Screening ultrasound as an adjunct to mammography in women with mammographically dense breasts

Extremely dense breasts: A comprehensive review of increased cancer risk and supplementary screening methods

Women with extremely dense breasts account for approximately 10% of the screening population and face an increased lifetime risk of developing breast cancer. At the same time, the sensitivity of mammography, the first-line screening modality, is significantly reduced in this breast density group, owing to the masking effect of the abundant fibroglandular tissue. Consequently, this population has garnered increasing scientific attention due to the unique diagnostic challenge they present. Several research initiatives have attempted to address this diagnostic challenge by incorporating supplemental imaging modalities such as ultrasound, MRI, and contrast-enhanced mammography. Each of these modalities offers different benefits as well as limitations, both clinically and practically, including considerations of availability and costs. The purpose of this article is to critically review the background, latest scientific evidence, and future directions for the use of the various supplemental screening techniques for women with extremely dense breasts.

Developing a nomogram prediction model to enhance diagnostic accuracy of supplemental ultrasound post-negative mammography

The effectiveness of mammography in women with dense breasts is compromised by a high rate of false-negative results. While supplemental ultrasound increases sensitivity, its low positive predictive value (PPV) leads to more unnecessary biopsies. This study aims to develop a nomogram model to predict the malignancy of breast masses that are additionally identified as suspicious by supplemental ultrasound after an initial negative screening mammography. The goal is to improve the PPV of supplemental ultrasound and potentially reduce unnecessary biopsies. In this study, eligible data were collected retrospectively and then randomized into training and validation sets. The Least Absolute Shrinkage and Selection Operator was used to identify the most important predictive variables in the training set. The maximum Youden index determined the optimal model threshold, and model performance was evaluated using receiver operating characteristic curves, calibration curves, decision curve analyses, and metrics such as sensitivity, specificity, PPV, and negative predictive value. The study included 425 breast masses, 345 benign and 80 malignant. These were divided into 298 for the training set and 127 for the validation set. Least Absolute Shrinkage and Selection Operator identified the 5 most important predictive variables for the construction of the model. The model showed strong discrimination with area under the curve values of 0.91 (0.87-0.95) for the training set and 0.88 (0.81-0.96) for the validation set. Hosmer-Lemeshow tests indicated a good model fit, with P-values of 0.78 and 0.12 for the training and validation sets, respectively. In addition, decision curve analyses highlighted the clinical utility of the model. The model also showed commendable diagnostic performance in terms of sensitivity, specificity, PPV, and negative predictive value. The nomogram model significantly increased the PPV of supplemental ultrasound from 0.18 to 0.56 in the training set and from 0.21 to 0.56 in the validation set. This study successfully developed a nomogram model to predict the malignancy of suspicious breast masses additionally identified by supplemental ultrasound. The model shows robust performance and significantly improves the PPV of supplemental ultrasound, suggesting a promising way to reduce unnecessary biopsies in such cases.

Ultrasonographic and pathological correlation of asymmetric retroareolar density on mammogram

BACKGROUND

Retroareolar region refers to the region within two centimeters from the nipple and/or involves the nipple-areolar complex on mammogram. In this study, we graded asymmetric retroareolar density on mammography and determined the underlying cause.

OBJECTIVES

To identify and grade retroareolar densities and evaluate characteristics of lesion using ultrasonography and histopathology.

METHODS

Mammograms with asymmetric retroareolar density done in our tertiary care hospital were included. Retroareolar density was categorized into three grades based on morphological appearance in mammography. Sonography was performed in all patients and tissue diagnosis was obtained for suspicious lesions.

RESULTS

Of the 100 patients included in the study, most of the patients with mammographic grade 1, grade 2 and 3 retroareolar asymmetry had normal sonography, pathologically proven mastitis and invasive ductal carcinoma, respectively. Presenting indication usually was diagnostic (n = 87), lump being most common. Benign (58%) diagnosis was more often present, with equal number of normal studies and malignancies (21%). Frequently pathologically proven malignant lesions (n = 17) had grade 3 asymmetry and none were grade 1. Invasive ductal carcinoma was the most common malignancy while mastitis the most common benign disease.

CONCLUSIONS

Grade I retroareolar asymmetric density on mammography was normal or had a benign etiology while grade 2 or 3 asymmetric density had underlying pathology, often malignancy.

CONTRIBUTION

Grading retroareolar density in mammogram may improve the evaluation of retroareolar region and increase emphasis on higher grades.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway

OBJECTIVE

To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.

MATERIALS AND METHOD

We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG.

RESULTS

We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4.

CONCLUSION

The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.

CLINICAL RELEVANCE STATEMENT

Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.

KEY POINTS

• Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Radiomics in breast cancer: Current advances and future directions

Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.

The current diagnosis and treatment strategy of breast cancer based on multicentre retrospective data in Shaanxi province

BACKGROUND

Breast cancer is a common malignancy, and early detection coupled with standardized treatment is crucial for patient survival and recovery. This study aims to scrutinize the current state of breast cancer diagnosis and treatment in Shaanxi province, providing valuable insights into the local practices and outcomes.

METHODS

We selected 25 hospitals that typically represent the current diagnosis and treatment strategy of breast cancer in Shaanxi (a province in northwest China). The questionnaire comprised sections on fundamental information, outpatient consultations, breast-conserving surgery, neoadjuvant and adjuvant therapy, sentinel lymph node biopsy, breast reconstruction surgery.

RESULTS

A total of 6665 breast cancer operations were performed in these 25 hospitals in 2021. The overall proportion of breast-conserving surgery (BCS) is 23.6%. There was a statistically significant positive correlation between the annual volume of breast cancer surgery and the implementation rate of BCS (P = 0.004). A total of 2882 cases of neoadjuvant treatment accounted for 43.24% of breast cancer patients treated with surgery in 2017. Hospitals in Xi'an performed more neoadjuvant therapy for patients with breast cancer compared to other districts (P = 0.008). There was a significantly positive correlation between outpatient visits and the implementation rate of sentinel lymph node biopsy (SLNB) (P = 0.005). 14 hospitals in Shaanxi performed reconstructive surgery.

CONCLUSIONS

Breast conserving surgery, adjuvant and neoadjuvant therapy and sentinel lymph node biopsy in Shaanxi province have reached the China's average level. Moreover, hospitals in Xi 'an have surpassed this average. However, a disparity is observed in the development of breast reconstruction surgery when compared to top-tier hospitals.

Comparison of diagnostic efficacy of 2D and 3D ultrasound in women under the age of mammography screening

Breast cancer is the most common cancer among women worldwide. Younger women are more likely than older women to have aggressive molecular subtypes and late-stage disease. Mammography has less sensitivity in detecting breast cancer in women with a dense breast, and 2D ultrasound (2D US) has limitations, such as the specialists high level of skill and experience and the time it takes to perform the examination. Nowadays, there is a new technique – automated volumetric ultrasound scanning of the breast (3D US), which allows you to obtain high-resolution images.

Aim. To perform a comparative analysis of the diagnostic efficacy of 2D US and 3D US among women under 40 years of age with high breast tissue density.

