A multi-centre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women

A Prospective Multicenter Study on the Additive Value of Contrast-Enhanced Ultrasound for Biopsy Decision of Ultrasound BI-RADS 4 Breast Lesions

OBJECTIVE

The main aim of this study was to determine whether the use of contrast-enhanced ultrasound (CEUS) could improve the categorization of suspicious breast lesions based on the Breast Imaging Reporting and Data System (BI-RADS), thereby reducing the number of benign breast lesions referred for biopsy.

METHODS

This prospective study, conducted between January 2017 and December 2018, enrolled consenting patients from eight teaching hospitals in China, who had been diagnosed with solid breast lesions classified as BI-RADS 4 using conventional ultrasound. CEUS was performed within 1 wk of diagnosis for reclassification of breast lesions. Histopathological results obtained from core needle biopsies or surgical excision samples served as the reference standard. The simulated biopsy rate and cancer-to-biopsy yield were used to compare the accuracy of CEUS and conventional ultrasound (US).

RESULTS

Among the 1490 lesions diagnosed as BI-RADS 4 with conventional ultrasound, 486 malignant and 1004 benign lesions were confirmed based on histology. Following CEUS, 2, 395, and 211 lesions were reclassified as CEUS-based BI-RADS 2, 3, and 5, respectively, while 882 (59%) remained as BI-RADS 4. The actual cancer-to-biopsy yield based on US was 32.6%, which increased to 43.4% when CEUS-based BI-RADS 4A was used as the cut-off point to recommend biopsy. The simulated biopsy rate decreased to 73.4%. Overall, in this preselected BI-RADS 4 population, only 2.5% (12/486) of malignant lesions would have been miscategorized as BI-RADS 3 using CEUS-based reclassification. The diagnostic accuracy, sensitivity, and specificity of contrast-enhanced ultrasound reclassification were 57.65%, 97.53%, and 38.35%, respectively.

CONCLUSION

Our collective findings indicate that CEUS is a valuable tool in further triage of BI-RADS category 4 lesions and facilitates a reduction in the number of biopsies while increasing the cancer-to-biopsy yield.

Evaluation of large language models in breast cancer clinical scenarios: a comparative analysis based on ChatGPT-3.5, ChatGPT-4.0, and Claude2

BACKGROUND

Large language models (LLMs) have garnered significant attention in the AI domain owing to their exemplary context recognition and response capabilities. However, the potential of LLMs in specific clinical scenarios, particularly in breast cancer diagnosis, treatment, and care, has not been fully explored. This study aimed to compare the performances of three major LLMs in the clinical context of breast cancer.

METHODS

In this study, clinical scenarios designed specifically for breast cancer were segmented into five pivotal domains (nine cases): assessment and diagnosis, treatment decision-making, postoperative care, psychosocial support, and prognosis and rehabilitation. The LLMs were used to generate feedback for various queries related to these domains. For each scenario, a panel of five breast cancer specialists, each with over a decade of experience, evaluated the feedback from LLMs. They assessed feedback concerning LLMs in terms of their quality, relevance, and applicability.

RESULTS

There was a moderate level of agreement among the raters (Fleiss' kappa=0.345, P<0.05). Comparing the performance of different models regarding response length, GPT-4.0 and GPT-3.5 provided relatively longer feedback than Claude2. Furthermore, across the nine case analyses, GPT-4.0 significantly outperformed the other two models in average quality, relevance, and applicability. Within the five clinical areas, GPT-4.0 markedly surpassed GPT-3.5 in the quality of the other four areas and scored higher than Claude2 in tasks related to psychosocial support and treatment decision-making.

CONCLUSION

This study revealed that in the realm of clinical applications for breast cancer, GPT-4.0 showcases not only superiority in terms of quality and relevance but also demonstrates exceptional capability in applicability, especially when compared to GPT-3.5. Relative to Claude2, GPT-4.0 holds advantages in specific domains. With the expanding use of LLMs in the clinical field, ongoing optimization and rigorous accuracy assessments are paramount.

