Diagnostic Performance of Adjunctive Imaging Modalities Compared to Mammography Alone in Women with Non-Dense and Dense Breasts: A Systematic Review and Meta-Analysis

Access to prior screening mammograms affects the specificity but not sensitivity of radiologists' performance

AIMS

To establish the impact that access to prior mammograms has on radiologists' performance and the influence of radiologists' characteristics and breast density on their subsequent performance.

METHODS

Eight participants independently interpreted 72 digital screening mammograms in two reading sessions using the Royal Australian and New Zealand College of Radiologist's classification. In the first reading session, participants were given access to current and prior mammograms. In the second reading session six months later, participants only had access to the current mammograms. Radiologists' specificity, sensitivity, lesion sensitivity, Receiver Operating Characteristic (ROC) curve, and Jacknife Alternative Free-response ROC (JAFROC) were calculated. A Paired T-test was used to compare readings with and without prior mammograms, and to assess if breast density influenced participants performance. Independent Sample T-test was used to compare performance across radiologists' characteristics. A relative risk analysis was conducted to assess the probability of false positives and false negatives when prior mammograms were available.

RESULTS

Access to prior mammograms improved specificity in dense and non-dense breasts (p≤0.01) and reduced false positives (p = 0.01) but had no effect on sensitivity (p = 0.37), lesion sensitivity (p = 0.67), ROC (p = 0.16), and JAFROC (p = 0.24). Prior mammogram also reduced the probability of false positives (RR = 0.38; 95%CI:0.26-0.57, p<0.0001) without affecting the false negative rate (RR = 1.14; 95%CI:0.88-1.49, p = 0.30). The impact of prior mammograms on performance was not influenced by breast density or radiologists' characteristics.

CONCLUSIONS

Access to prior mammograms improves radiologists' specificity and reduces false positives without affecting sensitivity and the false negative rate regardless of radiologists' characteristics and breast density.

Imaging the dense breast

The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.

Arbeitsgemeinschaft Gynäkologische Onkologie Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2024

Introduction

Each year the interdisciplinary AGO (Arbeitsgemeinschaft Gynäkologische Onkologie, German Gynecological Oncology Group) Breast Committee on Diagnosis and Treatment of Breast Cancer provides updated state-of-the-art recommendations for early and metastatic breast cancer.

Methods

The updated evidence-based treatment recommendations for early and metastatic breast cancer have been released in March 2024.

Results and Conclusion

This paper concisely captures the updated recommendations for early breast cancer chapter by chapter.

BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems

PURPOSE

Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI-RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above-mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems.

ACQUISITION AND VALIDATION METHODS

The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy-proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI-RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5- and 10-fold cross-validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions.

DATA FORMAT AND USAGE NOTES

The BUS dataset is publicly available for academic and research purposes through an open-access repository under the name BUS-BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma-Separated Value (CSV) files.

POTENTIAL APPLICATIONS

The BUS-BRA dataset can be used to develop and assess artificial intelligence-based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI-RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.

Supplemental magnetic resonance imaging plus mammography compared with magnetic resonance imaging or mammography by extent of breast density

BACKGROUND

Examining screening outcomes by breast density for breast magnetic resonance imaging (MRI) with or without mammography could inform discussions about supplemental MRI in women with dense breasts.

METHODS

We evaluated 52 237 women aged 40-79 years who underwent 2611 screening MRIs alone and 6518 supplemental MRI plus mammography pairs propensity score-matched to 65 810 screening mammograms. Rates per 1000 examinations of interval, advanced, and screen-detected early stage invasive cancers and false-positive recall and biopsy recommendation were estimated by breast density (nondense = almost entirely fatty or scattered fibroglandular densities; dense = heterogeneously/extremely dense) adjusting for registry, examination year, age, race and ethnicity, family history of breast cancer, and prior breast biopsy.

