Breast

Mammography is the gold standard examination for breast cancer screening. In women with high breast density, mammography has reduced sensitivity. In these women, an additional screening option is often recommended. This study prospectively compared ABVS and HHUS in women with mammography-negative examinations and dense breasts. Materials and methods: N = 222 women were evaluated prospectively and consecutively between January 2019 and June 2019 (average age 53 years; range 39−89). McNemar’s test and ROC analysis were used with standard statistical software. We included in the study both symptomatic and asymptomatic women with dense breasts. Women included underwent both HHUS and ABVS after mammography with independent reading. Results: N = 33/222 (15%) women resulted in having breast cancer. Both ABVS and HHUS identified more cancers than standard mammography, and both HHUS and ABVS had false-positive examinations: n = 13 for HHUS and n = 12 for ABVS. We found that HHUS had better accuracy than ABVS. The AUC of the ROC was 0.788 (95% CI 0.687−0.890) for ABVS and 0.930 (95% CI 0.868−0.993) for HHUS. This difference was statistically significant (p < 0.05). Conclusions: HHUS was more accurate in breast cancer detection than ABVS. Multicentric studies must confirm these data for supplemental imaging in women with dense breasts.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Fully automated breast segmentation on spiral breast computed tomography images

Introduction

The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon‐counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components—the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant—is required. We propose a fully automated breast segmentation method for breast CT images.

Methods

The framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five‐point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists.

Results

The performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90–0.97 and in DCs 0.01–0.08. The readers rated 4.5–4.8 (5 highest score) with an excellent inter‐reader agreement. The breast density varied by 3.7%–7.1% when including mis‐segmented muscle or skin.

Conclusion

The automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

Screening MRI in Women at Intermediate Breast Cancer Risk: An Update of the Recent Literature

Guidelines issued by the American Cancer Society (ACS) in 2007 recommend neither for nor against screening MRI in women at intermediate breast cancer risk (15%–20%), including those with dense breast tissue, a history of lobular neoplasia or atypical ductal hyperplasia (ADH), or a prior breast cancer, because of scarce supporting evidence about the utility of MRI in these specific patient populations. However, since the issuance of the ACS guidelines in 2007, multiple investigations have found that women at intermediate risk may be suitable candidates for screening MRI, given the high detection rates of early-stage cancers and acceptable false-positive rates. For women with dense breast tissue, the Dense Tissue and Early Breast Neoplasm Screening trial reported that the incremental cancer detection rate (CDR) by MRI exceeded 16 cancers per 1000 examinations but decreased in the second round of screening; this decrease in CDR, however, occurred alongside a marked decrease in the false-positive rate. For women with lobular neoplasia or ADH, single-institution retrospective analyses have shown CDRs mostly ranging from 11 to 16 cancers per 1000 MRI examinations, with women with lobular carcinoma in situ benefitting more than women with atypical lobular hyperplasia or ADH. For patients with a prior breast cancer, the cancer yield by MRI varies widely but mostly ranges from 8 to 20 cancers per 1000 examinations, with certain subpopulations more likely to benefit, such as those with dense breasts. This article reviews and summarizes more recent studies on MRI screening of intermediate-risk women.

Breast Density Lacks Influence on Upgrade Rates to High Risk Lesions and Cancer Among Proliferative Breast Lesion Excisions

INTRODUCTION

Breast density is an independent risk factor for the development of breast cancer. We assessed if upgrade rates to high-risk lesions and cancer were influenced by density when evaluating proliferative complex sclerosing lesions and intraductal papillomas.

METHODS

This is a retrospective single institution study. We evaluated 168 women aged 18-86 who received a core needle biopsy revealing a breast proliferative lesion of complex sclerosing lesion (CSL) or intraductal papilloma. We analyzed the upgrade rate to high-risk atypia (HRL) and cancer. Subgroup analysis based on age and breast density was performed.

RESULTS

The patient collective was well balanced-51% had dense breasts and 42% were under 50 years old. Half were diagnosed with papilloma based on CNB and the other half with CSL. For those proliferative lesions without atypia, the upgrade rate to cancer was 1.6%. CNB showed concomitant HRL in 23% of patients with non-dense breasts and in 22% with dense tissue. In 24 cases, the pathology was considered an upgrade by showing either a not prior noted HRL or carcinoma. Most patients with upgrade following surgical excision were over 50 years old. Dense breasts did not show a higher risk of upgrade following surgical excision (P = .975).

