Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark

US-based radiomics analysis of different machine learning models for differentiating benign and malignant BI-RADS 4A breast lesions

RATIONALE AND OBJECTIVES

To investigate and authenticate the effectiveness of various radiomics models in distinguishing between benign and malignant BI-RADS 4A lesions.

METHODS

A total of 936 patients with pathologically confirmed 4A lesions were included in the study (training cohort: n = 655; test cohort: n = 281). Radiomic features were derived from greyscale US images. Following dimensionality reduction and feature selection, radiomics models were developed using logistic regression (LR), support vector machine (SVM), random forest (RF), eXtreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. Univariate and multivariable logistic regression analyses were employed to investigate clinical-radiological characteristics and determine variables for creating a clinical model. Five combined models integrating radiomic and clinical parameters were constructed by using each algorithm, and comparison with radiologists' performance was performed. SHapley Additive exPlanations (SHAP) approach was used to elucidate the radiomic model by ranking the significance of features based on their contribution to the evaluation.

RESULTS

A total of 1561 radiomic features were extracted. Thirty-six features were deemed significant by dimensionality reduction and selection. The radiomic models showed good performance with AUCs of 0.829-0.945 in training cohort; and 0.805-0.857 in test cohort. The combined model developed by using LR showed the best performance (AUC, training cohort: 0.909; test cohort: 0.905), which is superior to radiologists' performance. Decision curve analysis (DCA) of this combined model indicated better clinical efficacy than clinical and radiomic models.

CONCLUSIONS

The combined model integrating radiomic and clinical features demonstrated excellent performance in differentiating between benign and malignant 4A lesions. It may offer a non-invasive and efficient approach to aid in clinical decision-making.

Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening

Assessing a woman's risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1-4) and a deep-learning texture risk model, with scores categorized into four quartiles (1-4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43-4.82)-4.57 (95% CI: 3.66-5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77-13.45)-16.94 (95% CI: 9.93-30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31-6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening.

Role of Supplemental Breast MRI in Screening Women with Mammographically Dense Breasts: A Systematic Review and Meta-analysis

BACKGROUND

High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk.

METHODS

PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277).

RESULTS

Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies.

CONCLUSION

Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings.

Validation of Contrast-Enhanced Mammography as Breast Imaging Modality Compared to Standard Mammography and Digital Breast Tomosynthesis

Contrast-enhanced mammography (CEM) is a relatively new imaging technique that allows morphologic, anatomic and functional imaging of the breast. The aim of our study was to validate contrast-enhanced mammography (CEM) compared to mammography (MMG) and digital breast tomosynthesis (DBT) in daily clinical practice. This retrospective study included 316 consecutive patients who underwent MMG, DBT and CEM at the Centre for Prevention and Diagnosis of Chronic Diseases of Primorsko-goranska County. Two breast radiologists independently analyzed the image data, without available anamnestic information and without the possibility of comparison with previous images, to determine the presence of suspicious lesions and their morphological features according to the established criteria of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The diagnostic value of MMG, DBT and CEM was assessed by ROC analysis. The interobserver agreement was excellent. CEM showed higher diagnostic accuracy in terms of sensitivity and specificity compared to MMG and DBT, the reporting time for CEM was significantly shorter, and CEM findings resulted in a significantly lower proportion of equivocal findings (BI-RADS 0), suggesting fewer additional procedures. In conclusion, CEM achieves high diagnostic accuracy while maintaining simplicity, reproducibility and applicability in complex clinical settings.

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.

Bridging Gaps: Analyzing Breast Imaging-Reporting and Data System (BI-RADS) 0 Rates and Associated Risk Factors in Disproportionally Affected Communities

Introduction Disparities in access to breast cancer screening led to the creation of the Linda Fenner 3D Mobile Mammography Center (LFMMC), successfully increasing screening for uninsured women in Miami-Dade. However, a higher-than-expected rate of inconclusive mammograms (Breast Imaging-Reporting and Data System (BI-RADS) 0) was found, which could lead to unnecessary procedures, stress, costs, and radiation. Methods In this retrospective cross-sectional study, we analyzed data from 3,044 uninsured women aged over 40 (younger if positive family history of breast cancer) from Miami-Dade without breast symptoms or breast cancer history. Women's demographic characteristics, primary language spoken, body mass index (BMI), use of hormone replacement therapy and birth control, history of benign biopsy, breast surgery, family breast cancer, and menopausal status were assessed as potential risk factors for an inconclusive (BI-RADS 0) screening mammogram result. Multivariable logistic regression analyses were used to evaluate associations. Results The average age of women was 51 years (SD = 9); 59% were White, and 30% were African American. The overall frequency of BI-RADS 0 was 35%. Higher odds of BI-RADS 0 were found for women who were younger, single, premenopausal, and with benign biopsy history. Conversely, obesity and breast implant history decreased the odds of BI-RADS 0. Conclusion We found a high frequency of BI-RADS 0 in the LFMMC sample. Potential reasons include a higher risk for breast cancer or a younger sample of women screened. Future research should explore radiologists' reasoning for assigning BI-RADS 0 results and testing alternative screening strategies for younger women.

Status of breast cancer detection in young women and potential of liquid biopsy

Young onset breast cancer (YOBC) is an increasing demographic with unique biology, limited screening, and poor outcomes. Further, women with postpartum breast cancers (PPBCs), cancers occurring up to 10 years after childbirth, have worse outcomes than other young breast cancer patients matched for tumor stage and subtype. Early-stage detection of YOBC is critical for improving outcomes. However, most young women (under 45) do not meet current age guidelines for routine mammographic screening and are thus an underserved population. Other challenges to early detection in this population include reduced performance of standard of care mammography and reduced awareness. Women often face significant barriers in accessing health care during the postpartum period and disadvantaged communities face compounding barriers due to systemic health care inequities. Blood tests and liquid biopsies targeting early detection may provide an attractive option to help address these challenges. Test development in this area includes understanding of the unique biology involved in YOBC and in particular PPBCs that tend to be more aggressive and deadly. In this review, we will present the status of breast cancer screening and detection in young women, provide a summary of some unique biological features of YOBC, and discuss the potential for blood tests and liquid biopsy platforms to address current shortcomings in timely, equitable detection.

Implementing supplementary breast cancer screening in women with dense breasts: Insights from European radiographers and radiologists

INTRODUCTION

In response to the critical need for enhancing breast cancer screening for women with dense breasts, this study explored the understanding of challenges and requirements for implementing supplementary breast cancer screening for such women among clinical radiographers and radiologists in Europe.