Association between sociodemographic factors and health beliefs related to breast cancer screening behavior among Northern Thai women: a hospital-based study

Early diagnosis of breast cancer is crucial for reducing mortality rates. The purpose of this study is to determine the impact of demographics/social determinants of health on beliefs about the practice of self-breast examination, using mammogram and ultrasound in the context of breast cancer screening among Thai women in a hospital-based setting for implying program planning and future research. A cross-sectional study was conducted in two health centers in Chiang Mai Province from August 2021 to December 2021, involving 130 Thai women ages 40 to 70 years. Data were collected by a survey using a questionnaire to gather sociodemographic information, and health beliefs about breast cancer and screening behavior utilizing the modified Thai version of Champion's Health Belief Model Scale (MT-CHBMS). Descriptive statistics, t-tests, ANOVA, and linear regression models were employed for examining association between sociodemographic factors and health beliefs about the practice of self-breast examination (BSE), using mammogram (MG) and ultrasound (UTS). Health insurance schemes were associated with Benefit-MG, Barrier-BSE, Barrier-MG and Barrier-UTS subscales. Additionally, monthly income was associated with Barrier-MG and Barrier-UTS subscales. The most common barriers reported were "embarrassment", "worry", and "takes too much time". To enhance breast cancer screening in Thailand, program planning and future research should focus on health insurance schemes, especially women with social security schemes, as they may be the most appropriate target group for intervention.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

OBJECTIVE

To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment.

METHODS

Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established.

RESULTS

A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867.

CONCLUSION

Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women

BACKGROUND

The incidence of breast cancer among Chinese women has gradually increased in recent years. This study aims to analyze the situation of breast cancer screening programs in China and compare the cancer detection rates (CDRs), early-stage cancer detection rates (ECDRs), and the proportions of early-stage cancer among different programs.

METHODS

We conducted a systematic review and meta-analysis of studies in multiple literature databases. Studies that were published between January 1, 2010 and June 30, 2023 were retrieved. A random effects model was employed to pool the single group rate, and subgroup analyses were carried out based on screening model, time, process, age, population, and follow-up method.

RESULTS

A total of 35 studies, including 47 databases, satisfied the inclusion criteria. Compared with opportunistic screening, the CDR (1.32‰, 95% CI: 1.10‰-1.56‰) and the ECDR (0.82‰, 95% CI: 0.66‰-0.99‰) were lower for population screening, but the proportion of early-stage breast cancer (80.17%, 95% CI: 71.40%-87.83%) was higher. In subgroup analysis, the CDR of population screening was higher in the urban group (2.28‰, 95% CI: 1.70‰-2.94‰), in the breast ultrasonography (BUS) in parallel with mammography (MAM) group (3.29‰, 95% CI: 2.48‰-4.21‰), and in the second screening follow-up group (2.47‰, 95% CI: 1.64‰-3.47‰), and the proportion of early-stage breast cancer was 85.70% (95% CI: 68.73%-97.29%), 88.18% (95% CI: 84.53%-91.46%), and 90.05% (95% CI: 84.07%-94.95%), respectively.

CONCLUSION

There were significant differences between opportunistic and population screening programs. The results of these population screening studies were influenced by the screening process, age, population, and follow-up method. In the future, China should carry out more high-quality and systematic population-based screening programs to improve screening coverage and service.

Ultrasound characteristics of breast fibromatosis mimicking carcinoma

PURPOSE

To explore the value of ultrasound (US) characteristics in diagnosing breast fibromatosis (BF) and evaluate their differences from breast carcinoma.

METHODS

A total of 121 patients with BF (n = 24, 29 lesions) or invasive ductal carcinoma (IDC) (n = 97, 102 lesions) of the breast were included. Their clinical and US findings were recorded and analyzed.

RESULTS

The mean age of BF was younger than that of IDC (28.75 ± 5.55 vs. 50.19 ± 9.87, p < 0.001). The mean size of the BF was smaller than that of IDC (2.09 ± 0.91 vs. 2.71 ± 1.20, p = 0.011). Compared to IDC, BF had more frequency of posterior echo attenuation (p < 0.001), less frequency of peripheral hyperechoic halo (p = 0.002), calcification (p = 0.001), US reported axillary lymph node positive (p = 0.025), and grade 2-3 vascularity (p < 0.001). The Breast Imaging Reporting and Data System categorized BF at a lower level than IDC (p < 0.001). After adjusting for age, the peripheral hyperechoic halo, posterior echo feature, and vascularity could independently identify the differences between these two entities.

CONCLUSION

Some differences were observed between BF and IDC in terms of patient age, lesion size, and US characteristics.

A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer

Objective

To compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.

Methods

Published studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan's nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.