CONCLUSION

Our data did not reveal a difference between upgrade rates of proliferative lesions excised in dense and non-dense breasts. Further evaluation is warranted to establish whether density should be considered as a meaningful factor in excision vs observation of CSL and papillomas.

Deep-learning-based projection-domain breast thickness estimation for shape-prior iterative image reconstruction in digital breast tomosynthesis

BACKGROUND

Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and in-plane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation.

PURPOSE

In this study, we propose a deep-learning-based methodology for projection-domain breast thickness estimation and demonstrate a shape-prior iterative DBT image reconstruction.

METHODS

We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin-line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the x-ray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel-wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique (DART).

RESULTS

The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root-mean-squared error (NRMSE; 1.976%) and high structural similarity (SSIM; 99.997%) indicated a good agreement between the network-generated thickness corrected image and the ground-truth image. Compared to the existing methods and simple single-input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape-prior for the iterative DBT reconstruction.

CONCLUSIONS

We presented a novel deep-learning-based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation. This article is protected by copyright. All rights reserved.

Family History of Breast Cancer and Mammographic Breast Density in Premenopausal Women

Importance

Family history of breast cancer (FHBC) and mammographic breast density are independent risk factors for breast cancer, but the association of FHBC and mammographic breast density in premenopausal women is not well understood.

Objectives

To investigate the association of FHBC and mammographic breast density in premenopausal women using both quantitative and qualitative measurements.

Design, Setting, and Participants

This single-center cohort study examined 2 retrospective cohorts: a discovery set of 375 premenopausal women and a validation set of 14 040 premenopausal women. Data from women in the discovery set was collected between December 2015 and October 2016, whereas data from women in the validation set was collected between June 2010 and December 2015. Data analysis was performed between June 2018 and June 2020.

Exposures

Family history of breast cancer (FHBC).

Main Outcomes and Measures

The primary outcomes were mammographic breast density measured quantitatively as volumetric percent density using Volpara (discovery set) and qualitatively using BI-RADS (Breast Imaging Reporting and Data System) breast density (validation set). Multivariable regressions were performed using a log-transformed normal distribution for the discovery set and a logistic distribution for the validation set.

Results

Of 14 415 premenopausal women included in the study, the discovery set and validation set had similar characteristics (discovery set with FHBC: mean [SD] age, 47.1 [5.6] years; 15 [17.2%] were Black or African American women and 64 [73.6%] were non-Hispanic White women; discovery set with no FHBC: mean [SD] age, 47.7 [4.5] years; 87 [31.6%] were Black or African American women and 178 [64.7%] were non-Hispanic White women; validation set with FHBC: mean [SD] age, 46.8 [7.3] years; 720 [33.4%] were Black or African American women and 1378 [64.0%] were non-Hispanic White women]; validation set with no FHBC: mean [SD] age, 47.5 [6.1] years; 4572 [38.5%] were Black or African American women and 6632 [55.8%] were non-Hispanic White women]). In the discovery set, participants who had FHBC were more likely to have a higher mean volumetric percent density compared with participants with no FHBC (11.1% vs 9.0%). In the multivariable-adjusted model, volumetric percent density was 25% higher (odds ratio [OR], 1.25 ;95% CI, 1.12-1.41) in women with FHBC compared with women without FHBC; and 24% higher (OR, 1.24; 95% CI, 1.10-1.40) in women who had 1 affected relative, but not significantly higher in women who had at least 2 affected relatives (OR, 1.40; 95% CI, 0.95-2.07) compared with women with no relatives affected. In the validation set, women with a positive FHBC were more likely to have dense breasts (BI-RADS 3-4) compared with women with no FHBC (BI-RADS 3: 41.1% vs 38.8%; BI-RADS 4: 10.5% vs 7.7%). In the multivariable-adjusted model, the odds of having dense breasts (BI-RADS 3-4) were 30% higher (OR, 1.30; 95% CI, 1.17-1.45) in women with FHBC compared with women without FHBC; and 29% higher (OR, 1.29; 95% CI, 1.14-1.45) in women who had 1 affected relative, but not significantly higher in women who had at least 2 affected relatives (OR, 1.38; 95% CI, 0.85-2.23) compared with women with no relatives affected.

Conclusions and Relevance

In this cohort study, having an FHBC was positively associated with mammographic breast density in premenopausal women. Our findings highlight the heritable component of mammographic breast density and underscore the need to begin annual screening early in premenopausal women with a family history of breast cancer.

Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic

PURPOSE

A personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment.

METHODS

Women underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations.