METHOD

Fourteen (14) semi-structured online interviews were conducted with European clinical radiologists (n = 5) and radiographers (n = 9) specializing in breast cancer screening from 8 different countries: Denmark, Finland, Greece, Italy, Malta, the Netherlands, Switzerland, United Kingdom. The interview schedule comprised questions regarding professional background and demographics and 13 key questions divided into six subgroups, namely Supplementary Imaging, Training, Resources and Guidelines, Challenges, Implementing supplementary screening and Women's Perspective. Data analysis followed the six phases of reflexive thematic analysis.

RESULTS

Six significant themes emerged from the data analysis: Understanding and experiences of supplementary imaging for women with dense breasts; Challenges and requirements related to training among clinical radiographers and radiologists; Awareness among radiographers and radiologists of guidelines on imaging women with dense breasts; Challenges to implement supplementary screening; Predictors of Implementing Supplementary screening; Views of radiologists and radiographers on women's perception towards supplementary screening.

CONCLUSION

The interviews with radiographers and radiologists provided valuable insights into the challenges and potential strategies for implementing supplementary breast cancer screening. These challenges included patient and staff related challenges. Implementing multifaceted solutions such as Artificial Intelligence integration, specialized training and resource investment can address these challenges and promote the successful implementation of supplementary screening. Further research and collaboration are needed to refine and implement these strategies effectively.

IMPLICATIONS FOR PRACTICE

This study highlights the urgent need for specialized training programs and dedicated resources to enhance supplementary breast cancer screening for women with dense breasts in Europe. These resources include advanced imaging technologies, such as MRI or ultrasound, and specialized software for image analysis. Moreover, further research is imperative to refine screening protocols and evaluate their efficacy and cost-effectiveness, based on the findings of this study.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection

BACKGROUND

Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women.

METHODS

We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories.

RESULTS

Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category.

CONCLUSIONS

AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Surgical and Radiology Trainees' Proficiency in Reading Mammograms: the Importance of Education for Cancer Localisation

Medical imaging with mammography plays a very important role in screening and diagnosis of breast cancer, Australia's most common female cancer. The visualisation of cancers on mammograms often forms a diagnosis and guidance for radiologists and breast surgeons, and education platforms that provide real cases in a simulated testing environment have been shown to improve observer performance for radiologists. This study reports on the performance of surgical and radiology trainees in locating breast cancers. An enriched test set of 20 mammography cases (6 cancer and 14 cancer free) was created, and 18 surgical trainees and 32 radiology trainees reviewed the cases via the Breast Screen Reader Assessment Strategy (BREAST) platform and marked any lesions identifiable. Further analysis of performance with high- and low-density cases was undertaken, and standard metrics including sensitivity and specificity. Radiology trainees performed significantly better than surgical trainees in terms of specificity (0.72 vs. 0.35; P < 0.01). No significant differences were observed between the surgical and radiology trainees in sensitivity or lesion sensitivity. Mixed results were obtained with participants regarding breast density, with higher density cases generally having lower performance. The higher specificity of the radiology trainees compared to the surgical trainees likely represents less exposure to negative mammography cases. The use of high-fidelity simulated self-test environments like BREAST is able to benchmark, understand and build strategies for improving cancer education in a safe environment, including identifying challenging scenarios like breast density for enhanced training.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound

RATIONALE AND OBJECTIVES

This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS).

MATERIALS AND METHODS

ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared.

RESULTS

For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing.

CONCLUSION

AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.

Diagnostic performance of screening mammography according to menstrual cycle among Asian women

PURPOSE

To investigate the performance metrics of screening mammography according to menstrual cycle week in premenopausal Asian women.

METHODS

This retrospective study included 69,556 premenopausal Asian women who underwent their first screening mammography between 2011 and 2019. The presence or absence of a breast cancer diagnosis within 12 months after the index screening mammography served as the reference standard, determined by linking the study data to the national cancer registry data. Menstrual cycles were calculated, and participants were assigned to groups according to weeks 1-4. The performance metrics included cancer detection rate (CDR), sensitivity, specificity, and positive predictive value (PPV), with comparisons across menstrual cycles.

RESULTS

Among menstrual cycles, the lowest CDR at 4.7 per 1000 women (95% confidence interval [CI], 3.8-5.8 per 1000 women) was observed in week 4 (all P < 0.05). The highest sensitivity of 72.7% (95% CI, 61.4-82.3) was observed in week 1, although the results failed to reach statistical significance. The highest specificity of 80.4% (95% CI, 79.5-81.3%) was observed in week 1 (P = 0.01). The lowest PPV of 2.2% (95% CI, 1.8-2.7) was observed in week 4 (all P < 0.05).

CONCLUSION

Screening mammography tended to show a higher performance during week 1 and a lower performance during week 4 of the menstrual cycle among Asian women. These results emphasize the importance of timing recommendations that consider menstrual cycles to optimize the effectiveness of screening mammography for breast cancer detection.

Microwave Breast Lesion Classification - Results from Clinical Investigation of the SAFE Microwave Breast Cancer System

RATIONALE AND OBJECTIVES

Microwave breast cancer imaging (MWI) is an emerging non-invasive technology used to clinically assess the internal breast tissue inhomogeneity. MWI utilizes the variance in dielectric properties of healthy and cancerous tissue to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate our SAFE MWI system in a clinical setting. Capability of SAFE to provide breast pathology is assessed.

MATERIALS AND METHODS

Patients with BI-RADS category 4 or 5 who were scheduled for biopsy were included in the study. Machine learning approach, more specifically the Adaptive Boosting (AdaBoost) model, was implemented to determine if the level of difference between backscattered signals of breasts with the benign and malignant pathological outcome is significant enough for quantitative breast health classification via SAFE.

RESULTS

A dataset of 113 (70 benign and 43 malignant) breast samples was used in the study. The proposed classification model achieved the sensitivity, specificity, and accuracy of 79%, 77%, and 78%, respectively.

CONCLUSION

The non-ionizing and non-invasive nature gives SAFE an opportunity to impact breast cancer screening and early detection positively. Device classified both benign and malignant lesions at a similar rate. Further clinical studies are planned to validate the findings of this study.

Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.