Methods. A retro-prospective clinical single-center study. From February 2019 to May 2023, 1511 patients under the age of 40 were examined. The patients were divided into two groups. Patients in group A underwent 2D ultrasound, the results of the study were evaluated according to the BI-RADS classification. In addition to 2D ultrasound, the patients who were placed in group B underwent 3D US also with the BI-RADS category. Based on the results of the study, the positive and negative predictive value, sensitivity, specificity and accuracy, as well as the compilation of a predictive model of the method were determined.

Results. The 2D US in group A showed sensitivity of 0.8, specificity 1, balanced accuracy of 0.9, and area under the predictive model curve of 0.947, US in group B 0.89, 0.98, 0.94, and 0.903, respectively, and US of the entire sample of 0.87, 0.99, 0.93, and 0.916, respectively. The 3D US in group B showed a sensitivity of 0.95, specificity of 0.99 and a balanced accuracy of 0.97 and an area under the predictive model curve of 0.968.

Conclusion. The diagnostic efficiency of 3D US of the mammary glands in patients under 40 years of age is comparable in terms of specificity and is better in terms of accuracy, sensitivity and a better prognostic model of the method compared to US examination in 2D mode. The 3D US method has advantages in comparison with 2D US examination, namely reproducibility, operator independence of the method, reduced examination time, obtaining visualization of the entire organ, improved visualization in multicentric and multifocal processes, the possibility of operational planning, the possibility of “double reading” of the results.

Factors Associated with False Positive Breast Cancer Results in the Real-Time Sonoelastography Evaluation of Solid Breast Lesions

Background and Objectives: Breast cancer is one of the most widespread cancers among the female population around the world and is curable if diagnosed in an early stage. Consequently, breast cancer screening imaging techniques have greatly evolved and adjusted over the last decades. Alongside mammography, sonoelastography became an important tool for breast cancer detection. However, sonoelastography still has its limitations, namely, there is still a high occurrence of false positive results in the BIRADS 4 category. The aim of our study is to identify potential false positive predictors and to ascertain the factors influencing the quality of strain ultrasound elastography for the evaluation of suspicious solid breast lesions categorized as BIRADS 4B, 4C, and 5. Materials and Methods: We conducted a retrospective study in a single private medical center in Timisoara between January 2017 and January 2022 analyzing 1625 solid breast lesions by the sonoelastography strain using a standardized BIRADS-US lexicon. Results: Our study showed that most sonoelastography factors linked to incorrect and overdiagnosis were due to a nodule dimension (OR = 1.02 per unit increase), posterior acoustic shadowing (OR = 12.26), reactive adenopathy (OR = 6.35), and an increased TES score (TES3 OR = 6.60; TES4 OR = 23.02; TES5 OR = 108.24). Regarding patient characteristics, age (OR = 1.09 per unit increase), BMI, (OR = 1.09 per unit increase), and breastfeeding history (OR = 3.00) were observed to increase the likelihood of false positive results. On the other hand, the nodules less likely to be part of the false positive group exhibited the following characteristics: a regular shape (OR = 0.27), homogenous consistency (OR = 0.42), and avascularity (OR = 0.22). Conclusions: Older age, high BMI, patients with a breastfeeding history, and those who exhibit the following specific nodule characteristics were most often linked to false positive results: large tumors with posterior acoustic shadowing and high elasticity scores, accompanied by reactive adenopathy. On the other hand, homogenous, avascular nodules with regular shapes were less likely to be misdiagnosed.

ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Knowledge, attitude, and practice toward ultrasound screening for breast cancer among women

Background

Several obstacles can hinder breast cancer screening. This study aimed to investigate the knowledge, attitude, and practice (KAP) toward ultrasound screening for breast cancer in women.

Methods

This cross-sectional study recruited women who visited the breast specialist clinic of Zhongshan City People's Hospital (a tertiary hospital) between August 2022 and April 2023 through convenience sampling. KAP scores ≥70% were considered adequate.

Results

This study enrolled 501 participants. The mean knowledge, attitude, and practice levels were 8.56 ± 1.81/12 (possible range 0-12, 71.33%), 29.80 ± 2.71 (possible range 8-40, 74.50%), and 32.04 ± 3.09 (possible range 8-40, 80.10%). Senior high school education (vs. junior high school and below, coefficient = 1.531, 95%CI: 1.013-2.312, p = 0.044), bachelor's education and above (vs. junior high school and below, coefficient = 5.315, 95%CI: 3.546-7.966, p < 0.001), housewife or unemployed (vs. employed, coefficient = 0.671, 95%CI: 0.466-0.966, p = 0.032), and a history of breast ultrasound (vs. no, coefficient = 1.466, 95%CI: 1.121-1.917, p = 0.005) were independently and positively associated with knowledge. Knowledge (coefficient = 1.303, 95%CI: 1.100-1.544, p = 0.002) and monthly income >10,000 (vs. <5,000, coefficient = 4.364, 95%CI: 1.738-10.956, p = 0.002) were independently and positively associated with attitude. Only attitude (coefficient = 1.212, 95%CI: 1.096-1.340, p < 0.001) was independently and positively associated with the practice. A structural equation modeling (SEM) analysis was used to estimate causality among KAP dimensions, showing that knowledge directly influenced attitude (β = -1.090, p = 0.015), knowledge did not directly influence practice (β = -0.117, p = 0.681) but had an indirect influence (β = 0.826, p = 0.028), and attitude directly influenced practice (β = -0.757, p = 0.016).

Conclusion

Women in Zhongshan City had good knowledge, favorable attitudes, and active practice toward breast ultrasound screening for breast cancer. Women's characteristics associated with a poorer KAP were identified, allowing for more targeted interventions.

Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence

Background

Mammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT).

Methods

We used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles.

Results

The average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40-49, 50-59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40-49, 50-59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40-59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36.

Conclusion

The study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40-59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.

Supplemental Screening as an Adjunct to Mammography for Breast Cancer Screening in People With Dense Breasts: A Health Technology Assessment

Background

Screening with mammography aims to detect breast cancer before clinical symptoms appear. Among people with dense breasts, some cancers may be missed using mammography alone. The addition of supplemental imaging as an adjunct to screening mammography has been suggested to detect breast cancers missed on mammography, potentially reducing the number of deaths associated with the disease. We conducted a health technology assessment of supplemental screening with contrast-enhanced mammography, ultrasound, digital breast tomosynthesis (DBT), or magnetic resonance imaging (MRI) as an adjunct to mammography for people with dense breasts, which included an evaluation of effectiveness, harms, cost-effectiveness, the budget impact of publicly funding supplemental screening, the preferences and values of patients and health care providers, and ethical issues.

Methods

We performed a systematic literature search of the clinical evidence published from January 2015 to October 2021. We assessed the risk of bias of each included study using the Cochrane Risk of Bias or RoBANS tools, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature review and conducted cost-effectiveness analyses with a lifetime horizon from a public payer perspective. We also analyzed the budget impact of publicly funding supplemental screening as an adjunct to mammography for people with dense breasts in Ontario. To contextualize the potential value of supplemental screening for dense breasts, we spoke with people with dense breasts who had undergone supplemental screening; performed a rapid review of the qualitative literature; and conducted an ethical analysis of supplemental screening as an adjunct to mammography.