RESULTS

Screen-detected early stage cancer rates were statistically higher for MRI plus mammography vs mammography for nondense (9.3 vs 2.9; difference = 6.4, 95% confidence interval [CI] = 2.5 to 10.3) and dense (7.5 vs 3.5; difference = 4.0, 95% CI = 1.4 to 6.7) breasts and for MRI vs MRI plus mammography for dense breasts (19.2 vs 7.5; difference = 11.7, 95% CI = 4.6 to 18.8). Interval rates were not statistically different for MRI plus mammography vs mammography for nondense (0.8 vs 0.5; difference = 0.4, 95% CI = -0.8 to 1.6) or dense breasts (1.5 vs 1.4; difference = 0.0, 95% CI = -1.2 to 1.3), nor were advanced cancer rates. Interval rates were not statistically different for MRI vs MRI plus mammography for nondense (2.6 vs 0.8; difference = 1.8 (95% CI = -2.0 to 5.5) or dense breasts (0.6 vs 1.5; difference = -0.9, 95% CI = -2.5 to 0.7), nor were advanced cancer rates. False-positive recall and biopsy recommendation rates were statistically higher for MRI groups than mammography alone.

CONCLUSION

MRI screening with or without mammography increased rates of screen-detected early stage cancer and false-positives for women with dense breasts without a concomitant decrease in advanced or interval cancers.

Breast Cancer Screening and Treatment Clinical Trials Updated for 2023

There are many active or recently completed breast cancer screening and treatment trials in 2023 that have the potential to fundamentally change the way breast radiologists practice medicine. Breast cancer screening trials may provide evidence to support supplemental screening beyond mammography to include US, contrast-enhanced mammography, and breast MRI. Furthermore, there are multiple efforts to support risk-adaptive screening strategies that would personalize screening modalities, frequencies, and ages of initiation. For breast cancer treatment, aims to reduce overtreatment may provide nonsurgical treatment options for women with low-risk breast cancer. Breast radiologists must be familiar with the study designs, major inclusion and exclusion criteria, and principal endpoints in order to determine when and how the study results should influence clinical care. As multidisciplinary team members, breast radiologists will have major roles in the success or failure of these trials as they transition from research to actual clinical practice.

Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks

Breast ultrasound (BUS) image classification in benign and malignant classes is often based on pre-trained convolutional neural networks (CNNs) to cope with small-sized training data. Nevertheless, BUS images are single-channel gray-level images, whereas pre-trained CNNs learned from color images with red, green, and blue (RGB) components. Thus, a gray-to-color conversion method is applied to fit the BUS image to the CNN's input layer size. This paper evaluates 13 gray-to-color conversion methods proposed in the literature that follow three strategies: replicating the gray-level image to all RGB channels, decomposing the image to enhance inherent information like the lesion's texture and morphology, and learning a matching layer. Besides, we introduce an image decomposition method based on the lesion's structural information to describe its inner and outer complexity. These gray-to-color conversion methods are evaluated under the same experimental framework using a pre-trained CNN architecture named ResNet-18 and a BUS dataset with more than 3000 images. In addition, the Matthews correlation coefficient (MCC), sensitivity (SEN), and specificity (SPE) measure the classification performance. The experimental results show that decomposition methods outperform replication and learning-based methods when using information from the lesion's binary mask (obtained from a segmentation method), reaching an MCC value greater than 0.70 and specificity up to 0.92, although the sensitivity is about 0.80. On the other hand, regarding the proposed method, the trade-off between sensitivity and specificity is better balanced, obtaining about 0.88 for both indices and an MCC of 0.73. This study contributes to the objective assessment of different gray-to-color conversion approaches in classifying breast lesions, revealing that mask-based decomposition methods improve classification performance. Besides, the proposed method based on structural information improves the sensitivity, obtaining more reliable classification results on malignant cases and potentially benefiting clinical practice.

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.

Artificial intelligence to support person-centred care in breast imaging - A scoping review

AIM

To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies.

METHODS

The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage.

RESULTS

3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used.

CONCLUSIONS

The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.