Screening Outcomes of Supplemental Automated Breast US in Asian Women with Dense and Nondense Breasts

Background Automated breast (AB) US effectively depicts mammographically occult breast cancers in Western women. However, few studies have focused on the outcome of supplemental AB US in Asian women who have denser breasts than Western women. Purpose To evaluate the performance of supplemental AB US on mammography-based breast cancer screening in Asian women with dense breasts and those with nondense breasts. Materials and Methods A retrospective database search identified asymptomatic Korean women who underwent digital mammography (DM) and supplemental AB US screening for breast cancer between January 2018 and December 2019. We excluded women without sufficient follow-up, established final diagnosis, or histopathologic results. Performance measures of DM alone and AB US combined with DM (hereafter AB US plus DM) were compared. The primary outcome was cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed based on mammography density. Results From 2785 screening examinations in 2301 women (mean age, 52 years ± 9 [SD]), 28 cancers were diagnosed (26 screening-detected cancers, two interval cancers). When compared with DM alone, AB US plus DM resulted in a higher CDR of 9.3 per 1000 examinations (95% CI: 7.7, 10.3) versus 6.5 per 1000 examinations (95% CI: 5.2, 7.2; P < .001) and a higher sensitivity of 90.9% (95% CI: 77.3, 100.0) versus 63.6% (95% CI: 40.9, 81.8; P < .001) but a lower specificity of 86.8% (95% CI: 85.2, 88.2) versus 94.6% (95% CI: 93.6, 95.5; P < .001) in women with dense breasts. In women with nondense breasts, AB US plus DM resulted in a higher CDR of 9.5 per 1000 examinations (95% CI: 7.1, 10.6) versus 6.3 per 1000 examinations (95% CI: 3.5, 7.1; P < .001), whereas specificity was lower at 95.2% (95% CI: 93.4, 96.8) versus 97.1% (95% CI: 95.8, 98.4; P < .001). Conclusion In Asian women, the addition of automated breast US to digital mammography showed higher cancer detection rates but lower specificities in both dense and nondense breasts. © RSNA, 2023 Supplemental material is available for this article.

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.

Performance analysis of screening mammography in Asian women under 40 years

BACKGROUND

Screening mammography performance among young women remains uncertain in East Asia, where the proportion of young breast cancer patients is higher than that in Western countries. Thus, we analyzed the performance of screening mammography in women under 40 years in comparison with older age groups.

METHODS

This retrospective study comprised 95,431 Asian women with 197,525 screening mammograms. The reference standard was determined by linkage to the national cancer registry data and the 12-month follow-up outcomes after the index mammogram. The performance metrics included sensitivity, specificity, cancer detection rate (CDR), positive predictive value (PPV), recall rate, and areas under the receiver operating characteristic curve (AUCs), with comparisons across age groups (30 s, 40 s, and ≥ 50 s).

RESULTS

For young women aged < 40 years, sensitivity and AUC (95% confidence interval [CI]) of screening mammography were 60.4% (50.4-69.7) and 0.73 (0.68-0.77), respectively, with no significant difference compared to women in their 40 s (sensitivity: 64.0% [95% CI: 57.8-69.8], P = 0.52; AUC: 0.75 [95% CI: 0.73-0.78], P = 0.35). The CDR (95% CI) was 0.8 (0.6-1.1) per 1,000 mammograms for young women, poorer than 1.8 (1.6-2.1) per 1,000 mammograms for women in their 40 s (P < 0.001). The PPV and recall rate (95% CI) for young women were 0.6% (0.4-0.7) and 14.9% (14.6-15.1), poorer than 1.4% (1.2-1.6) and 13.3% (13.1-13.5) for women in their 40 s (P < 0.001), respectively.

CONCLUSION

The accuracy of screening mammography for young women in their 30 s was not significantly different from that for women in their 40 s, but the cancer detection and recall rates were poorer.

Audit of Prior Screening Mammograms of Screen-Detected Cancers: Implications for the Delay in Breast Cancer Detection

When cancer is detected in a screening mammogram, on occasion retrospective review of prior screening (pre-index) mammograms indicates a likely presence of cancer. These missed cancers during pre-index screens constitute a delay in detection and diagnosis. This study was undertaken to quantify the missed cancer rate by auditing pre-index screens to improve the quality of mammography screening practice. From a cohort of 135 screen-detected cancers, 120 pre-index screening mammograms could be retrieved and served as the study sample. A consensus read by 2 radiologists who interpreted the pre-index screens in an unblinded manner with full knowledge of cancer location, cancer type, lesion type, and pathology served as the truth or reference standard. Five radiologists interpreted the pre-index screens in a blinded manner. Established performance metrics such as sensitivity and specificity were quantified for each reader in interpreting these pre-index screens in a blinded manner. All five radiologists detected lesions in 8/120 (6.7%) screens. Excluding the 2 readers whose performance was close to random, all the 3 remaining readers detected lesions in 13 pre-index screens. This indicates that there is a delay in diagnosis by at least one cycle from 8/120 (6.7%) to 13/120 (10.8%). There were no observable trends in terms of either the cancer type or the lesion type. Auditing prior screening mammograms in screen-detected cancers can help in identifying the proportion of cases that were missed during interpretation and help in quantifying the delay in breast cancer detection.

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.

Gradient-Boosting Algorithm for Microwave Breast Lesion Classification-SAFE Clinical Investigation

(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations.

Abbreviated Molecular Breast Imaging: Feasibility and Future Considerations

OBJECTIVE

Molecular breast imaging (MBI) is a supplemental screening modality consistently demonstrating incremental cancer detection over mammography alone; however, its lengthy duration may limit widespread utilization. The study purpose was to assess feasibility of an abbreviated MBI protocol, providing readers with mediolateral oblique (MLO) projections only and assessing performance in lesion detection and localization.

METHODS

Retrospective IRB-exempt blinded reader study administered to 5 fellowship-trained breast imaging radiologists. Independent reads performed for 124 screening MBI cases, half abnormal and half negative/normal. Readers determined whether an abnormality was present, side of abnormality, and location of abnormality (medial/lateral). Abnormal cases had confirmatory biopsy or surgical pathology; normal cases had imaging follow-up ensuring true negative results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess performance. A false negative result indicated that a reader failed to detect abnormal uptake; a false positive result indicated a reader incorrectly called an abnormality for a negative case. Tests for association included chi-square, Fisher-exact, and analysis of variance.

RESULTS

Mean reader performance for detecting abnormal uptake: sensitivity 96.8%, specificity 98.7%, PPV 98.8%, and NPV 96.9%. Accuracy in localizing lesions to the medial or lateral breast was 100%. There were no associations in reader performance with reader experience, reader technique, lesion morphology, or lesion pathology. Median lesion size was 1.0 cm (range: 0.4-8.0 cm). All readers correctly identified 97.7% (42/43) of lesions with malignant or elevated risk pathology.

CONCLUSION

An abbreviated MBI protocol (MLO images only) maintained high accuracy in lesion detection and localization.