Results

We included eight primary studies in the clinical evidence review. No studies evaluated contrast-enhanced mammography. Nonrandomized and randomized evidence (GRADE: Very low to Moderate) suggests that mammography plus ultrasound was more sensitive and less specific, and detected more cancers compared to mammography alone. Fewer interval cancers occurred after mammography plus ultrasound (GRADE: Very low to Low), but recall rates were nearly double that of mammography alone (GRADE: Very low to Moderate). Evidence of Low to Very low quality suggested that compared with supplemental DBT, supplemental ultrasound was more sensitive, detected more cancers, and led to more recalls. Among people with extremely dense breasts, fewer interval cancers occurred after mammography plus supplemental MRI compared to mammography alone (GRADE: High). Supplemental MRI after negative mammography was highly accurate in people with extremely dense breasts and heterogeneously dense breasts in nonrandomized and randomized studies (GRADE: Very Low and Moderate). In people with extremely dense breasts, MRI after negative mammography detected 16.5 cancers per 1,000 screens (GRADE: Moderate), and up to 9.5% of all people screened were recalled (GRADE: Moderate). Contrast-related adverse events were infrequent (GRADE: Moderate). No study reported psychological impacts, breast cancer-specific mortality, or overall mortality.We included nine studies in the economic evidence, but none of the study findings was directly applicable to the Ontario context. Our lifetime cost-effectiveness analyses showed that supplemental screening with ultrasound, MRI, or DBT found more screen-detected cancers, decreased the number of interval cancers, had small gains in life-years or quality-adjusted life-years (QALYs), and was associated with savings in cancer management costs. However, supplemental screening also increased imaging costs and the number of false-positive cases. Compared to mammography alone, the incremental cost-effectiveness ratios (ICERs) for supplemental screening with handheld ultrasound, MRI, or DBT for people with dense breasts were $119,943, $314,170, and $212,707 per QALY gained, respectively. The ICERs for people with extremely dense breasts were $83,529, $101,813, and $142,730 per QALY gained, respectively. In sensitivity analyses, the diagnostic test sensitivity of mammography alone and of mammography plus supplemental screening had the greatest effect on ICER estimates. The total budget impact of publicly funding supplemental screening with handheld ultrasound, MRI, or DBT for people with dense breasts over the next 5 years is estimated at $15 million, $41 million, or $33 million, respectively. The corresponding total budget impact for people with extremely dense breasts is $4 million, $10 million, or $9 million.We engaged directly with 70 people via interviews and an online survey. The participants provided diverse perspectives on broad access to supplemental screening for people with dense breasts in Ontario. Themes discussed in the interviews included self-advocacy, patient-doctor partnership, preventive care, and a shared preference for broad access to screening modalities that are clinically effective in detecting breast cancer in people with dense breasts.We included 10 studies in the qualitative evidence rapid review. Thematic synthesis of these reports yielded three analytical themes: coming to know and understand breast density, which included introductions to and making sense of breast density; experiences of vulnerability, which influenced or were influenced by understandings and misunderstandings of breast density and responses to breast density; and choosing supplemental screening, which was influenced by knowledge and perception of the risks and benefits of supplemental screening, and the availability of resources.The ethics review determined that the main harms of supplemental screening for people with dense breasts are false-positives and overdiagnosis, both of which lead to unnecessary and burdensome health care treatments. Screening programs raise inherent tensions between individual- and population-level interests; they may yield population-level benefit, but are statistically of very little benefit to individuals. Entrenched cultural beliefs about the value of breast cancer screening, combined with uncertainty about the effects of supplemental screening on some outcomes and the discomfort of many health care providers in discussing screening options for people with dense breasts suggest that it may be difficult to ensure that patients can provide informed consent to engage in supplemental screening. Funding supplemental screening for people with dense breasts may lead to improved equity in the effectiveness of identifying cancers in people with dense breasts (compared to mammography alone), but it is not clear whether it would lead to equity in terms of improved survival and decreased morbidity.

Conclusions

Supplemental screening with ultrasound, DBT, or MRI as an adjunct to mammography detected more cancers and increased the number of recalls and biopsies, including false-positive results. Fewer interval cancers tended to occur after supplemental screening compared to mammography alone. It is unclear whether supplemental screening as an adjunct to mammography would reduce breast cancer-related or overall mortality among people with dense breasts.Supplemental screening with ultrasound, DBT, or MRI as an adjunct to mammography in people aged 50 to 74 years improved cancer detection but increased costs. Depending on the type of imaging modality, publicly funding supplemental screening in Ontario over the next 5 years would require additional total costs between $15 million and $41 million for people with dense breasts, and between $4 million and $10 million for people with extremely dense breasts.The people we engaged with directly valued the potential clinical benefits of supplemental screening and emphasized that patient education and equitable access should be a requirement for implementation in Ontario. Our review of the qualitative literature found that the concept of breast density is poorly understood, both by people with dense breasts and by some general practitioners. People with dense breasts who receive routine mammography (especially those who receive health care in their nonpreferred language or are perceived to have lower economic status or health literacy) and their general practitioners may not have the awareness or knowledge to make informed decisions about supplemental screening. Some people with dense breasts experienced emotional distress from barriers to accessing supplemental screening, and many wanted to engage in supplemental screening, even when educated about its potential harms, including false-positives and overdiagnosis.Given an overall lack of robust evidence about morbidity and mortality associated with supplemental screening for people with dense breasts, it is not possible to determine whether funding supplemental screening for dense breasts delivers on the ethical duties to maximize benefits and minimize harms for populations and individuals. It is likely that existing inequities in access to breast screening and cancer treatment will persist, even if supplemental screening for dense breasts is funded. Continued efforts to address these inequities by removing barriers to screening might mitigate this concern. It will be important to identify and minimize sources of uncertainty related to benefits and risks of supplemental screening for dense breasts to optimize the capacity for everyone involved to live up to their ethical obligations. Some of these may be resolved with further evidence related to the outcomes of supplemental screening for dense breasts.

Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images

Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the 'starting point set' of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer (only the healthy side) and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as 'wavelet-H_glcm_Correlation' and 'wavelet-H_firstorder_Maximum', showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was ≥0.95. Radiomics features from mammography images can help predict breast cancer risk.

Make It Less Complex: Autoencoder for Speckle Noise Removal-Application to Breast and Lung Ultrasound

Ultrasound (US) imaging is used in the diagnosis and monitoring of COVID-19 and breast cancer. The presence of Speckle Noise (SN) is a downside to its usage since it decreases lesion conspicuity. Filters can be used to remove SN, but they involve time-consuming computation and parameter tuning. Several researchers have been developing complex Deep Learning (DL) models (150,000-500,000 parameters) for the removal of simulated added SN, without focusing on the real-world application of removing naturally occurring SN from original US images. Here, a simpler (<30,000 parameters) Convolutional Neural Network Autoencoder (CNN-AE) to remove SN from US images of the breast and lung is proposed. In order to do so, simulated SN was added to such US images, considering four different noise levels (σ = 0.05, 0.1, 0.2, 0.5). The original US images (N = 1227, breast + lung) were given as targets, while the noised US images served as the input. The Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to compare the output of the CNN-AE and of the Median and Lee filters with the original US images. The CNN-AE outperformed the use of these classic filters for every noise level. To see how well the model removed naturally occurring SN from the original US images and to test its real-world applicability, a CNN model that differentiates malignant from benign breast lesions was developed. Several inputs were used to train the model (original, CNN-AE denoised, filter denoised, and noised US images). The use of the original US images resulted in the highest Matthews Correlation Coefficient (MCC) and accuracy values, while for sensitivity and negative predicted values, the CNN-AE-denoised US images (for higher σ values) achieved the best results. Our results demonstrate that the application of a simpler DL model for SN removal results in fewer misclassifications of malignant breast lesions in comparison to the use of original US images and the application of the Median filter. This shows that the use of a less-complex model and the focus on clinical practice applicability are relevant and should be considered in future studies.