Results

A total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73-0.95)], specificity was 0.93 [95% CI (0.82-0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94-0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84-0.91)], specificity was 0.76 [95% CI (0.66-0.84)], AUC was 0.89 [95% CI (0.86-0.92)] and DOR was 24. The Deeks' funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.

Conclusion

Compared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.

Cancer screening in China: a steep road from evidence to implementation

Cancer screening has the potential to decrease mortality from several common cancer types. The first cancer screening programme in China was initiated in 1958 and the Cancer High Incidence Fields established in the 1970s have provided an extensive source of information for national cancer screening programmes. From 2012 onwards, four ongoing national cancer screening programmes have targeted eight cancer types: cervical, breast, colorectal, lung, oesophageal, stomach, liver, and nasopharyngeal cancers. By synthesising evidence from pilot screening programmes and population-based studies for various screening tests, China has developed a series of cancer screening guidelines. Nevertheless, challenges remain for the implementation of a fully successful population-based programme. The aim of this Review is to highlight the key milestones and the current status of cancer screening in China, describe what has been achieved to date, and identify the barriers in transitioning from evidence to implementation. We also make a set of implementation recommendations on the basis of the Chinese experience, which might be useful in the establishment of cancer screening programmes in other countries.

Impacts of Sociodemographic Factors, Screening, and Organization of Health Services on Breast Cancer Mortality in Brazil: An Ecological Study of 20 Years

Background

Breast cancer mortality is increasing in Brazil. This study examines the impact of sociodemographic factors, screening procedures, and primary healthcare (PHC) on breast cancer mortality.

Methods

An ecological study analyzed secondary data of women diagnosed with breast cancer who died between 2000 and 2019. Sociodemographic factors, screening procedures, and PHC were examined in relation to breast cancer mortality. Statistical analyses included normality tests, Kruskal-Wallis and one-way ANOVA tests with post hoc comparisons, Pearson and Spearman correlation tests, age-period-cohort analysis, Kaplan-Meier analysis, and Cox regression analysis. Significance was set at p < 0.05.

Results

Mortality rates were higher in the southeast (15.77) and south (15.97) regions compared to the north (5.07) (p < 0.0001). Survival rates were longer in the southeast (70.3 ± 0.05) and south (70.6 ± 0.09) than in the north (63.98 ± 0.053) (p ≤ 0.001). Mortality increased with age after 32 years (p ≤ 0.001). Brown and indigenous women had lower mortality and survival rates. Increased coverage of PHC, ultrasound, and biopsy did not reduce mortality. However, improved cytopathologic analysis led to a decrease in mortality.

Conclusions

Sociodemographic factors, screening procedures, and PHC are specific predictors of breast cancer mortality in Brazil.

Breast cancer pre-clinical screening using infrared thermography and artificial intelligence: a prospective, multicentre, diagnostic accuracy cohort study

BACKGROUND

Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening.

MATERIALS AND METHODS

This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital.

RESULTS

The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals.

CONCLUSIONS

The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.

Estimating disparities in breast cancer screening programs towards mortality, case fatality, and DALYs across BRICS-plus

BACKGROUND

Numerous studies over the past four decades have revealed that breast cancer screening (BCS) significantly reduces breast cancer (BC) mortality. However, in BRICS-plus countries, the association between BCS and BC case fatality and disability are unknown. This study examines the association of different BCS approaches with age-standardized mortality, case-fatality, and disability-adjusted life years (DALYs) rates, as well as with other biological and sociodemographic risk variables, across BRICS-plus from a national and economic perspective.

METHODS

In this ecological study applying mixed-effect multilevel regression models, a country-specific dataset was analyzed by combining data from the Global Burden of Disease study 2019 on female age-standardized BC mortality, incidence, and DALYs rates with information on national/regional BCS availability (against no such program or only a pilot program) and BCS type (only self-breast examination (SBE) and/or clinical breast examination (CBE) [SBE/CBE] versus SBE/CBE with mammographic screening availability [MM and/or SBE/CBE] versus SBE/CBE/mammographic with digital mammography and/or ultrasound (US) [DMM/US and/or previous tests] in BRICS-plus countries.