Arbeitsgemeinschaft Gynäkologische Onkologie Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2023

Background

Each year the interdisciplinary Arbeitsgemeinschaft Gynäkologische Onkologie (AGO), German Gynecological Oncology Group Breast Committee on Diagnosis and Treatment of Breast Cancer provides updated state-of-the-art recommendations for early and metastatic breast cancer.

Summary

The updated evidence-based treatment recommendation for early and metastatic breast cancer has been released in March 2023.

Key Messages

This paper concisely captures the updated recommendations for early breast cancer chapter by chapter.

Recommendations for the Screening of Breast Cancer of the Brazilian College of Radiology and Diagnostic Imaging, Brazilian Society of Mastology and Brazilian Federation of Gynecology and Obstetrics Association

OBJECTIVE

 To present the update of the recommendations of the Brazilian College of Radiology and Diagnostic Imaging, the Brazilian Society of Mastology and the Brazilian Federation of Associations of Gynecology and Obstetrics for breast cancer screening in Brazil.

METHODS

 Scientific evidence published in Medline, EMBASE, Cochrane Library, EBSCO, CINAHL and Lilacs databases between January 2012 and July 2022 was searched. Recommendations were based on this evidence by consensus of the expert committee of the three entities.

RECOMMENDATIONS

 Annual mammography screening is recommended for women at usual risk aged 40-74 years. Above 75 years, it should be reserved for those with a life expectancy greater than seven years. Women at higher than usual risk, including those with dense breasts, with a personal history of atypical lobular hyperplasia, classic lobular carcinoma in situ, atypical ductal hyperplasia, treatment for breast cancer or chest irradiation before age 30, or even, carriers of a genetic mutation or with a strong family history, benefit from complementary screening, and should be considered individually. Tomosynthesis is a form of mammography and should be considered in screening whenever accessible and available.

Recommendations for breast cancer screening in Brazil, from the Brazilian College of Radiology and Diagnostic Imaging, the Brazilian Society of Mastology, and the Brazilian Federation of Gynecology and Obstetrics Associations

Objective

To present an update of the recommendations of the Brazilian College of Radiology and Diagnostic Imaging, the Brazilian Society of Mastology, and the Brazilian Federation of Gynecology and Obstetrics Associations for breast cancer screening in Brazil.

Materials and Methods

Scientific evidence published between January 2012 and July 2022 was gathered from the following databases: Medline (PubMed); Excerpta Medica (Embase); Cochrane Library; Ebsco; Cumulative Index to Nursing and Allied Health Literature (Cinahl); and Latin-American and Caribbean Health Sciences Literature (Lilacs). Recommendations were based on that evidence and were arrived at by consensus of a joint committee of experts from the three entities.Recommendations: Annual mammographic screening is recommended for women between 40 and 74 years of age. For women at or above the age of 75, screening should be reserved for those with a life expectancy greater than seven years. Women at higher than average risk are considered by category: those with dense breasts; those with a personal history of atypical lobular hyperplasia, classical lobular carcinoma in situ, or atypical ductal hyperplasia; those previously treated for breast cancer; those having undergone thoracic radiotherapy before age 30; and those with a relevant genetic mutation or a strong family history. The benefits of complementary screening are also addressed according to the subcategories above. The use of tomosynthesis, which is an evolved form of mammography, should be considered in screening, whenever accessible and available.

Performance of Supplemental Imaging Modalities for Breast Cancer in Women With Dense Breasts: Findings From an Umbrella Review and Primary Studies Analysis