Breast Cancer Screening and Diagnosis: Recent Advances in Imaging and Current Limitations

Breast cancer detection has a significant impact on population health. Although there are many breast imaging modalities, mammography is the predominant tool for breast cancer screening. The introduction of digital breast tomosynthesis to mammography has contributed to increased cancer detection rates and decreased recall rates. In average-risk women, starting annual screening mammography at age 40 years has demonstrated the highest mortality reduction. In intermediate- and high-risk women as well as in those with dense breasts, additional modalities, including MRI, ultrasound, and molecular breast imaging, can also be considered for adjunct screening to improve the detection of mammographically occult malignancy.

Enhanced Detection of Suspicious Breast Lesions: A Comparative Study of Full-Field Digital Mammography and Automated Breast Ultrasound in 117 Patients with Core Needle Biopsy

BACKGROUND This retrospective study from a single center aimed to compare the performance of full-field digital mammography (FFDM) vs automated breast ultrasound (ABUS) in the identification and characterization of suspicious breast lesions in 117 patients who underwent core-needle biopsy (CNB) of the breast. MATERIAL AND METHODS The study involved a group of 301 women. Every patient underwent FFDM followed by ABUS, which were assessed in concordance with BI-RADS (Breast Imaging Reporting and Data System) classification. RESULTS No focal lesions were found in 168 patients. In 133 patients, 117 histopathologically verified focal lesions were found. Among them, 78% appeared to be malignant and 22% benign. ABUS detected 246 focal lesions, including 115 classified as BI-RADS 4 or 5 and submitted to verification, while FFDM revealed 122 lesions, including 75 submitted to verification. The analysis revealed that combined application of both methods caused sensitivity to increase to 100, and improved accuracy improvement. Margin assessments in these examinations are consistent (P<0.00), the lesion's margin type with both methods depends on its malignant or benign character (P<0.03), lesion margins distribution on ABUS depends on estrogen receptor presence (P=0.033), and there was significant correlation between malignant character of the lesion and retraction phenomenon sign (P=0.033). ABUS obtained higher compliance between the size of the lesion in histopathology compared to FFDM (P>0.05). CONCLUSIONS The results shows that ABUS is comparable to FFDM, and even outperforms it in a few of the analyzed categories, suggesting that the combination of these 2 methods may have an important role in breast cancer detection.

Integration of Handheld Ultrasound or Automated Breast Ultrasound among Women with Negative Mammographic Screening Findings: A Multi-center Population-based Study in China

RATIONALE AND OBJECTIVES

This study assessed the role of second-look automated breast ultrasound (ABUS) adjunct to mammography (MAM) versus MAM alone in asymptomatic women and compared it with supplementing handheld ultrasound (HHUS).

MATERIALS AND METHODS

Women aged 45 to 64 underwent HHUS, ABUS, and MAM among six hospitals in China from 2018 to 2022. We compared the screening performance of three strategies (MAM alone, MAM plus HHUS, and MAM plus ABUS) stratified by age groups and breast density. McNemar's test was used to assess differences in the cancer detection rate (CDR), the false-positive biopsy rate, sensitivity, and specificity of different strategies.

RESULTS

Of 19,171 women analyzed (mean [SD] age, 51.54 [4.61] years), 72 cases of breast cancer (3.76 per 1000) were detected. The detection rates for both HHUS and ABUS combined with MAM were statistically higher than those for MAM alone (all p < 0.001). There was no significant difference in cancer yields between the two integration strategies. The increase in CRD of the integrated strategies was higher in women aged 45-54 years with denser breasts compared with MAM alone (all p < 0.0167). In addition, the false-positive biopsy rate of MAM plus ABUS was lower than that of MAM plus HHUS (p = 0.025). Moreover, the retraction in ABUS was more frequent in cases detected among MAM-negative results.

CONCLUSION

Integrated ABUS or HHUS into MAM provided similar CDRs that were significantly higher than those for MAM alone in younger women (45-54 years) with denser breasts. ABUS has the potential to avoid unnecessary biopsies and provides specific image features to distinguish malignant tumors from HHUS.

Practice, confidence and continuity of breast self-examination among women in Thailand during COVID-19 pandemic: a cross-sectional study

OBJECTIVE

Breast self-examination (BSE) is the most feasible screening tool compared with clinical breast examination and mammography. It is crucial to address the associated factors of practising BSE to develop a targeted BSE promotion programme and improve the BSE quality in Thai women, particularly during the COVID-19 pandemic.

DESIGN AND SETTING

We conducted a cross-sectional study in Thailand's north and northeast region from March 2020 to November 2022.

PARTICIPANTS

This study involved 405 women aged 30-70 years old.

VARIABLES AND OUTCOMES

Demographic information, health status and BSE were collected using a modified questionnaire based on the Champion Health Belief Model. The outcomes were ever-practising BSE, BSE practice within the last 6 months, continuity of BSE and confidence in doing BSE. Logistic regression and decision tree analysis identified the associated factors.

RESULTS

75.55% of participants ever performed BSE. Around 74.18% did BSE within the last 6 months. Diploma graduates (adjusted OR (aOR) 25.48, 95% CI 2.04 to 318.07), 21-40 reproductive years (aOR 4.29, 95% CI 1.22 to 15.08), ever pregnant (aOR 3.31, 95% CI 1.05 to 10.49), not drinking alcohol (aOR 2.1, 95% CI 1.04 to 4.55), not receiving hormone replacement (aOR 5.51, 95% CI 2.04 to 14.89), higher knowledge (aOR 1.29, 95% CI 1.09 to 1.52), attitude (aOR 1.15, 95% CI 1.05 to 1.26) and practice/cues of action towards BSE were associated with ever-practising BSE. Frequent high-fat diet, high awareness of breast cancer, lower knowledge of BSE and lower attitude toward BSE were associated with not practising BSE within 6 months and BSE discontinuation. Only high knowledge of BSE was associated with absolute confidence in BSE (p<0.05).

CONCLUSION

Despite having a higher percentage than other studies in different countries prior to the pandemic, it is still crucial to improve knowledge of BSE to encourage BSE practice, confidence and continuity of BSE in Thai women. Moreover, the BSE campaign should target women with prolonged exposure to oestrogen and sedentary lifestyle.