RESULTS

A total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk.

CONCLUSION

Individual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.

OUP accepted manuscript

In screening for breast cancer (BC), mammographic breast density (MBD) is a powerful risk factor that increases breast carcinogenesis and synergistically reduces the sensitivity of mammography. It also reduces specificity of lesion identification, leading to recalls, additional testing, and delayed and later-stage diagnoses, which result in increased health care costs. These findings provide the foundation for dense breast notification laws and lead to the increase in patient and provider interest in MBD. However, unlike other risk factors for BC, MBD is dynamic through a woman’s lifetime and is modifiable. Although MBD is known to change as a result of factors such as reproductive history and hormonal status, few conclusions have been reached for lifestyle factors such as alcohol, diet, physical activity, smoking, body mass index (BMI), and some commonly used medications. Our review examines the emerging evidence for the association of modifiable factors on MBD and the influence of MBD on BC risk. There are clear associations between alcohol use and menopausal hormone therapy and increased MBD. Physical activity and the Mediterranean diet lower the risk of BC without significant effect on MBD. Although high BMI and smoking are known risk factors for BC, they have been found to decrease MBD. The influence of several other factors, including caffeine intake, nonhormonal medications, and vitamins, on MBD is unclear. We recommend counseling patients on these modifiable risk factors and using this knowledge to help with informed decision making for tailored BC prevention strategies.

Breast Density Knowledge and Awareness Among Latinas in a Low-Resource Setting

PURPOSE

Latinas in low-resource settings face additional barriers to understanding mammographic breast density (MBD) implications. The authors compared MBD awareness and knowledge in Latinas from a safety-net clinic in Arizona with a national sample.

METHODS

Latinas 40 to 74 years of age were recruited within a safety-net clinic during screening mammography appointments from 2016 to 2019 (AZ cohort) and from a nationally representative online panel in 2017 (NS cohort). Surveys completed in either English or Spanish assessed awareness and knowledge of MBD. Chi-square tests and logistic regression were used for comparisons.

RESULTS

The NS cohort (n = 152) was older, more educated, more likely to have undergone prior mammography, and more likely to prefer English compared with the AZ cohort (n = 1,327) (P ≤ .03 for all) The NS cohort was more likely to be aware of MBD (32.6% versus 20.7%). Of those aware, the NS cohort was more likely to understand MBD's effect on masking (67.8% versus 37.0%) and breast cancer risk (72.2% versus 32.6%) compared with the AZ cohort (P ≤ .001 for all). Adjusting for age, education, screening history, and language, MBD awareness was similar between the two cohorts (adjusted odds ratio [ORadj], 0.95; P = .83), but knowledge of MBD as a masking factor (ORadj, 2.8; P = .03) and risk factor (ORadj, 7.2; P < .001) remained higher in the NS cohort compared with the AZ cohort.

CONCLUSIONS

Differences in MBD awareness, but not knowledge, between Latinas in a low-resource setting compared with a national sample could be explained by age, education, screening history, and language preference, underscoring the need for tailored approaches to MBD education among Latinas.

Evaluation of the Value of Multiplex MicroRNA Analysis as a Breast Cancer Screening in Korean Women under 50 Years of Age with a High Proportion of Dense Breasts

This study was conducted to confirm the performance of the microRNA (miRNA) biomarker combination as a new breast cancer screening method in Korean women under the age of 50 with a high percentage of dense breasts. To determine the classification performance of a set of miRNA biomarkers (miR-1246, 202, 21, and 219B) useful for breast cancer screening, we determined whether there was a significant difference between the breast cancer and healthy control groups through box plots and the Mann–Whitney U-test, which was further examined in detail by age group. To verify the classification performance of the 4 miRNA biomarker set, 4 classification methods (logistic regression, random forest, XGBoost, and generalized linear model plus random forest) were applied, and 10-fold cross-validation was used as a validation method to improve performance stability. We confirmed that the best breast cancer detection performance was achievable in patients under 50 years of age when the set of 4 miRNAs were used. Under the age of 50, the 4 miRNA biomarkers showed the highest performance with a sensitivity of 85.29%, specificity of 93.33%, and area under the curve (AUC) of 0.961. Examining the results of 4 miRNA biomarkers was found to be an effective strategy for diagnosing breast cancer in Korean women under 50 years of age with dense breasts, and hence has the potential as a new breast cancer screening tool. Further validation in an appropriate screening population with large-scale clinical trials is required.