RESULTS

Compared to self/clinical breast examinations (SBE/CBE) across BRICS-plus, more complex BCS program availability was the most significant predictor of decreased mortality [MM and/or SBE/CBE: - 2.64, p < 0.001; DMM/US and/or previous tests: - 1.40, p < 0.001]. In the BRICS-plus, CVD presence, high BMI, second-hand smoke, and active smoking all contributed to an increase in BC mortality and DALY rate. High-income and middle-income regions in BRICS-plus had significantly lower age-standardized BC mortality, case-fatality, and DALYs rates than low-income regions when nationwide BC screening programs were implemented.

CONCLUSIONS

The availability of mammography (digital or traditional) and BCS is associated with breast cancer burden in BRICS-plus countries, with regional variations. In light of high-quality evidence from previous causal studies, these findings further support the preventive role of mammography screening for BCS at the national level. Intervening on BCS related risk factors may further reduce the disease burden associated with BC.

Epidemiology and clinicopathologic features of breast cancer in China and the United States

Background

Breast cancer has kept increasing since the past decades and the incidence rate is the highest among all neoplasms nowadays. China, as well as other countries, faces severe burden from the increasing population with breast cancer. This study aimed to analyze the epidemiology and clinicopathologic features of breast cancer in China and the United States (US).

Methods

Data of hospitalized patients diagnosed with primary breast cancer between 1 January 1999 and 31 December 2014 in the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) were reviewed. Clinical and demographic data were extracted from medical history systems, and the sixteen-year trends were analyzed. Meanwhile, retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database from 1999 to 2014 were used for comparisons.

Results

A total of 18,768 breast cancer patients were included from CHCAMS, China, with 18,685 female cases (99.57%) and 81 male cases (0.43%). A total of 762,954 breast cancer patients were included from the SEER database, US, with 757,357 female cases (99.27%) and 5,597 male cases (0.73%). The peak age of breast cancer was 45-49 years old from 1999 to 2014 in China, while the peak age was 55-59 years from 1999 to 2006 and 60-64 years from 2007 to 2014 in the US. There were more young (<35 years, 6.56% vs. 1.97%, P<0.001), less elderly (≥65 years, 9.99% vs. 40.88%, P<0.001), less stage I (24.93% vs. 48.84%, P<0.001) and more stage III (21.00% vs. 12.35%, P<0.001) breast cancer patients in China than in the US. Patients aged 30-49 years old had a decreased trend (P<0.001), while 55-64 years old patients had an increased trend (P<0.001) from 1999 to 2014 in China, the same trend was also observed in the US. Mucinous carcinoma and histological grade I breast cancer patients increased with age both in China and the US (P<0.001).

Conclusions

The unique epidemiology and clinicopathologic features of breast cancer (earlier peak age, more younger patients, more advanced stage, etc.), as well as the typical trend in China, should be seriously recognized, so as to guide future prevention and management strategies.

A Diagnostic Model for Breast Lesions With Enlarged Enhancement Extent on Contrast-Enhanced Ultrasound Improves Malignancy Prediction

OBJECTIVE

The aim of the work described here was to develop a diagnostic model based on contrast-enhanced ultrasound (CEUS) features to improve performance in predicting the probability of malignancy for breast lesions with an enlarged enhancement extent on CEUS.

METHODS

In total, 299 consecutive patients who underwent CEUS examination and had confirmed pathological results were retrospectively enrolled. Among the 299 patients, an enlarged enhancement extent on CEUS was found in 142 patients. In this special cohort, we analyzed the association of malignant pathologic results with perfusion patterns emphatically by reclassifying the patterns.

RESULTS

A diagnostic model was developed and presented as a nomogram, assessed with discrimination and calibration. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of the conventional perfusion and modified perfusion patterns were 0.58 and 0.76 (p < 0.001), respectively. A diagnostic model was built and exhibited good discrimination with a C-index of 0.95 (95% confidence interval: 0.91-0.98), which was confirmed to be 0.93 via internal bootstrapping validation.

CONCLUSION

The nomogram based on CEUS features provides radiologists with a quantitative tool to predict the probability of malignancy in this special cohort of breast lesions.

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study

Background

Early diagnosis of breast cancer has always been a difficult clinical challenge. We developed a deep-learning model EDL-BC to discriminate early breast cancer with ultrasound (US) benign findings. This study aimed to investigate how the EDL-BC model could help radiologists improve the detection rate of early breast cancer while reducing misdiagnosis.