A comprehensive comparison of circulating tumor cells and breast imaging modalities as screening tools for breast cancer in Chinese women

Background

Circulating tumor cells (CTCs) have been recognized as a sensitive biomarker for breast cancer (BC). This study aimed to comprehensively compare CTC with imaging modalities, including ultrasonography, mammography, and contrast-enhanced magnetic resonance imaging (MRI) in screening for BC in Chinese women.

Methods

Three hundred forty-three participants were enrolled in this study, including 102 treatment-naive BC patients, 177 with breast benign diseases (BBD) and 64 healthy female patients. All participants underwent CTC testing and at least one of the following examinations, ultrasonography, mammography, and MRI at the Second Affiliated Hospital of Zhejiang University between December 2017 and November 2020. CTCs were quantitatively assessed using cell counting (CTC detection rate/counts) and categorically examined using a cutoff value (CTC classification). The diagnostic power of CTC tests and imaging modalities, including accuracy and capability to predict clinicopathological characteristics of BC, were evaluated and compared.

Results

CTC classification with a cutoff value of 2 showed a “good” diagnostic accuracy of 0.889 for early- to mid-stage BC comparable to breast imaging modalities using Breast Imaging-Reporting and Data System (BI-RADS). MRI demonstrated the highest sensitivity of 0.872 for BC, and CTC classification had the highest specificity of 0.938. A relatively low sensitivity was found for mammography in this cohort of patients. Successful detection of BC by CTC detection rate/counts, but not CTC classification, correlated with two important clinicopathological features, American Joint Committee on Cancer (AJCC) stage and tumor-node-metastasis (TNM) stage. The detection power of certain imaging modalities was also associated with AJCC stage (ultrasonography, p = 0.0438 and MRI, p = 0.0422) and lymph node metastasis (ultrasonography, 0.0157). There were clear correlations between CTC tests (counts or classification) and imaging BI-RADS scoring system in detecting positive BC cases (p < 0.05). Further correlation analysis suggested that CTC quantity, but not CTC classification, had the capability to predict clinicopathological traits of BC that were identified by ultrasonography.

Conclusions

CTC tests have a diagnostic potency comparable to breast imaging modalities, and may be used as an alternative screening tool for BC.

An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload

Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk, and suspicious mammograms in a screening sample to safely reduce radiologist workload and evaluate across Breast Imaging Reporting and Data System (BI-RADS) densities. Materials and Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January 2014 to December 2015 in the Danish Capital Region breast cancer screening program. All mammograms were scored from 0 to 10, representing the risk of malignancy, using an AI tool. During simulation, normal mammograms (score < 5) would be excluded from radiologist reading and suspicious mammograms (score > recall threshold [RT]) would be recalled. Two radiologists read the remaining mammograms. The RT was fitted using another independent cohort (same institution) by matching to the radiologist sensitivity. This protocol was further applied to each BI-RADS density. Screening outcomes were measured using the sensitivity, specificity, workload, and false-positive rate. The AI-based screening was tested for noninferiority sensitivity compared with radiologist screening using the Farrington-Manning test. Specificities were compared using the McNemar test. Results The study sample comprised 114 421 screenings for breast cancer in 114 421 women, resulting in 791 screen-detected, 327 interval, and 1473 long-term cancers and 2107 false-positive screenings. The mean age of the women was 59 years ± 6 (SD). The AI-based screening sensitivity was 69.7% (779 of 1118; 95% CI: 66.9, 72.4) and was noninferior (P = .02) to the radiologist screening sensitivity of 70.8% (791 of 1118; 95% CI: 68.0, 73.5). The AI-based screening specificity was 98.6% (111 725 of 113 303; 95% CI: 98.5, 98.7), which was higher (P < .001) than the radiologist specificity of 98.1% (111 196 of 113 303; 95% CI: 98.1, 98.2). The radiologist workload was reduced by 62.6% (71 585 of 114 421), and 25.1% (529 of 2107) of false-positive screenings were avoided. Screening results were consistent across BI-RADS densities, although not significantly so for sensitivity. Conclusion Artificial intelligence (AI)-based screening could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload. AI-based screening performed consistently across breast densities. © RSNA, 2022 Online supplemental material is available for this article.

The emerging role of photoacoustic imaging in clinical oncology

Clinical oncology can benefit substantially from imaging technologies that reveal physiological characteristics with multiscale observations. Complementing conventional imaging modalities, photoacoustic imaging (PAI) offers rapid imaging (for example, cross-sectional imaging in real time or whole-breast scanning in 10-15 s), scalably high levels of spatial resolution, safe operation and adaptable configurations. Most importantly, this novel imaging modality provides informative optical contrast that reveals details on anatomical, functional, molecular and histological features. In this Review, we describe the current state of development of PAI and the emerging roles of this technology in cancer screening, diagnosis and therapy. We comment on the performance of cutting-edge photoacoustic platforms, and discuss their clinical applications and utility in various clinical studies. Notably, the clinical translation of PAI is accelerating in the areas of macroscopic and mesoscopic imaging for patients with breast or skin cancers, as well as in microscopic imaging for histopathology. We also highlight the potential of future developments in technological capabilities and their clinical implications, which we anticipate will lead to PAI becoming a desirable and widely used imaging modality in oncological research and practice.

Feasibility study using multifocal Doppler twinkling artifacts to detect suspicious microcalcifications in ex vivo specimens of breast cancer on US

Multifocal Doppler twinkling artifact (MDTA) imaging has shown high detection rates of microcalcifications in phantom studies. We aimed to evaluate its performance in detecting suspicious microcalcifications in comparison with mammography by using ex vivo breast cancer specimens. We prospectively included ten women with breast cancer that presented with calcifications on mammography. Both digital specimen mammography and MDTA imaging were performed for ex vivo breast cancer specimens on the day of surgery. Five breast radiologists marked cells that included suspicious microcalcifications (referred to as 'positive cell') on specimen mammographic images using a grid of 5-mm cells. Cells that were marked by at least three readers were considered as 'consensus-positive'. Matched color Doppler twinkling artifact (CDTA) signals were compared between reconstructed US-MDTA projection images and mammographic images. The median detection rate for each case was 74.7% for positive cells and 96.7% for consensus-positive cells. Of the 10 cases, 90% showed a detection rate of ≥ 80%, with 50% of cases showing a 100% detection rate for consensus-positive cells. The proposed MDTA imaging method showed high performance for detecting suspicious microcalcifications in ex vivo breast cancer specimens, and may be a feasible approach for detecting suspicious breast microcalcifications with US.