Diagnostic Performance of Contrast-Enhanced Digital Mammography versus Conventional Imaging in Women with Dense Breasts

The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast composition category ACR BI-RADS c-d at DM and an abnormal finding (BI-RADS 3-4-5) at DM and/or BUS were considered. During CEM, a nonionic iodinated contrast agent (Iohexol 350 mg I/mL, 1.5 mL/kg) was power-injected intravenously. Images were evaluated independently by two breast radiologists. Findings classified as BI-RADS 1-3 were considered benign, while BI-RADS 4-5 were considered malignant. In case of discrepancies, the higher category was considered for DM+BUS. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, using histology/≥12-month follow-up as gold standards. In total, 51 patients with 65 breast lesions were included. 59 (90.7%) abnormal findings were detected at DM+BUS, and 65 (100%) at CEM. The inter-reader agreement was excellent (Cohen's k = 0.87 for DM+BUS and 0.97 for CEM). CEM showed a 93.5% sensitivity (vs. 90.3% for DM+BUS), a 79.4-82.4% specificity (vs. 32.4-35.5% for DM+BUS) (McNemar p = 0.006), a 80.6-82.9% PPV (vs. 54.9-56.0% for DM+BUS), a 93.1-93.3% NPV (vs. 78.6-80.0% for DM+BUS), and a 86.1-87.7% accuracy (vs. 60.0-61.5% for DM+BUS). The AUC was higher for CEM than for DM+BUS (0.865 vs. 0.613 for Reader 1, and 0.880 vs. 0.628, for Reader 2) (p < 0.001). In conclusion, CEM had a better diagnostic performance than DM and BUS alone and combined together in patients with dense breasts.

Laparoscopic transversus abdominis plane block is useful in pain relief after laparoscopic stapled repair of diastasis recti and ventral hernia

Background

There is still no consensus on perioperative pain control techniques in patients undergoing laparoscopic surgery; protocols of conventional therapy can be improved by the use of perioperative anaesthesiologic techniques, such as epidural or loco-regional analgesic administration as transversus abdominis plane (TAP) block. The aim of this evaluation was to investigate the role of laparoscopic-assisted TAP block during repair of diastasis recti associated with primary midline hernias in term of post-operative pain relief.

Materials

and

Methods

This was a retrospective evaluation of a prospectively maintained database including patients undergoing laparoscopic repair of diastasis recti associated with primary ventral hernia. Patients were divided into two groups: Group A patients (n = 34) received laparoscopic-assisted bilateral TAP-block of 7.5 mg/ml ropivacaine for each side and Group B patients (n = 29) received conventional post-operative therapy. All patients received 24 h infusion of 20 mg morphine; pain was checked at 6, 24 and 48 h after surgery by numeric rating scale (NRS) score. A rescue analgesia by was given if NRS score was >4 or on patient request.

Results

No differences in operative time, complications and post-operative stay, no complications related to TAP-block technique were found. Post-operative pain scores (determined by NRS) were found to be significantly different between groups. Group A patients showed a significant reduction in NRS score at 6, 24 and 48 h (P < 0.005) and in the number of patients requiring further analgesic drugs administration (P < 0.005) compared to Group B patients.

Conclusions

Laparoscopic-guided TAP-block can be considered safe and effective in the management of post-operative pain and in the reduction of analgesic need in patients undergoing laparoscopic repair of diastasis recti and ventral hernias. The non-randomised nature of the study and the lack of a consistent series of patients require further evaluations.

Current status of Cancer Nanotheranostics: Emerging strategies for cancer management

Cancer diagnosis and management have been a slow-evolving area in medical science. Conventional therapies have by far proved to have various limitations. Also, the concept of immunotherapy which was thought to revolutionize the management of cancer has presented its range of drawbacks. To overcome these limitations nanoparticulate-derived diagnostic and therapeutic strategies are emerging. These nanomaterials are to be explored as they serve as a prospect for cancer theranostics. Nanoparticles have a significant yet unclear role in screening as well as therapy of cancer. However, nanogels and Photodynamic therapy is one such approach to be developed in cancer theranostics. Photoactive cancer theranostics is a vivid area that might prove to help manage cancer. Also, the utilization of the quantum dots as a diagnostic tool and to selectively kill cancer cells, especially in CNS tumors. Additionally, the redox-sensitive micelles targeting the tumor microenvironment of the cancer are also an important theranostic tool. This review focuses on exploring various agents that are currently being studied or can further be studied as cancer theranostics.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Imaging Surveillance Options for Individuals With a Personal History of Breast Cancer: AJR Expert Panel Narrative Review

Annual surveillance mammography is recommended for breast cancer survivors on the basis of observational studies and meta-analyses showing reduced breast cancer mortality and improved quality of life. However, breast cancer survivors are at higher risk of subsequent breast cancer and have a fourfold increased risk of interval breast cancers compared with individuals without a personal history of breast cancer. Supplemental surveillance modalities offer increased cancer detection compared with mammography alone, but utilization is variable, and benefits must be balanced with possible harms of false-positive findings. In this review, we describe the current state of mammographic surveillance, summarize evidence for supplemental surveillance in breast cancer survivors, and explore a risk-based approach to selecting surveillance imaging strategies. Further research identifying predictors associated with increased risk of interval second breast cancers and development of validated risk prediction tools may help physicians and patients weigh the benefits and harms of surveillance breast imaging and decide on a personalized approach to surveillance for improved breast cancer outcomes.

MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions

Magnetic Resonance Imaging (MRI) is the most sensitive and advanced imaging technique in diagnosing breast cancer and is essential in improving cancer detection, lesion characterization, and determining therapy response. In addition to the dynamic contrast-enhanced (DCE) technique, functional techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) further characterize and differentiate benign and malignant lesions thus, improving diagnostic accuracy. There is now an increasing clinical usage of MRI breast, including screening in high risk and supplementary screening tools in average-risk patients. MRI is becoming imperative in assisting breast surgeons in planning breast-conserving surgery for preoperative local staging and evaluation of neoadjuvant chemotherapy response. Other clinical applications for MRI breast include occult breast cancer detection, investigation of nipple discharge, and breast implant assessment. There is now an abundance of research publications on MRI Breast with several areas that still remain to be explored. This review gives a comprehensive overview of the clinical trends of MRI breast with emphasis on imaging features and interpretation using conventional and advanced techniques. In addition, future research areas in MRI breast include developing techniques to make MRI more accessible and costeffective for screening. The abbreviated MRI breast procedure and an area of focused research in the enhancement of radiologists' work with artificial intelligence have high impact for the future in MRI Breast.

Comparison of Characteristics of Breast Cancer Detected through Different Imaging Modalities in a Large Cohort of Hong Kong Chinese Women: Implication of Imaging Choice on Upcoming Local Screening Program

Background

We compared the clinico-radio-pathological characteristics of breast cancer detected through mammogram (MMG) and ultrasound (USG) and discuss the implication of the choice of imaging as the future direction of our recently launched local screening program.

Methods

Retrospective study of 14613 Hong Kong Chinese female patients with histologically confirmed breast cancer registered in the Hong Kong Breast Cancer Registry between January 2006 and February 2020. Patients were classified into four groups based on the mode of breast cancer detection (detectable by both mammogram and ultrasound (MMG+/USG+), mammogram only (MMG+/USG-), ultrasound only (MMG-/USG+), or not detectable by either (MMG-/USG-). Characteristics of breast cancer detected were compared, including patient demographics, breast density on MMG, mode of presentation, tumour size, histological type, and staging. Types of mammographic abnormalities were also evaluated for MMG+ subgroups.