Optimal Breast Density Characterization Using a Three-Dimensional Automated Breast Densitometry System

Dense breasts are a risk factor for breast cancer. Assessment of breast density is important and radiologist-dependent. We objectively measured mammographic density using the three-dimensional automatic mammographic density measurement device Volpara™ and examined the criteria for combined use of ultrasonography (US). Of 1227 patients who underwent primary breast cancer surgery between January 2019 and April 2021 at our hospital, 441 were included. A case series study was conducted based on patient age, diagnostic accuracy, effects of mammography (MMG) combined with US, size of invasion, and calcifications. The mean density of both breasts according to the Volpara Density Grade (VDG) was 0–3.4% in 2 patients, 3.5–7.4% in 55 patients, 7.5–15.4% in 173 patients, and ≥15.5% in 211 patients. Breast density tended to be higher in younger patients. Diagnostic accuracy of MMG tended to decrease with increasing breast density. US detection rates were not associated with VDG on MMG and were favorable at all densities. The risk of a non-detected result was high in patients without malignant suspicious calcifications. Supplementary use of US for patients without suspicious calcifications on MMG and high breast density, particularly ≥25.5%, could improve the breast cancer detection rate.

Mammographic Breast Density and Acculturation: Longitudinal Analysis in Chinese Immigrants

Breast cancer is the most common cancer in women. Asian American women have experienced steadily increasing breast cancer incidence rates over the past several decades. The increased rate might be in part due to acculturation. We tested the hypothesis that higher level of acculturation was associated with higher mammographic breast density (MBD), an indicator of breast cancer risk, in a cohort of 425 premenopausal Chinese immigrant women in Philadelphia. Generalized estimating equations accounted for repeated observations and adjusted for age, type of mammographic image, body mass index, months of breastfeeding, number of live births, age at first birth, and menopausal stage (pre, early peri, late peri, post). Results indicated that acculturation level was not associated with any of the MBD measures. Findings were contrary to our hypothesis and previous, cross-sectional studies. In this study population, reproductive factors had a greater effect on MBD than acculturation-related behaviors in adulthood.

Influence of Menopausal Hormone Therapy on the Breast: Counseling Your Patients Before You Prescribe

Menopausal hormone therapy (HT) aims to improve a woman's quality of life by treating bothersome menopausal symptoms associated with low estrogen levels. Although HT is prescribed to millions of women worldwide, its breast-related adverse effects have always been a concern. Some of the common adverse effects of HT are breast fullness, increased breast density, and increased breast cancer (BC) risk. Health care professionals need to be aware of the influence of HT on breast tissue to provide appropriate counseling as part of informed decision making. Our review summarizes the influence of HT on breast symptoms, breast density, mammograms, and BC risk.

Opportunities in cancer imaging: risk-adapted breast imaging in screening

In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.

Monochromatic X-rays: The future of breast imaging

PURPOSE

To present details about the innovative and disruptive technology of monochromatic X-rays and its application to breast imaging.

METHODS

To analyze results of studies done using a prototype system for breast imaging that generates monochromatic X-rays through fluorescence emission. To assess signal-to-noise ratio (SNR) as a measure of image quality at different doses in breast phantoms of different sizes and review the comparison of parameters with a standard mammography system.

RESULTS

Monochromatic X-rays reduce the radiation dose per mammogram by a factor of 5 to 10 times. For phantom simulating thick breast (9 cm), the SNR for monochromatic system was 2.6 times higher and the dose 4.2 times lower than the respective values obtained with the conventional system within the same 5 mm × 5 mm square area of the 100% glandular step wedge. For the conventional broadband system to equal the SNR of the monochromatic system, it would require a dose of 19 mGy, 29 times higher than the dose delivered by the monochromatic system. Contrast-enhanced digital mammography with monochromatic X-rays is shown to provide a simpler and more effective technique at substantially lower radiation dose.

CONCLUSIONS

Lowering radiation dose by a factor of 5 to 10 while maintaining image quality implies a major reduction in total exposure from breast cancer screening and dramatically less risk of radiation-induced cancers in at-risk women. The high SNRs for very thick breast phantoms provide strong evidence that screening with lower breast compression is possible while maintaining image quality.