Methods

In this retrospective, multicentre cohort study, we developed an ensemble deep learning model called EDL-BC based on deep convolutional neural networks. The EDL-BC model was trained and internally validated on B-mode and color Doppler US image of 7955 lesions from 6795 patients between January 1, 2015 and December 31, 2021 in the First Affiliated Hospital of Army Medical University (SW), Chongqing, China. The model was assessed by internal and external validations, and outperformed radiologists. The model performance was validated in two independent external validation cohorts included 448 lesions from 391 patients between January 1 to December 31, 2021 in the Tangshan People's Hospital (TS), Chongqing, China, and 245 lesions from 235 patients between January 1 to December 31, 2021 in the Dazu People's Hospital (DZ), Chongqing, China. All lesions in the training and total validation cohort were US benign findings during screening and biopsy-confirmed malignant, benign, and benign with 3-year follow-up records. Six radiologists performed the clinical diagnostic performance of EDL-BC, and six radiologists independently reviewed the retrospective datasets on a web-based rating platform.

Findings

The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and two independent external validation cohorts for EDL-BC was 0.950 (95% confidence interval [CI]: 0.909-0.969), 0.956 (95% [CI]: 0.939-0.971), and 0.907 (95% [CI]: 0.877-0.938), respectively. The sensitivity values were 94.4% (95% [CI]: 72.7%-99.9%), 100% (95% [CI]: 69.2%-100%), and 80% (95% [CI]: 28.4%-99.5%), respectively, at 0.76. The AUC for accurate diagnosis of EDL-BC (0.945 [95% [CI]: 0.933-0.965]) and radiologists with artificial intelligence (AI) assistance (0.899 [95% [CI]: 0.883-0.913]) was significantly higher than that of the radiologists without AI assistance (0.716 [95% [CI]: 0.693-0.738]; p < 0.0001). Furthermore, there were no significant differences between the EDL-BC model and radiologists with AI assistance (p = 0.099).

Interpretation

EDL-BC can identify subtle but informative elements on US images of breast lesions and can significantly improve radiologists' diagnostic performance for identifying patients with early breast cancer and benefiting the clinical practice.

Funding

The National Key R&D Program of China.

Efficiency and impact factors of anatomical intelligence for breast and hand-held ultrasound in lesion detection

PURPOSE

To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection.

METHODS

A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed.

RESULTS

The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05).

CONCLUSIONS

With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.

Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions

Breast cancer (BC) is the most prevalent cancer among women globally. Cancer screening can reduce mortality and improve women’s health. In developed countries, mammography (MAM) has been primarily utilized for population-based BC screening for several decades. However, it is usually unavailable in low-resource settings due to the lack of equipment, personnel, and time necessary to conduct and interpret the examinations. Ultrasound (US) with high detection sensitivity for women of younger ages and with dense breasts has become a supplement to MAM for breast examination. Some guidelines suggest using US as the primary screening tool in certain settings where MAM is unavailable and infeasible, but global recommendations have not yet reached a unanimous consensus. With the development of smart devices and artificial intelligence (AI) in medical imaging, clinical applications and preclinical studies have shown the potential of US combined with AI in BC screening. Nevertheless, there are few comprehensive reviews focused on the role of US in screening BC in underserved conditions, especially in technological, economical, and global perspectives. This work presents the benefits, limitations, advances, and future directions of BC screening with technology-assisted and resource-appropriate strategies, which may be helpful to implement screening initiatives in resource-limited countries.

Mammography in combination with breast ultrasonography versus mammography for breast cancer screening in women at average risk

BACKGROUND

Screening mammography can detect breast cancer at an early stage. Supporters of adding ultrasonography to the screening regimen consider it a safe and inexpensive approach to reduce false-negative rates during screening. However, those opposed to it argue that performing supplemental ultrasonography will also increase the rate of false-positive findings and can lead to unnecessary biopsies and treatments.

OBJECTIVES

To assess the comparative effectiveness and safety of mammography in combination with breast ultrasonography versus mammography alone for breast cancer screening for women at average risk of breast cancer.

SEARCH METHODS

We searched the Cochrane Breast Cancer Group's Specialised Register, CENTRAL, MEDLINE, Embase, the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), and ClinicalTrials.gov up until 3 May 2021.

SELECTION CRITERIA

For efficacy and harms, we considered randomised controlled trials (RCTs) and controlled non-randomised studies enrolling at least 500 women at average risk for breast cancer between the ages of 40 and 75. We also included studies where 80% of the population met our age and breast cancer risk inclusion criteria.