Recent Development in Metallic Nanoparticles for Breast Cancer Therapy and Diagnosis

Metal-based nanoparticles are very promising for their applications in cancer diagnosis, drug delivery and therapy. Breast cancer is the major reason of death in woman especially in developed countries including EU and USA. Due to the heterogeneity of cancer cells, nanoparticles are effective as therapeutics and diagnostics. Anti-cancer therapy of breast tumors is challenging because of highly metastatic progression of the disease to brain, bone, lung, and liver. Magnetic nanoparticles are crucial for metastatic breast cancer detection and protection. This review comprehensively discusses the application of nanomaterials as breast cancer therapy, therapeutics, and diagnostics.

Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis

To investigate whether artificial-intelligence-based, computer-aided diagnosis (AI-CAD) could facilitate the detection of missed cancer on digital mammography, a total of 204 women diagnosed with breast cancer with diagnostic (present) and prior mammograms between 2018 and 2020 were included in this study. Two breast radiologists reviewed the mammographic features and classified them into true negative, minimal sign or missed cancer. They analyzed the AI-CAD results with an abnormality score and assessed whether the AI-CAD correctly localized the known cancer sites. Of the 204 cases, 137 were classified as true negative, 33 as minimal signs, and 34 as missed cancer. The sensitivity, specificity and diagnostic accuracy of AI-CAD were 84.7%, 91.5% and 86.3% on diagnostic mammogram and 67.2%, 91.2% and 83.38% on prior mammogram, respectively. The AI-CAD correctly localized 27 cases from 34 missed cancers on prior mammograms. The findings in the preceding mammography of AI-CAD-detected missed cancer were common in the order of calcifications, focal asymmetry and asymmetry. Asymmetry was the most common finding among the seven cases, which could not be detected by AI-CAD in the missed cases (5/7). The assistance of AI-CAD can be helpful in the early detection of breast cancer in mammography screenings.

The blood level of thioredoxin 1 as a supporting biomarker in the detection of breast cancer

BACKGROUND

There is a long-time unmet need for a means to detect breast cancer (BC) using blood. Although mammography is accepted as the gold standard for screening, a blood-based diagnostic can complement mammography and assist in the accurate detection of BC in the diagnostic process period of early diagnosis. We have previously reported the possible use of thioredoxin 1 (Trx1) in serum as a novel means to detect BC. In the present study, we validated the clinical utility of Trx1 to identify BC by testing sera from biopsy-confirmed cancer patients and women without cancer.

METHODS

We have generated monoclonal antibodies against Trx1 and developed an ELISA kit that can quantitate Trx1 in sera. The level of Trx1 was determined in each serum from women without cancer (n = 114), as well as in serum from patients with BC (n = 106) and other types of cancers (n = 74), including cervical, lung, stomach, colorectal, and thyroid cancer. The sera from BC patients were collected and classified by the subjects' age and cancer stage. In addition to the Trx1 levels of BC patients, several pathological and molecular aspects of BC were analyzed. Test results were retrospectively compared to those from mammography. Each test was duplicated, and test results were analyzed by ROC analysis, one-way ANOVA tests, and unpaired t-tests.

RESULTS

The mean level of Trx1 from women without cancer was 5.45 ± 4.16 (±SD) ng/ml, that of the other malignant cancer patient group was 2.70 ± 2.01 ng/ml, and that from the BC group was 21.96 ± 6.79 ng/ml. The difference among these values was large enough to distinguish BC sera from non-BC control sera with a sensitivity of 97.17% and specificity of 94.15% (AUC 0.990, p < 0.0001). Most Trx1 levels from BC patients' sera were higher than the cut-off value of 11.4 ng/ml regardless of age, stage, histological grade, type, and specific receptors' expression profile of BC. The level of Trx1 could rescue women from most cases of misread or incomplete mammography diagnoses.

CONCLUSION

These results indicated that the blood level of Trx1 could be an effective and accurate means to assist the detection of BC during the early diagnosis period.

Cone-Beam Breast Computed Tomography: Time for a New Paradigm in Breast Imaging

It is time to reconsider how we image the breast. Although the breast is a 3D structure, we have traditionally used 2D mammography to perform screening and diagnostic imaging. Mammography has been continuously modified and improved, most recently with tomosynthesis and contrast mammography, but it is still using modifications of compression 2D mammography. It is time to consider 3D imaging for this 3D structure. Cone-beam breast computed tomography (CBBCT) is a revolutionary modality that will assist in overcoming the limitations of current imaging for dense breast tissue and overlapping structures. It also allows easy administration of contrast material for functional imaging. With a radiation dose on par with diagnostic mammography, rapid 10 s acquisition, no breast compression, and true high-resolution isotropic imaging, CBBCT has the potential to usher in a new era in breast imaging. These advantages could translate into lower morbidity and mortality from breast cancer.

A Quantitative Framework to Study Potential Benefits and Harms of Multi-Cancer Early Detection Testing

BACKGROUND

Multi-cancer tests offer screening for multiple cancers with one blood draw, but the potential population impact is poorly understood.

METHODS

We formulate mathematical expressions for expected numbers of: (i) individuals exposed to unnecessary confirmation tests (EUC), (ii) cancers detected (CD), and (iii) lives saved (LS) given test performance, disease incidence and mortality, and mortality reduction. We add colorectal, liver, lung, ovary, and pancreatic cancer to a test for breast cancer, approximating prevalence at ages 50, 60, or 70 using incidence over the next 5 years and mortality using corresponding probabilities of cancer death over 15 years in the Surveillance, Epidemiology, and End Results registry.

RESULTS

EUC is overwhelmingly determined by specificity. For a given specificity, EUC/CD is most favorable for higher prevalence cancers. Under 99% specificity and sensitivities as published for a 50-cancer test, EUC/CD is 1.1 for breast + lung versus 1.3 for breast + liver at age 50. Under a common mortality reduction associated with screening, EUC/LS is most favorable when the test includes higher mortality cancers (e.g., 19.9 for breast + lung vs. 30.4 for breast + liver at age 50 assuming a common 10% mortality reduction).

CONCLUSIONS

Published multi-cancer test performance suggests a favorable tradeoff of EUC to CD, yet the full burden of unnecessary confirmations will depend on the posttest work-up protocol. Harm-benefit tradeoffs will be improved if tests prioritize more prevalent and/or lethal cancers for which curative treatments exist.

IMPACT

The population impact of multi-cancer testing will depend not only on test performance but also on disease characteristics and efficacy of early treatment.See related commentary by Stephen Duffy, p. xxx.

Invasive ductal carcinoma mammographic findings: Correlation with age, breast composition and tumor size

BACKGROUND/OBJECTIVE

The aim of this study was to identify the mammographic findings associated with malignancy in different age groups, taking into account breast composition (BC) and lesion size.