Breast cancer screening performance of supplemental imaging modalities by breast density and breast cancer risk has not been widely studied, and the optimal choice of modality for women with dense breasts remains unclear in clinical practice and guidelines. This systematic review aimed to assess breast cancer screening performance of supplemental imaging modalities for women with dense breasts, by breast cancer risk. Systematic reviews (SRs) in 2000 to 2021, and primary studies in 2019 to 2021, on outcomes of supplemental screening modalities (digital breast tomography [DBT], MRI (full/abbreviated protocol), contrast enhanced mammography (CEM), ultrasound (hand-held [HHUS]/automated [ABUS]) in women with dense breasts (BI-RADS C&D) were identified. None of the SRs analyzed outcomes by cancer risk. Meta-analysis of the primary studies was not feasible due to lack of studies (MRI, CEM, DBT) or methodological heterogeneity (ultrasound); therefore, findings were summarized narratively. For average risk, a single MRI trial reported a superior screening performance (higher cancer detection rate [CDR] and lower interval cancer rate [ICR]) compared to HHUS, ABUS and DBT. For intermediate risk, ultrasound was the only modality assessed, but accuracy estimates ranged widely. For mixed risk, a single CEM study reported the highest CDR, but included a high proportion of women with intermediate risk. This systematic review does not allow a complete comparison of supplemental screening modalities for dense breast populations by breast cancer risk. However, the data suggest that MRI and CEM might generally offer superior screening performance versus other modalities. Further studies of screening modalities are urgently required.

Development of a breast cancer screening protocol to use automated breast ultrasound in a local setting

Introduction

The sensitivity of mammography screening is lower in women with dense breast. Increasing the efficacy of breast cancer screening have received special attention recently. The automated breast ultrasound (ABUS) shows promising results to complement mammography. Our aim was to expand the existing breast cancer screening protocol with ABUS within a Hungarian pilot project.

Methods

First, we developed a protocol for the screening process focusing on integrating ABUS to the current practice. Consensus among clinical experts was achieved considering information from the literature and the actual opportunities of the hospital. Then we developed a protocol for evaluation that ensures systematic data collection and monitoring of screening with mammography and ABUS. We identified indicators based on international standards and adapted them to local setting. We considered their feasibility from the data source and timeframe perspective. The protocol was developed in a partnership of researchers, clinicians and hospital managers.

Results

The process of screening activity was described in a detailed flowchart. Human and technological resource requirements and communication activities were defined. We listed 23 monitoring indicators to evaluate the screening program and checked the feasibility to calculate these indicators based on local data collection and other sources. Partnership between researchers experienced in planning and evaluating screening programs, interested clinicians, and hospital managers resulted in a locally implementable, evidence-based screening protocol.

Discussion

The experience and knowledge gained on the implementation of the ABUS technology could generate real-world data to support the decision on using the technology at national level.

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

Background: To compare the diagnostic efficacy of digital breast tomosynthesis (DBT) and ultrasound across breast densities in women recalled for assessment. Methods: A total of 482 women recalled for assessment from January 2017 to December 2019 were selected for the study. Women met the inclusion criteria if they had undergone DBT, ultrasound and had confirmed biopsy results. We calculated sensitivity, specificity, PPV, and AUC for DBT and ultrasound. Results: In dense breasts, DBT showed significantly higher sensitivity than ultrasound (98.2% vs. 80%; p < 0.001), but lower specificity (15.4% vs. 55%; p < 0.001), PPV (61.3% vs. 71%; p = 0.04) and AUC (0.568 vs. 0.671; p = 0.001). In non-dense breasts, DBT showed significantly higher sensitivity than ultrasound (99.2% vs. 84%; p < 0.001), but no differences in specificity (22% vs. 33%; p = 0.14), PPV (69.2% vs. 68.8%; p = 0.93) or AUC (0.606 vs. 0.583; p = 0.57). Around 73% (74% dense and 71% non-dense) and 77% (81% dense and 72% non-dense) of lesions assigned a RANZCR 3 by DBT and ultrasound, respectively, were benign. Conclusion: DBT has higher sensitivity, but lower specificity and PPV than ultrasound in women with dense breasts recalled for assessment. Most lesions rated RANZCR 3 on DBT and ultrasound are benign and may benefit from short interval follow-up rather than biopsy.

Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head

INTRODUCTION AND BACKGROUND

Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality.

METHODS

This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets.

RESULTS

A detailed evaluation of the proposed model's accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset.

CONCLUSIONS

The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.