Patient Characteristics Associated With Patient-Reported Deterrents to Adjunct Breast Cancer Screening of Patients With Dense Breasts

BACKGROUND. The success of adjunct breast cancer screening of women with dense breasts can be enhanced by identifying and addressing patient concerns regarding adjunct screening modalities. OBJECTIVE. The purpose of this study was to identify patient characteristics associated with patient-reported concerns about adjunct breast cancer screening to facilitate the development of a more effective screening model for women with dense breasts. METHODS. Patients with dense breasts completed surveys between March 2017 and February 2018 regarding factors that might deter them from adjunct screening and about which of three hypothetical screening examinations they might prefer. Additional patient data were extracted from medical records, and socioeconomic data were imputed from federal census data. Logistic regression analyses were conducted to identify associations between patient characteristics and patient attitudes toward adjunct screening. RESULTS. Surveys were completed by 508 women (median age, 59.0 years) with dense breasts. Lower confidence in the sensitivity of mammography of dense breasts was independently associated with lesser concern about adjunct screening examination time (1 divided by adjusted odds ratio [1/AOR], 0.55 [95% CI, 0.34-0.89]), additional imaging that could result (1/AOR, 0.51 [95% CI, 0.31-0.85]), and greater preference for a more sensitive hypothetical screening examination (1/AOR, 1.85 [95% CI, 1.20-2.86]). Concern about examination cost, the most commonly cited deterrent to adjunct screening (66.9%), was independently associated with younger age (1/AOR, 1.45 [95% CI, 1.01-2.08]) but not with imputed socioeconomic variables or other tested variables. Younger age was also associated with lesser concern about pain (1/AOR, 0.69 [95% CI, 0.48-0.99]), additional imaging that could result (1/AOR, 0.48 [95% CI, 0.31-0.76]), and IV contrast administration (1/AOR, 0.56 [95% CI, 0.37-0.83]). CONCLUSION. Younger age and lower confidence in the sensitivity of mammography among women with dense breasts are independently associated with lesser patient concern about common deterrents to adjunct breast cancer screening. Younger age is independently associated with greater concern about the cost of undergoing adjunct breast cancer screening. CLINICAL IMPACT. Concerns about adjunct screening may be reduced by educating patients about the lower sensitivity of mammography of dense breasts and by finding ways to address or mitigate the financial and daily-life impact of adjunct screening, especially for younger patients.

BI-RADS 3 on dense breast screening ultrasound after digital mammography versus digital breast tomosynthesis

OBJECTIVE

Compare the BI-RADS 3 rate and follow-up of dense breast ultrasound (US) screening following digital mammography (DM) versus digital breast tomosynthesis (DBT).

METHODS

IRB-approved, HIPAA compliant retrospective search was performed of databases at two tertiary breast centers and an office practice for BI-RADS 3 screening US examinations performed 10/1/14-9/30/16. Prior DM versus DBT, downgrade and upgrade rate, and timing and pathology results were recorded. Differences were compared using the two-sample proportions test.

RESULTS

3183 screening US examinations were performed, 1434/3183 (45.1%) after DM and 1668/3183 (52%) after DBT (2.5% (81/3183) no prior mammogram available). 13.9% (199/1434) had BI-RADS 3 results after DM and 10.6% (177/1668) after DBT (p < 0.01). Median imaging follow-up after DM was 12 months (IQR 6, 24) versus 18 after DBT (IQR 11, 25), p = 0.02. 19.5% (73/375) of patients were lost to follow-up (19.2% (38/198) after DM (68.4% (26/38) no follow-up after initial exam) versus 19.8% (35/177) after DBT (54.3% (19/35) no follow-up after initial exam). 1.3% (5/375) of patients elected biopsy (1.5% (3/198) after DM and 1.1% (2/177) after DBT). 75.2% (282/375) of patients were downgraded (75.3% (149/198) after DM and 75.1% (133/177) after DBT). 2.5% (5/198) were upgraded after DM and 0.6% (1/177) after DBT. Median time to upgrade was 6 months after both DM and DBT. 0.3% (1/375) of patients with BI-RADS 3 results had cancer on follow-up.

CONCLUSION

Patients with prior DBT had a lower risk of encountering BI-RADS 3 findings on screening ultrasound. BI-RADS 3 findings on screening ultrasound had an extremely low rate of being cancer.