METHODS

Preoperative mammograms of 1023 invasive ductal carcinomas were retrospectively evaluated. According to the American College of Radiology Breast Imaging Reporting and Data System, cancer mammographic findings were classified as mass, calcifications, architectural distortion and asymmetry, and breasts were assessed as non-dense (A or B BC) and dense (C or D BC). Patient cohort was subdivided into three age groups (group 1: <50 years of age; group 2: between 50 and 69; group 3: ≥70 years of age).

RESULTS

Significant results of multinomial logistic regression were the association between mass and non-dense breast (p < 0.0001) and the association between mass and tumor size larger than 15 mm (p = 0.0049).

CONCLUSIONS

Mass finding of invasive ductal breast carcinoma is associated with breast composition and tumor size.

Effect of x-ray energy on the radiological image quality in propagation-based phase-contrast computed tomography of the breast

Purpose: Breast cancer is the most common cancer in women in developing and developed countries and is responsible for 15% of women's cancer deaths worldwide. Conventional absorption-based breast imaging techniques lack sufficient contrast for comprehensive diagnosis. Propagation-based phase-contrast computed tomography (PB-CT) is a developing technique that exploits a more contrast-sensitive property of x-rays: x-ray refraction. X-ray absorption, refraction, and contrast-to-noise in the corresponding images depend on the x-ray energy used, for the same/fixed radiation dose. The aim of this paper is to explore the relationship between x-ray energy and radiological image quality in PB-CT imaging. Approach: Thirty-nine mastectomy samples were scanned at the imaging and medical beamline at the Australian Synchrotron. Samples were scanned at various x-ray energies of 26, 28, 30, 32, 34, and 60 keV using a Hamamatsu Flat Panel detector at the same object-to-detector distance of 6 m and mean glandular dose of 4 mGy. A total of 132 image sets were produced for analysis. Seven observers rated PB-CT images against absorption-based CT (AB-CT) images of the same samples on a five-point scale. A visual grading characteristics (VGC) study was used to determine the difference in image quality. Results: PB-CT images produced at 28, 30, 32, and 34 keV x-ray energies demonstrated statistically significant higher image quality than reference AB-CT images. The optimum x-ray energy, 30 keV, displayed the largest area under the curve ( AUC VGC ) of 0.754 ( p = 0.009 ). This was followed by 32 keV ( AUC VGC = 0.731 , p ≤ 0.001 ), 34 keV ( AUC VGC = 0.723 , p ≤ 0.001 ), and 28 keV ( AUC VGC = 0.654 , p = 0.015 ). Conclusions: An optimum energy range (around 30 keV) in the PB-CT technique allows for higher image quality at a dose comparable to conventional mammographic techniques. This results in improved radiological image quality compared with conventional techniques, which may ultimately lead to higher diagnostic efficacy and a reduction in breast cancer mortalities.

Modeling viscous damping in actuated breast tissue to provide diagnostic insight for breast cancer: A proof-of-concept analysis

PURPOSE

This study develops a viscous damping model (VDM) based on Rayleigh Damping (RD) with potential use in low cost, non-invasive breast cancer diagnostics using Digital Image Elasto Tomography (DIET).

METHODS

A clinical trial involving 13 subjects, each with a tumor in one breast, resulted in 13 cancerous and 13 healthy breasts. Displacement data following actuator induced steady state vibration in the breast tissue were captured using the DIET system. Over 14 000 reference points on the breast surface were split into four segments and viscous damping constant calculated for each reference point. The VDM was fit to median-filtered data for each breast segment and VDM coefficients compared within each breast. One model coefficient, relating to stiffness, was hypothesized to differ in breast segments containing a tumor. Comparison of " b " coefficients in different breast segments using percentage tolerances provided an unbiased, generalizable diagnostic method. Bootstrapping with replacement was used to upsample the data and create smooth receiver operator characteristic (ROC) curves. A total of 12 breast segmentation configurations were used to demonstrate the robustness of the method.

RESULTS

Fitting the VDM to median-filtered data gave consistent results for one VDM coefficient (" a ") across all breasts. The second VDM coefficient (" b ") showed diagnostic potential with breast segments having consistent coefficients in healthy breasts. In cancerous breasts " b " coefficients were found to be statistically different in segments containing and adjacent to the tumor compared with the segment furthest from the tumor with p < 0.02 using the Student t-Test. Large discrepancies in " b " coefficients were found to be indicative of a tumor with a 14.5% tolerance resulting in sensitivity and specificity of 76.9%. The optimal breast configuration resulted in an area under the ROC curve (AUC) of 0.81 with sensitivity and specificity at 77% and 72%, respectively.

CONCLUSION

This VDM method enables a computationally simple diagnostic technique using DIET for comfortable breast screening for women of all ages. Regular screening potential allows for tolerance alteration based on age, prior subject-specific results, and other risk factors to manage false positives, reducing psychological harm while optimizing early detection for successful treatment and decreased mortality.

Enhancing the Screening Efficiency of Breast Cancer by Combining Conventional Medical Imaging Examinations With Circulating Tumor Cells

PURPOSE

Ultrasound (US) and mammogram (MMG) are the two most common breast cancer (BC) screening tools. This study aimed to assess how the combination of circulating tumor cells (CTC) with US and MMG would improve the diagnostic performance.

METHODS

CTC detection and imaging examinations, US and MMG, were performed in 238 treatment-naive BC patients, 217 patients with benign breast diseases (BBD), and 20 healthy females. Correlations of CTC, US and MMG with patients' clinicopathological characteristics were evaluated. Diagnostic performances of CTC, US and MMG were estimated by the receiver operating characteristic curves.

RESULTS

CTC, US and MMG could all distinguish BC patients from the control (p < 0.0001). Area under curve (AUC) of CTC, US and MMG are 0.855, 0.861 and 0.759, respectively. While US has the highest sensitivity of 0.79, CTC and MMG have the same specificity of 0.92. Notably, CTC has the highest accuracy of 0.83. Combination with CTC increases the AUC of US and MMG to 0.922 and 0.899, respectively. Combining MMG with CTC or US increases the sensitivity of MMG to 0.87, however "CTC + MMG" has a higher specificity of 0.85. "CTC + US" performs the best in BC diagnosis, followed by "CTC + MMG" and then "US + MMG".

CONCLUSION

CTC can be used as a diagnostic aid for BC screening. Combination with CTC increases the diagnostic potency of conventional BC screening imaging examinations, US and MMG, in BC diagnosis, especially for MMG.