Results

85% of the cancers were detectable by MMG, while USG detected an additional 9%. MMG+/USG+ cancers were larger, more advanced in stage, often of symptomatic presentation, and commonly manifested as mammographic mass. MMG+/USG- cancers were more likely of asymptomatic presentation, manifested as microcalcifications, and of earlier stage and to be ductal carcinoma in situ. MMG-/USG+ cancers were more likely seen in young patients and those with denser breasts and more likely of symptomatic presentation. MMG-/USG- cancers were often smaller and found in denser breasts.

Conclusion

Mammogram has a good detection rate of cancers in our local population. It has superiority in detecting early cancers by detecting microcalcifications. Our current study agrees that ultrasound is one of the key adjunct tools of breast cancer detection.

An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.

Comparison of the background echotexture between automated breast ultrasound and handheld breast ultrasound

This study aimed to compare the background echotexture (BE) between automated breast ultrasound (ABUS) and handheld breast ultrasound (HHUS) and evaluate the correlation of BE with mammographic (MG) density and background parenchymal enhancement (BPE) on magnetic resonance imaging (MRI). A total of 212 women with newly diagnosed breast cancer who had undergone preoperative ABUS, HHUS, MG, and MRI were included. Two breast radiologists blinded to the menopausal status analyzed the BE of the contralateral breasts of the patients with breast cancer in consensus. The MG density and BPE of breast MRI on the radiologic reports were compared with the BE in the ultrasound. We used the cumulative link mixed model to compare the BE and Spearman rank correlation to evaluate the association between BE with MG density and BPE. BE was more heterogeneous in ABUS than in HHUS (P < .001) and in the premenopausal group than in the postmenopausal group (P < .001). The heterogeneity of BE in the premenopausal group was higher with ABUS than with HHUS (P = .013). BE and MG density showed a moderate correlation in the postmenopausal group, but a weak correlation in the premenopausal group. BE and BPE showed moderate correlations only in the premenopausal group. ABUS showed a more heterogeneous BE, especially in the premenopausal group. Therefore, more attention is required to interpret ABUS screening in premenopausal women.

Comparative Study of Digital Breast Tomosynthesis (DBT) with and without Ultrasound versus Breast Magnetic Resonance Imaging (MRI) in Detecting Breast Lesion

Digital breast tomosynthesis (DBT) is a fairly recent breast imaging technique invented to overcome the challenges of overlapping breast tissue. Ultrasonography (USG) was used as a complementary tool to DBT for the purpose of this study. Nonetheless, breast magnetic resonance imaging (MRI) remains the most sensitive tool to detect breast lesion. The purpose of this study was to evaluate diagnostic performance of DBT, with and without USG, versus breast MRI in correlation to histopathological examination (HPE). This was a retrospective study in a university hospital over a duration of 24 months. Findings were acquired from a formal report and were correlated with HPE. The sensitivity of DBT with or without USG was lower than MRI. However, the accuracy, specificity and PPV were raised with the aid of USG to equivalent or better than MRI. These three modalities showed statistically significant in correlation with HPE (p < 0.005, chi-squared). Generally, DBT alone has lower sensitivity as compared to MRI. However, it is reassuring that DBT + USG could significantly improve diagnostic performance to that comparable to MRI. In conclusion, results of this study are vital to centers which do not have MRI, as complementary ultrasound can accentuate diagnostic performance of DBT.

Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists

PURPOSE

To evaluate if a computer-aided diagnosis (CAD) system on ultrasound (US) can improve the diagnostic performance of inexperienced radiologists.

METHODS

We collected ultrasound images of 256 breast lesions taken between March and May 2020. We asked two experienced and two inexperienced radiologists to retrospectively review the US features of each breast lesion according to the Breast Imaging Reporting and Data System (BI-RADS) categories. A CAD examination with S-Detect™ software (Samsung Healthcare, Seoul, South Korea) was conducted retrospectively by another uninvolved radiologist blinded to the BIRADS values previously attributed to the lesions. Diagnostic performances of experienced and inexperienced radiologists and CAD were compared and the inter-observer agreement among radiologists was calculated.

RESULTS

The diagnostic performance of the experienced group in terms of sensitivity was significantly higher than CAD (p < 0.001). Conversely, the diagnostic performance of inexperienced group in terms of both sensitivity and specificity was significantly lower than CAD (p < 0.001). We obtained an excellent agreement in the evaluation of the lesions among the two expert radiologists (Kappa coefficient: 88.7%), and among the two non-expert radiologists (Kappa coefficient: 84.9%).

CONCLUSION

The US CAD system is a useful additional tool to improve the diagnostic performance of the inexperienced radiologists, eventually reducing the number of unnecessary biopsies. Moreover, it is a valid second opinion in case of experienced radiologists.

Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.

The Effect of Supplemental Imaging on Interval Cancer Rates in Mammography Screening: Systematic Review

Supplemental screening with MRI or ultrasound increases cancer detection rate (CDR) in women with standard screening mammography. Whether it also reduces interval cancer rate (ICR) is unclear. This study reviewed the evidence evaluating the effect of supplemental imaging on ICR in women undergoing screening mammography. This systematic review included studies that reported both CDR and ICR in women undergoing screening mammography alone compared to those undergoing screening mammography with supplemental imaging. Five studies (3 randomized trials) were eligible. These reported on 142,153 women undergoing mammography screening alone or mammography with supplemental imaging (3 ultrasound and 2 MRI studies). Two studies included a general screening population and 3 included special populations (young, high genetic risk and/or dense breasts). The incremental CDR for supplemental MRI was 14.2 to 16.5/1000 screens and for ultrasound was 0 to 4.4/1000 screens. Effect on ICR was variable but evidence of a reduced ICR was more consistent for studies using supplemental MRI (ICR 0.3 to 0.8 per 1000 screens) than those using ultrasound (ICR 0.49 to 1.9 per 1000 screens). The higher CDR and lower ICR with supplemental screening were associated with higher recall and biopsy rates particularly with supplemental MRI (9.5%-15.9%, up to 69/1000 screens). Cancers detected with supplemental imaging modalities were generally smaller and earlier stage. Mammography with supplemental MRI or ultrasound increases detection of cancers (versus mammography only) in some sub-groups but also increases recall and biopsy rates and may have a relatively modest effect in reducing ICR.

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.

Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates

In our study, we added a three-dimensional automated breast ultrasound (3D ABUS) to mammography to evaluate the performance and cancer detection rate of mammography alone or with the addition of 3D prone ABUS in women with dense breasts. Our prospective observational study was based on the screening of 1165 asymptomatic women with dense breasts who selected independent of risk factors. The results evaluated include the cancers detected between June 2017 and February 2019, and all surveys were subjected to a double reading. Mammography detected four cancers, while mammography combined with a prone Sofia system (3D ABUS) doubled the detection rate, with eight instances of cancer being found. The diagnostic yield difference was 3.4 per 1000. Mammography alone was subjected to a recall rate of 14.5 for 1000 women, while mammography combined with 3D prone ABUS resulted in a recall rate of 26.6 per 1000 women. We also observed an additional 12.1 recalls per 1000 women screened. Integrating full-field digital mammography (FFDM) with 3D prone ABUS in women with high breast density increases and improves breast cancer detection rates in a significant manner, including small and invasive cancers, and it has a tolerable impact on recall rate. Moreover, 3D prone ABUS performance results are comparable with the performance results of the supine 3D ABUS system.