Model-based optical and acoustical compensation for photoacoustic tomography of heterogeneous mediums

Photoacoustic tomography (PAT) is a non-invasive, high-resolution imaging modality, capable of providing functional and molecular information of various pathologies, such as cancer. One limitation of PAT is the depth and wavelength dependent optical fluence, which results in reduced PA signal amplitude from deeper tissue regions. These factors can therefore introduce errors into quantitative measurements such as oxygen saturation (sO₂) or the localization and concentration of various chromophores. The variation in the speed-of-sound between different tissues can also lead to distortions in object location and shape. Compensating for these effects allows PAT to be used more quantitatively. We have developed a proof-of-concept algorithm capable of compensating for the heterogeneity in speed-of-sound and depth dependent optical fluence. Speed-of-sound correction was done by using a straight ray-based algorithm for calculating the family of iso-time-of-flight contours between the transducers and every pixel in the imaging grid, while fluence compensation was done by utilizing the graphics processing unit (GPU) accelerated software MCXCL for Monte Carlo modeling of optical fluence variation. This algorithm was tested on a polyvinyl chloride plastisol (PVCP) phantom, which contained cyst mimics and blood inclusions to test the algorithm under relatively heterogeneous conditions. Our results indicate that our PAT algorithm can compensate for the speed-of-sound variation and depth dependent fluence effects within a heterogeneous phantom. The results of this study will pave the way for further development and evaluation of the proposed method in more complex in-vitro and ex-vivo phantoms, as well as compensating for the wavelength-dependent optical fluence in spectroscopic PAT.

Breast Acoustic Parameter Reconstruction Method Based on Capacitive Micromachined Ultrasonic Transducer Array

Ultrasound computed tomography (USCT) systems based on capacitive micromachined ultrasonic transducer (CMUT) arrays have a wide range of application prospects. For this paper, a high-precision image reconstruction method based on the propagation path of ultrasound in breast tissue are designed for the CMUT ring array; that is, time-reversal algorithms and FBP algorithms are respectively used to reconstruct sound speed distribution and acoustic attenuation distribution. The feasibility of this reconstruction method is verified by numerical simulation and breast model experiments. According to reconstruction results, sound speed distribution reconstruction deviation can be reduced by 53.15% through a time-reversal algorithm based on wave propagation theory. The attenuation coefficient distribution reconstruction deviation can be reduced by 61.53% through FBP based on ray propagation theory. The research results in this paper will provide key technological support for a new generation of ultrasound computed tomography systems.

Quantitative breast density measurement based on three-dimensional images: a study on cone-beam breast computed tomography

BACKGROUND

Breast density is an independent predictor of breast cancer risk. Quantitative volumetric breast density (QVBD) is expected to provide more information on the prediction of breast cancer risk.

PURPOSE

To evaluate the reliability of QVBD measurements based on cone-beam breast computed tomography (CBBCT) images.

MATERIAL AND METHODS

A total of 216 breasts were used to evaluate the stability of QVBD measurements based on CBBCT images and the correlations between this volumetric measurement and visual and area-based measurement methods. The intra- and inter-observer consistency of QVBD measurements were compared. Visual breast density (VBD) was evaluated with Breast Imaging Reporting and Data System (BI-RADS) standard on CBBCT images. The correlation between QVBD and VBD was evaluated by Spearman correlation coefficient. Receiver operating characteristic (ROC) curve was used to assess the sensitivity and specificity of the volumetric method in distinguishing dense and non-dense breasts. The correlation between QVBD and quantitative area-based breast density (QABD) was determined with Pearson correlation coefficient. Then, the breast volume measured with CBBCT images was compared with the breast specimen obtained during nipple-sparing mastectomy (NSM) by Pearson correlation coefficient and linear regression.

RESULTS

Excellent intra- and inter-observer consistency was found from QVBD measurements. The volumetric method distinguished dense and non-dense breasts at a cutoff value of 9.5%, with 94.5% sensitivity and 77.1% specificity. Positive correlations were found between QVBD and QABD (r=0.890; P<0.001) and between the volume measured with CBBCT images and Archimedes method (r=0.969; P<0.001).

CONCLUSION

CBBCT images can evaluate breast density reliably on a continuous scale.

Mode of detection matters: Differences in screen-detected versus symptomatic breast cancers

OBJECTIVE

Although extensive analyses evaluating screening mammography for breast cancer have been published, some utilized databases do not distinguish between modes of detection, which confounds the conclusions made about the impact of screening mammography.

METHODS

A retrospective cohort study of women at our institution with pathologically-proven breast cancer from January 2015 to April 2018 was conducted. Subjects were categorized by their mode of diagnosis: screening or non-screening. Patient demographics, tumor characteristics, and treatments were compared between detection methods using Wilcoxon rank-sum test for continuous variables and chi-squared or Fisher's exact test.