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.

Characteristics of clinically node negative breast cancer patients needing preoperative MRI

BACKGROUND

International guidelines do not recommend magnetic resonance imaging (MRI) for all breast cancer patients at primary diagnostics. This study aimed to understand which patient or tumor characteristics are associated with the use of MRI. The role of MRI among other preoperative imaging methods in clinically node negative breast cancer was studied.

MATERIAL AND METHODS

Patient and tumor characteristics were analyzed in association with the use of MRI by multivariable logistic regression analysis in 461 patients. Primary tumor size was compared between MRI, mammography (MGR), ultrasound (US) and histopathology by Spearman correlation. The delays in surgery and diagnosis were analyzed among patients with or without MRI, and axillary reoperations were evaluated.

RESULTS

Age (p < 0.0001), primary operation method (p < 0.0001), tumor histology (p < 0.0001) and HER2 status (p = 0.0064) were associated with the use of MRI. Spearman correlations between tumor size in histopathology and the difference in tumor size between histopathology and imaging methods were 0.52 in MGR, 0.66 in US and 0.36 in MRI (p < 0.0001 for all). A seven-day delay in surgical treatment was observed among patients with MRI compared to patients without MRI (p < 0.0001). Axillary reoperation rates were similar in patients with or without MRI (p = 0.57).

CONCLUSION

Patient selection through prearranged characterization is important in deciding on optimal candidates for preoperative MRI among breast cancer patients. MRI causes moderate delays in primary breast cancer surgery. Preoperative MRI is useful in the evaluation of tumor size but might be insufficient in detecting lymph node metastases.

A Practical, Clinical User-Friendly Format for Breast Ultrasound Report

Objective

Breast ultrasound (BUS) is often performed as an adjunct to mammography in breast cancer screening or for evaluating breast lesions. Our aim was to design a practical and user-friendly format for BUS that could include the details of the Breast Imaging Reporting and Data System.

Materials and Methods

As a team of radiologists and surgeons trained in the management of breast diseases, we gathered and carried out the project in four phases-literature search and collection of present report formats, summarizing key points and preparing the first draft, seeking expert opinion and preparing the final format, and pilot testing-followed by a survey was answered by the research team's radiologists and surgeons.

Results

It produced a list of items to be stated in the BUS report, the final BUS report format, and the pilot format guide. Then, the radiologists used the format in three active ultrasound units in university-affiliated centers, and reports were referred to the surgeons. At the end of the project, the survey showed a high degree of ease of use, clarity, conciseness, comprehensiveness, and well-classified structure of the report format; but radiologists believed that the new organization took more time.

Conclusion

We propose our design as a user-friendly and practical format for BUS reports. It should be used for a longer time and by various ultrasound centers in order to ascertain its benefits.

Impact of specificity on cost-effectiveness of screening women at high risk of breast cancer with magnetic resonance imaging, mammography and ultrasound

PURPOSE

Aim of this study was to analyze the comparative cost-effectiveness of MR-mammography vs conventional imaging in a screening setting for women with high risk of breast cancer, with particular focus on the impact of specificity of MRM.

METHOD

Decision analytic modelling and Markov Modelling were applied to evaluate cumulative costs of each screening modality and their subsequent treatments as well as cumulative outcomes in quality adjusted life years (QALYs). For the selected time horizon of 30 years, false positive and false negative results were included. Model input parameters for women with high risk of breast cancer were estimated based on published data from a US healthcare system perspective. Major influence factors were identified and evaluated in a deterministic sensitivity analysis. Based on current recommendations for economic evaluations, a probabilistic sensitivity analysis was conducted to test the model stability.

RESULTS

In a base-case analysis, screening with XM vs. MRM and treatment resulted in overall costs of $36,201.57 vs. $39,050.97 and a cumulative effectiveness of 19.53 QALYs vs. 19.59 QALYs. This led to an incremental cost-effectiveness ratio (ICER) of $ 45,373.94 per QALY for MRM. US and XM + US resulted in ICER values higher than the willingness to pay (WTP). In the sensitivity analyses, MRM remained a cost-effective strategy for screening high-risk patients as long as the specificity of MRM did not drop below 86.7 %.

CONCLUSION

In high-risk breast cancer patients, MRM can be regarded as a cost-effective alternative to XM in a yearly screening setting. Specificity may be an important cost driver in settings with yearly screening intervals.

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.

The effect of breast density on the missed lesion rate in screening digital mammography determined using an adjustable-density breast phantom tailored to Japanese women

OBJECTIVE

Despite the high risk of missing lesions in mammography, the missed lesion rate is yet to be clinically established. Further, no breast phantoms with adjustable breast density currently exist. We developed a novel, adjustable-density breast phantom with a composition identical to that of actual breasts, and determined the quantitative relationship between breast density and the missed lesion rate in mammography.

METHODS

An original breast phantom consisting of adipose- and fibroglandular-equivalent materials was developed, and a receiver operating characteristic (ROC) study was performed. Breast density, which is the fraction by weight of fibroglandular to total tissue, was adjusted to 25%, 50%, and 75% by arbitrarily mixing the two materials. Microcalcification, mass lesions, and spiculated lesions, each with unique characteristics, were inserted into the phantom. For the above-mentioned fibroglandular densities, 50 positive and 50 negative images for each lesion type were used as case samples for the ROC study. Five certified radiological technologists participated in lesion detection.

RESULTS

The mass-lesion detection rate, according to the area under the curve, decreased by 18.0% (p = 0.0001, 95% Confidence intervals [CI] = 0.1258 to 0.1822) and 37.8% (p = 0.0003, 95% CI = 0.2453 to 0.4031) for breast densities of 50% and 75%, respectively, compared to that for a 25% breast density. A similar tendency was observed with microcalcification; however, spiculated lesions did not follow this tendency.

CONCLUSIONS

We quantified the missed lesion rate in different densities of breast tissue using a novel breast phantom, which is imperative for advancing individualized screening mammography.

Comparative study between contrast-enhanced mammography, tomosynthesis, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma

Background

Mammography is accused of having low sensitivity and specificity in dense breast parenchyma. Also, women with dense breasts show an increased risk of developing breast cancer. Breast ultrasound has been used for several years for a better characterization of breast lesions. Contrast-enhanced mammography and tomosynthesis are relative novel imaging techniques that have been implicated in breast cancer detection and diagnosis. We aimed to compare breast tomosynthesis, contrast-enhanced mammography, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma.