Implementation of Abbreviated Breast MRI for Screening: AJR Expert Panel Narrative Review

Abbreviated breast MRI (AB-MRI) is being rapidly adopted to harness the high sensitivity of screening MRI while addressing issues related to access, cost, and workflow. The successful implementation of an ABI-MRI program requires collaboration across administrative, operational, financial, technical, and clinical providers. Institutions must be thoughtful in defining AB-MRI patient eligibility and providing recommendations for screening intervals, as existing practices are heterogeneous. Similarly, there is no universally accepted AB-MRI protocol, though guiding principles should harmonize abbreviated and full protocols while being mindful of scan duration and table time. The interpretation of AB-MRI will be a new experience for many radiologists and may require a phased rollout as well as a careful audit of performance metrics over time to ensure benchmark metrics are achieved. AB-MRI finances, which are driven by patient self-payment, will require buy-in from hospital administration with the recognition that downstream revenues will be needed to support initial costs. Finally, successful startup of an AB-MRI program requires active engagement with the larger community of patients and referring providers. As AB-MRI becomes more widely accepted and available, best practices and community standards will continue to evolve to ensure high quality patient care.

Breast Cancer Detection using Multimodal Time Series Features from Ultrasound Shear Wave Absolute Vibro-Elastography

In shear wave absolute vibro-elastography (S-WAVE), a steady-state multi-frequency external mechanical excitation is applied to tissue, while a time-series of ultrasound radio-frequency (RF) data are acquired. Our objective is to determine the potential of S-WAVE to classify breast tissue lesions as malignant or benign. We present a new processing pipeline for feature-based classification of breast cancer using S-WAVE data, and we evaluate it on a new data set collected from 40 patients. Novel bi-spectral and Wigner spectrum features are computed directly from the RF time series and are combined with textural and spectral features from B-mode and elasticity images. The Random Forest permutation importance ranking and the Quadratic Mutual Information methods are used to reduce the number of features from 377 to 20. Support Vector Machines and Random Forest classifiers are used with leave-one-patient-out and Monte Carlo cross-validations. Classification results obtained for different feature sets are presented. Our best results (95% confidence interval, Area Under Curve = 95%1.45%, sensitivity = 95%, and specificity = 93%) outperform the state-of-the-art reported S-WAVE breast cancer classification performance. The effect of feature selection and the sensitivity of the above classification results to changes in breast lesion contours is also studied. We demonstrate that time-series analysis of externally vibrated tissue as an elastography technique, even if the elasticity is not explicitly computed, has promise and should be pursued with larger patient datasets. Our study proposes novel directions in the field of elasticity imaging for tissue classification.

Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound

Low specificity and operator dependency are the main problems of breast ultrasound (US) screening. We investigated the added value of deep learning-based computer-aided diagnosis (S-Detect) and shear wave elastography (SWE) to B-mode US for evaluation of breast masses detected by screening US.Between February 2018 and June 2019, B-mode US, S-Detect, and SWE were prospectively obtained for 156 screening US-detected breast masses in 146 women before undergoing US-guided biopsy. S-Detect was applied for the representative B-mode US image, and quantitative elasticity was measured for SWE. Breast Imaging Reporting and Data System final assessment category was assigned for the datasets of B-mode US alone, B-mode US plus S-Detect, and B-mode US plus SWE by 3 radiologists with varied experience in breast imaging. Area under the receiver operator characteristics curve (AUC), sensitivity, and specificity for the 3 datasets were compared using Delong's method and McNemar test.Of 156 masses, 10 (6%) were malignant and 146 (94%) were benign. Compared to B-mode US alone, the addition of S-Detect increased the specificity from 8%-9% to 31%-71% and the AUC from 0.541-0.545 to 0.658-0.803 in all radiologists (All P < .001). The addition of SWE to B-mode US also increased the specificity from 8%-9% to 41%-75% and the AUC from 0.541-0.545 to 0.709-0.823 in all radiologists (All P < .001). There was no significant loss in sensitivity when either S-Detect or SWE were added to B-mode US.Adding S-Detect or SWE to B-mode US improved the specificity and AUC without loss of sensitivity.

European trends in breast cancer mortality, 1980-2017 and predictions to 2025

BACKGROUND

Breast cancer mortality in European women has been falling for three decades. We analysed trends in mortality from breast cancer in Europe over the period 1980-2017 and predicted number of deaths and rates to 2025.

METHODS

We extracted death certification data for breast cancer in women for 35 European countries, between 1980 and 2017, from the World Health Organisation database. We computed the age-standardised (world standard population) mortality rates per 100,000 person-years, by country and calendar year. We obtained also predictions for 2025 using a joinpoint regression model and calculated the number of avoided deaths over the period 1994-2025.

RESULTS

The mortality rate declined from 15.0 in 2012 to 14.4 in 2017 per 100,000 women (-3.9%) for the European Union (EU)-27. This fall was greater in the EU-14 (-5.2%), whereas rates rose in the transitional countries during this period by 1.9%. Mortality rate predictions across Europe are expected to reach relatively uniform levels in 2025. During the studied period, favourable trends in mortality emerged in most countries, with the greatest decrease in Denmark, whereas Poland and Romania showed an upward trend. The largest predicted decrease in breast cancer mortality was estimated for the United Kingdom (12.2/100,000 women in 2025), leading to the estimated avoidance of 150,000 breast cancer deaths over the period 1994-2025 and 470,000 in the EU-27.

CONCLUSIONS

Favourable trends in breast cancer mortality were observed in most European countries, and they will continue to fall in the coming years. Less favourable patterns were still observed among the transitional countries than other European areas.

Recent nanotheranostics applications for cancer therapy and diagnosis: A review

Nanotheranostics has attracted much attention due to its widespread application in molecular imaging and cancer therapy. Molecular imaging using nanoparticles has attracted special attention in the diagnosis of cancer at early stages. With the progress made in nanotheranostics, studying drug release, accumulation in the target tissue, biodistribution, and treatment effectiveness are other important factors. However, according to the studies conducted in this regard, each nanoparticle has some advantages and limitations that should be examined and then used in clinical applications. The main goal of this review is to explore the recent advancements in nanotheranostics for cancer therapy and diagnosis. Then, it is attempted to present recent studies on nanotheranostics used as a contrast agent in various imaging modalities and a platform for cancer therapy.