RESULTS

1026 breast cancers were analyzed. 80.8% of screen-detected breast cancers were invasive. Compared to symptomatically detected cancers, screen-detected were smaller (median size 8 mm vs. 15 mm, p < 0.001), less invasive (80.8% vs. 94.3), had a lower pathologic grade (29% grade 3 vs. 45.7%, p < 0.001), a lower clinical stage, and less aggressive histology (51.9% low Ki67 vs. 30.5%, and 88.2% HER2 negative vs. 76.6%, p < 0.001). Screen-detected cancers were less likely to have extramammary disease (13.2% positive lymph nodes vs. 34.0% and 0.4% distant metastases vs. 6.9%, p < 0.001). Women with screen-detected cancers were more likely to undergo conservative treatment (74.8% underwent lumpectomy vs. 59.9%, and 80.0% received no chemotherapy vs. 51.3%, p < 0.001).

CONCLUSION

In this study, while the vast majority of screen-detected cancers were invasive, they were more likely to be smaller, less aggressive, and a lower pathologic grade and clinical stage. Furthermore, women with screen-detected cancers were less likely to have extramammary disease and more likely to undergo conservative treatment.

Deep Learning-Based Artificial Intelligence for Mammography

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.

Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

Association of Interactions Between Mammographic Density Phenotypes and Established Risk Factors With Breast Cancer Risk, by Tumor Subtype and Menopausal Status

Previous studies suggest that the association between mammographic density (MD) and breast cancer risk might be modified by other breast cancer risk factors. In this study, we assessed multiplicative interactions between MD measures and established risk factors on the risk of invasive breast cancer overall and according to menopausal and estrogen receptor status. We used data on 2,137 cases and 4,346 controls from a nested case-control study within the Nurses' Health Study (1976-2004) and Nurses' Health Study II (1989-2007), whose data on percent mammographic density (PMD) and absolute area of dense tissue and nondense tissue (NDA) were available. No interaction remained statistically significant after adjusting for number of comparisons. For breast cancer overall, we observed nominally significant interactions (P < 0.05) between nulliparity and PMD/NDA, age at menarche and area of dense tissue, and body mass index and NDA. Individual nominally significant interactions across MD measures and risk factors were also observed in analyses stratified by either menopausal or estrogen receptor status. Our findings help provide further insights into potential mechanisms underlying the association between MD and breast cancer.

Breast Elasticity Imaging Techniques: Comparison of Strain Elastography and Shear-Wave Elastography in the Same Population

Our purpose was to compare the diagnostic performances of strain elastography (SE) and shear-wave elastography (SWE) in differentiating breast lesions by combining with conventional ultrasound (US). A total of 198 patients with 203 breast lesions underwent conventional US, SE and SWE examination using MyLab 90 and Aixplorer US systems. The SE parameters were SEscore, fat-to-lesion ratio, gland-to-lesion ratio, muscle-to-lesion ratio and SEmean, and the SWE parameters were Emax, Emean, Emin and Esd. Conventional US had the best diagnostic performance, with an area under the curve (AUC) of 0.896. Among all SE parameters, the AUCs of SEscore, fat-to-lesion ratio and SEmean were 0.802, 0.810 and 0.833. For SWE parameters, they were 0.845, 0.746 and 0.845, respectively, for Emax, Emean and Esd. When combined with US, the sensitivity and AUC of SWE seemed to be better than those of SE (96.55% vs. 93.10%, 0.958 vs. 0.947), but no statistically significant difference existed between them.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Evaluation of the association between mammographic density and the risk of breast cancer using Quantra software and the BI-RADS classification

To determine the association between mammographic density (MD) and the risk of breast cancer (BC) in Chinese women and to investigate the role of fertility risk factors in regulating the relationship between MD and BC.We used Quantra software and the BI-RADS classification to assess MD in 466 patients and 932 controls. Conditional matched logistic multiple regression analysis was used to determine the relationship between MD and BC, and risk was evaluated with the odds ratio (OR) and 95% confidence interval (CI).The ORs for category 4 versus category 2 were 1.95 (95% confidence interval [95% CI] (1.42∼2.66)) and 1.76 (95% CI (1.28∼2.42)) for the BI-RADS and Quantra classifications, respectively. The ORs for category 5 volumetric breast density (VBD) versus category 2 VBD and 5 fibroglandular tissue volume (FGV) versus category 2 FGV were 1.63 (95% CI (1.20∼2.23)) and 1.92 (95% CI (1.40∼2.63)), respectively. Females with category 5 VBD whose age at menarche was ≤13 years had the highest risk of BC (OR = 2.16, 95% CI (1.24∼3.79)), and females with category 5 FGV whose age at menarche was = 15 years had the lowest risk of BC (OR = 1.65, 95% CI (1.05∼2.62)). Females with categories 3-5 VBD and categories 3-5 FGV had reduced risks of BC with increasing number of births. Females with category 5 VBD had an increased risk of BC with increasing age at first childbirth (the OR increased from 1.49 to 1.95). Those with category 5 VBD had a reduced risk of BC with increasing breastfeeding duration (the OR decreased from 2.08 to 1.55). Females with category 5 FGV had a reduced risk of BC with increasing breastfeeding duration (the OR decreased from 4.12 to 1.62).Both the BI-RADS density classification and Quantra measures indicated that MD is positively associated with the risk of BC in Chinese women and that associations between MD and BC risk differ by age at menarche, parity, age at first childbirth and breastfeeding duration.

Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk

PURPOSE

To evaluate the agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist, as well as the impact on the assessment of breast cancer risk (BCR) at 5 years.

MATERIALS AND METHODS

We retrospectively included 311 consecutive women (mean age, 55.6±8.5 [SD]; range: 40-74 years) without a personal history of breast cancer who underwent routine mammography between January 1, 2019 and February 28, 2019. Mammographic breast density (MBD) was independently evaluated by a junior and a senior reader on digital mammography (DM) and synthetic mammography (SM) using BI-RADS (5th edition) and by an AI software. For each MBD, BCR at 5 years was estimated per woman by the AI software. Interobserver agreement for MBD between the two readers and the AI software were evaluated by quadratic κ coefficients. Reproducibility of BCR was assessed by intraclass correlation coefficient (ICC).

RESULTS

Agreement for MBD assessment on DM and SM was almost perfect between senior and junior radiologists (κ=0.88 [95% CI: 0.84-0.92] and κ=0.86 [95% CI: 0.82-0.90], respectively) and substantial between the senior radiologist and AI (κ=0.79; 95% CI: 0.73-0.84). There was substantial agreement between DM and SM for the senior radiologist (κ=0.79; 95% CI: 0.74-0.84). BCR evaluation at 5 years was highly reproducible between the two radiologists on DM and SM (ICC=0.98 [95% CI: 0.97-0.98] for both), between BCR evaluation based on DM and SM evaluated by the senior (ICC=0.96; 95% CI: 0.95-0.97) or junior radiologist (ICC=0.97; 95% CI: 0.96-0.98) and between the senior radiologist and AI (ICC=0.96; 95% CI: 0.95-0.97).

CONCLUSION

This preliminary study demonstrates a very good agreement for BCR evaluation based on the evaluation of MBD by a senior radiologist, junior radiologist and AI software.

Radiologic Technologist and Radiologist Knowledge Gaps about Breast Density Revealed by an Online Continuing Education Course

OBJECTIVE

We sought to identify provider knowledge gaps and their predictors, as revealed by a breast density continuing education course marketed to the radiology community.

METHODS

The course, continually available online during the study period of November 2, 2016 and December 31, 2018, includes demographics collection; a monograph on breast density, breast cancer risk, and screening; and a post-test. Four post-test questions were modified during the study period, resulting in different sample sizes pre- and postmodification. Multiple logistic regression was used to identify predictors of knowledge gaps (defined as > 25% of responses incorrect).

RESULTS

Of 1649 analyzable registrants, 1363 (82.7%) were radiologic technologists, 226 (13.7%) were physicians, and 60 (3.6%) were other nonphysicians; over 90% of physicians and over 90% of technologists/nonphysicians specialized in radiology. Sixteen of 49 physicians (32.7%) and 80/233 (34.3%) technologists/nonphysicians mistakenly thought the Gail model should be used to determine "high-risk" status for recommending MRI or genetic testing. Ninety-nine of 226 (43.8%) physicians and 682/1423 (47.9%) technologists/nonphysicians misunderstood the inverse relationship between increasing age and lifetime breast cancer risk. Fifty-two of 166 (31.3%) physicians and 549/1151 (47.7%) technologists/nonphysicians were unaware that MRI should be recommended for women with a family history of BRCA1/BRCA2 mutations. Tomosynthesis effectiveness was overestimated, with 18/60 (30.0%) physicians and 95/272 (34.9%) technologists/nonphysicians believing sensitivity nearly equaled MRI. Knowledge gaps were more common in technologists/nonphysicians.

CONCLUSIONS

Important knowledge gaps about breast density, breast cancer risk assessment, and screening exist among radiologic technologists and radiologists. Continued education efforts may improve appropriate breast cancer screening recommendations.