Results

The study included 37 patients with 63 inconclusive mammography breast lesions. They all performed contrast-enhanced mammography, single-view tomosynthesis, and breast ultrasound. Mammography had a sensitivity of 83%, a specificity of 48%, a positive predictive value of 68%, a negative predictive value of 68%, and a diagnostic accuracy of 68%. Contrast-enhanced mammography had a sensitivity of 89%, a specificity of 89%, a positive predictive value of 91%, a negative predictive value of 86%, and a diagnostic accuracy of 89%. Tomosynthesis had a sensitivity of 86%, a specificity of 81%, a positive predictive value of 86%, a negative predictive value of 81%, and a diagnostic accuracy of 84%. Breast ultrasound had a sensitivity of 97%, a specificity of 85%, a positive predictive value of 90%, a negative predictive value of 96%, and a diagnostic accuracy of 92%.

Conclusion

Breast ultrasound, tomosynthesis, and contrast-enhanced mammography showed better performance compared to mammography in dense breasts. However, ultrasound being safe with no radiation hazards should be the second step modality of choice after mammography in the assessment of mammography dense breasts. Adding tomosynthesis to mammography in screening increases its sensitivity. Contrast-enhanced mammography should be reserved for cases with inconclusive sonomammographic results.

Breast cancer mortality and overdiagnosis after implementation of population-based screening in Denmark

INTRODUCTION

Service breast cancer screening is difficult to evaluate because there is no unscreened control group. Due to a natural experiment, where 20% of women were offered screening in two regions up to 17 years before other women, Denmark is in a unique position. We utilized this opportunity to assess outcome of service screening.

MATERIALS AND METHODS

Screening was offered in Copenhagen from 1991 and Funen from 1993 to women aged 50-69 years. We used difference-in-differences methodology with a study group offered screening; a historical control group; a regional control group; and a regional-historical control group, comparing breast cancer mortality and incidence, including ductal carcinoma in situ, between study and historical control group adjusted for changes in other regions, and calculating ratios of rate ratios (RRR) with 95% confidence intervals (CI). Data came from Central Population Register; mammography screening databases; Cause of Death Register; and Danish Cancer Register.

RESULTS

For breast cancer mortality, the study group accumulated 1,551,465 person-years and 911 deaths. Long-term breast cancer mortality in Copenhagen was 20% below expected in absence of screening; RRR 0.80 (95% CI 0.71-0.90), and in Funen 22% below; RRR 0.78 (95% CI 0.68-0.89). Combined, cumulative breast cancer incidence in women followed 8+ years post-screening was 2.3% above expected in absence of screening; RRR 1.023 (95% CI 0.97-1.08).

DISCUSSION

Benefit-to-harm ratio of the two Danish screening programs was 2.6 saved breast cancer deaths per overdiagnosed case. Screening can affect only breast cancers diagnosed in screening age. Due to high breast cancer incidence after age 70, only one-third of breast cancer deaths after age 50 could potentially be affected by screening. Increasing upper age limit could be considered, but might affect benefit-to-harm ratio negatively.

Use of Palpation Imaging in Diagnosis of Breast Diseases: A Way to Improve the Detection Rate

Background

Breast diseases pose increasing threat to women health as peoples lifestyle changes. The aim of this study was to investigate the clinical application value of Palpation Imaging (PI) in the diagnosis of breast diseases.

Material/Methods

From October 2019 to February 2020, 184 patients with 225 breast lesions were examined by using PI, ultrasound, and mammography in the department of Breast Surgery, the First Affiliated Hospital of Anhui Medical University. All cases were confirmed pathologically by core-needle biopsy or excisional biopsy. The cut-off value of the PI tests was determined by receiver operating characteristic (ROC) curve. We compared the examination results of PI with ultrasound and mammography to analyze the diagnostic value of PI.

Results

Pathological examination revealed that 186/225(82.67%) lesions were benign, while 39 were malignant. All 8 parameters of PI were significantly correlated with pathological findings (P<0.05). The best cut-off value for the PI score was 19.5 and the area under the curve (AUC) for the PI was 0.921 (95% CI: 0.874–0.968, P<0.001) with 89.7% sensitivity and 86.0% specificity. PI showed greater sensitivity (89.7%) and its specificity (86.0% vs. 86.4%, P=0.931) and accuracy (86.7% vs. 84.6%, P=0.604) were similar to those of mammography. The combination of 3 types of test is superior to a single examination. The sensitivity was 100% and the specificity was 98.8%.

Conclusions

PI has high clinical value in differentiation of benign and malignant breast lesions. Combination examination has the potential to improve the detection of breast cancer in screening and diagnostic capacities and can be used as a supplement to ultrasound and mammography.

Frequency and diagnostic outcome of bilateral recall at screening mammography

Our study was performed to determine the frequency of recall for bilateral breast lesions at screening mammography and compare its outcome with respect to unilateral recall. We included 329 132 screening mammograms (34 889 initial screens and 294 243 subsequent screens) from a Dutch screening mammography program between January 2013 and January 2018. During a 2‐year follow‐up, we collected radiological data, pathology reports and surgical reports of all recalled women. At bilateral recall, the lesion with the highest Breast Imaging Reporting and Data System score was used as the index lesion when comparing screening mammography characteristics at bilateral vs unilateral recall. A total of 9806 women were recalled at screening (recall rate, 3.0%). Bilateral recall comprised 2.8% (271/9806) of all recalls. Biopsy was more frequently performed after bilateral recall than unilateral recall (54.6% [148/271] vs 44.1% [4201/9535], P < .001), yielding a lower positive predictive value (PPV) of biopsy after bilateral recall (42.6% vs 51.7%, P = .029). The PPV of recall was comparable for both groups (23.2% [63/271] vs 22.8% [2173/9535], P = .85). Invasive cancers after bilateral recall were larger than those diagnosed after unilateral recall (P = .02), but histological subtype, histologic grading, receptor status and proportions of lymph node positive cancers were comparable. Bilateral recall infrequently occurs at screening mammography. Biopsy is more frequently performed following bilateral recall, but the PPV of recall is similar for unilateral and bilateral recall. Invasive cancers of both groups show comparable pathological features except of a larger tumor size after bilateral recall.

What's new?

Data on bilateral breast cancer in a screened population is sparse, and information on bilateral recall is lacking. Based on more than 329,000 screening mammograms, our study shows that bilateral recall occurs infrequently at screening mammography, and that the majority of these recalls are false positives. Invasive cancer has comparable pathological features in bilateral and unilateral breast cancer patients, except larger tumour size after bilateral recall. Altogether, the results highlight the need for screening radiologists to pay vigorous attention to the contralateral breast after detecting a screening mammographic abnormality in order to facilitate a timely diagnosis of bilateral breast cancer.