Addition of Screening Breast US to Digital Mammography and Digital Breast Tomosynthesis for Breast Cancer Screening in Women at Average Risk

Background Digital breast tomosynthesis (DBT) with or without digital mammography (DM) is the primary method of breast cancer screening. However, the sufficiency of DBT screening for women at average risk and the need for supplemental whole-breast US needs further investigation. Purpose To evaluate the added value of supplemental US screening following combined DM/DBT. Materials and Methods A retrospective database search identified consecutive asymptomatic women who underwent DM/DBT and radiologist-performed screening breast US simultaneously between March 2016 and December 2018. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate of DM/DBT and DM/DBT combined with US were compared. Results A total of 1003 women (mean age, 56 years ± 8.6 [standard deviation]) were included. Among them, 12 cancers (mean invasive tumor size, 14 mm; range, 6-33 mm) were diagnosed. With DM/DBT and DM/DBT combined with US, the CDRs were 9.0 per 1000 screening examinations (nine of 1003 women; 95% CI: 4.1, 17) and 12 per 1000 screening examinations (12 of 1003 women; 95% CI: 6.2, 21), respectively, and the abnormal interpretation rates were 7.8% (78 of 1003 women; 95% CI: 6.2, 9.6) and 24% (243 of 1003 women; 95% CI: 22, 27). In women with negative findings at DM/DBT, supplementary US yielded a CDR of 3.2 per 1000 examinations (three of 925 women; 95% CI: 0.7, 9.4), sensitivity of 100% (three of three women; 95% CI: 29, 100), specificity of 82% (760 of 922 women; 95% CI: 80, 85), and abnormal interpretation rate of 18% (165 of 925 women; 95% CI: 15, 21). The three additional US-detected cancers were identified in women with dense breasts; no benefit was observed in women with nondense breasts. Conclusion The addition of breast US to digital mammography and digital breast tomosynthesis yielded an additional 0.7-9.4 cancers per 1000 women at average risk, with a substantial increase in the abnormal interpretation rate. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rahbar in this issue.

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists' Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P = .014), and patient age (P < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists' BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P < .001 in the development cohort; 97% vs. 45% P < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

Skin Electrical Resistance as a Diagnostic and Therapeutic Biomarker of Breast Cancer Measuring Lymphatic Regions

Skin changes associated with alterations in the interstitial matrix and lymph system might provide significant and measurable effects due to the presence of breast cancer. This study aimed to determine if skin electrical resistance changes could serve as a diagnostic and therapeutic biomarker associated with physiological changes in patients with malignant versus benign breast cancer lesions. Forty-eight women (24 with malignant cancer, 23 with benign lesions) were enrolled in this study. Repeated skin resistance measurements were performed within the same session and 1 week after the first measurement in the breast lymphatic region and non-breast lymphathic regions. Intraclass correlation coefficients were calculated to determine the technique's intrasession and intersession reproducibility. Data were then normalized as a mean of comparing cross-sectional differences between malignant and benign lesions of the breast. Six months longitudinal data from six patients that received therapy were analyzed to detect the effect of therapy. Standard descriptive statistics were used to compare ratiometric differences between groups. Skin resistance data were used to train a machine learning random forest classification algorithm to diagnose breast cancer lesions. Significant differences between malignant and benign breast lesions were obtained (p<0.01), also pre- and post-treatment (p<0.05). The diagnostic algorithm demonstrated the capability to classify breast cancer with an area under the curve of 0.68, sensitivity of 66.3%, specificity of 78.5%, positive predictive value 70.7% and negative predictive value 75.1%. Measurement of skin resistance in patients with breast cancer may serve as a convenient screening tool for breast cancer and evaluation of therapy. Further work is warranted to improve our approach and further investigate the biophysical mechanisms leading to the observed changes.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Tumor Proliferation and Invasiveness Derived From Ultrasound Appearances of Invasive Breast Cancers: Moving Beyond the Routine Differential Diagnosis

OBJECTIVES

To investigate the correlation between ultrasound (US) appearances of invasive breast cancers and tumor proliferation and invasiveness measured according to the histologic grade, Ki-67 expression, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI).

METHODS

This study evaluated 676 patients who underwent primary surgical treatment of invasive breast cancers. The preoperative US reports and postoperative pathologic and immunohistochemical results of the patients were retrospectively reviewed. Ultrasound characteristics were evaluated according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon. Logistic regression analyses were used to identify independent predictive US features that were correlated with tumor proliferation and invasiveness of breast cancers. Odds ratios (ORs) were calculated.

RESULTS

Posterior acoustic enhancement and calcifications on US images were independent predictive factors of a higher histologic grade and a higher Ki-67 level (OR, 1.69-6.54; P < .05). Meanwhile, a noncircumscribed margin (OR, 2.61; P < .05) and posterior acoustic shadow (OR, 1.62; P < .05) were independent predictors of ALNM. An irregular shape (OR, 2.13; P < .05) and calcifications (OR, 1.69; P < .05) were independent risk factors for LVI. Infiltrative breast cancers scored as BI-RADS category 5 had higher probability to be associated with ALNM (OR, 3.33; P < .0005) and LVI (OR, 2.87; P < .0005).

CONCLUSIONS

Ultrasound features of invasieve breast cancers might have a predictive value for tumor proliferation and invasiveness. The US features correlated with a high cellular proliferation rate were different from those associated with ALNM. The tumor shape, margin, posterior acoustic pattern, and calcifications at US are suggested to be considered by clinicians when making clinical decisions.

Automatic breast ultrasound: state of the art and future perspectives

The three-dimensional automated breast ultrasound system (3D ABUS) is a new device which represents a huge innovation in the breast ultrasound field, with several application scenarios of great interest.

ABUS's aim is to solve some of the main defects of traditional ultrasound, such as lack of standardization, high level of skill non-reproducibility, small field of view and high commitment of physician time. ABUS has proven to be an excellent non-ionising alternative to other supplemental screening options for women with dense breast tissue; also, it has appeared to be very promising in daily clinical practice.

The purpose of this paper is to present a summary of current applications of ABUS, focusing on clinical applications and future perspectives as ABUS is particularly promising for studies involving artificial intelligence, radiomics and evaluation of breast molecular subtypes.

How can additional ultrasonography screening improve the detection of occult breast cancer in women with dense breasts?

Purpose

Although mammography is a gold standard for breast cancer screening, the number of cancers that cannot be detected with mammography is substantial, especially in dense-breast (DB) women. Breast sonography can be a useful and powerful screening tool in these cases. The aim of this study is to assess the application of whole-breast sonography in the evaluation of breast lesions in women with DB tissue and estimate its accuracy in comparison with mammography.

Material and methods

A total of 207 asymptomatic DB women participated in this study. The breast tissue density was assessed using ACR BI-RADS. Patients underwent high-resolution ultrasonography of the breast in addition to physical examination and mammography. Different risk factors were also assessed.

Results

152 of 207 (73.4%) cases who had mammography performed had DB, and 55 (26.6%) cases had very dense breasts (very DBs). None of the cases had a positive history of malignancy, while 19% of them had a positive history of breast cancer in first- or second-degree relatives.

Conclusions

All findings were higher in cases with DB compared to very DBs except for fibroadenoma, which was detected more in cases with very DBs. Our study showed that the prevalence of different breast lesions had a significant relationship with the density of the breast. In our study, 48.3% of the cases were diagnosed with a lesion in their sonography result, although 81.0% of them were benign lesions, and the other 19.0% needed follow-up or biopsy evaluation. A substantial number of mammographically occult breast lesions, either benign or malignant, could be detected by ultrasound in DB tissue.