DATA COLLECTION AND ANALYSIS

Two review authors screened abstracts and full texts, assessed risk of bias, and applied the GRADE approach. We calculated the risk ratio (RR) with 95% confidence intervals (CI) based on available event rates. We conducted a random-effects meta-analysis.

MAIN RESULTS

We included eight studies: one RCT, two prospective cohort studies, and five retrospective cohort studies, enrolling 209,207 women with a follow-up duration from one to three years. The proportion of women with dense breasts ranged from 48% to 100%. Five studies used digital mammography; one study used breast tomosynthesis; and two studies used automated breast ultrasonography (ABUS) in addition to mammography screening. One study used digital mammography alone or in combination with breast tomosynthesis and ABUS or handheld ultrasonography. Six of the eight studies evaluated the rate of cancer cases detected after one screening round, whilst two studies screened women once, twice, or more. None of the studies assessed whether mammography screening in combination with ultrasonography led to lower mortality from breast cancer or all-cause mortality. High certainty evidence from one trial showed that screening with a combination of mammography and ultrasonography detects more breast cancer than mammography alone. The J-START (Japan Strategic Anti-cancer Randomised Trial), enrolling 72,717 asymptomatic women, had a low risk of bias and found that two additional breast cancers per 1000 women were detected over two years with one additional ultrasonography than with mammography alone (5 versus 3 per 1000; RR 1.54, 95% CI 1.22 to 1.94). Low certainty evidence showed that the percentage of invasive tumours was similar, with no statistically significant difference between the two groups (69.6% (128 of 184) versus 73.5% (86 of 117); RR 0.95, 95% CI 0.82 to 1.09). However, positive lymph node status was detected less frequently in women with invasive cancer who underwent mammography screening in combination with ultrasonography than in women who underwent mammography alone (18% (23 of 128) versus 34% (29 of 86); RR 0.53, 95% CI 0.33 to 0.86; moderate certainty evidence). Further, interval carcinomas occurred less frequently in the group screened by mammography and ultrasonography compared with mammography alone (5 versus 10 in 10,000 women; RR 0.50, 95% CI 0.29 to 0.89; 72,717 participants; high certainty evidence). False-negative results were less common when ultrasonography was used in addition to mammography than with mammography alone: 9% (18 of 202) versus 23% (35 of 152; RR 0.39, 95% CI 0.23 to 0.66; moderate certainty evidence). However, the number of false-positive results and necessary biopsies were higher in the group with additional ultrasonography screening. Amongst 1000 women who do not have cancer, 37 more received a false-positive result when they participated in screening with a combination of mammography and ultrasonography than with mammography alone (RR 1.43, 95% CI 1.37 to 1.50; high certainty evidence). Compared to mammography alone, for every 1000 women participating in screening with a combination of mammography and ultrasonography, 27 more women will have a biopsy (RR 2.49, 95% CI 2.28 to 2.72; high certainty evidence). Results from cohort studies with methodological limitations confirmed these findings. A secondary analysis of the J-START provided results from 19,213 women with dense and non-dense breasts. In women with dense breasts, the combination of mammography and ultrasonography detected three more cancer cases (0 fewer to 7 more) per 1000 women screened than mammography alone (RR 1.65, 95% CI 1.0 to 2.72; 11,390 participants; high certainty evidence). A meta-analysis of three cohort studies with data from 50,327 women with dense breasts supported this finding, showing that mammography and ultrasonography combined led to statistically significantly more diagnosed cancer cases compared to mammography alone (RR 1.78, 95% CI 1.23 to 2.56; 50,327 participants; moderate certainty evidence). For women with non-dense breasts, the secondary analysis of the J-START study demonstrated that more cancer cases were detected when adding ultrasound to mammography screening compared to mammography alone (RR 1.93, 95% CI 1.01 to 3.68; 7823 participants; moderate certainty evidence), whilst two cohort studies with data from 40,636 women found no statistically significant difference between the two screening methods (RR 1.13, 95% CI 0.85 to 1.49; low certainty evidence).

AUTHORS' CONCLUSIONS

Based on one study in women at average risk of breast cancer, ultrasonography in addition to mammography leads to more screening-detected breast cancer cases. For women with dense breasts, cohort studies more in line with real-life clinical practice confirmed this finding, whilst cohort studies for women with non-dense breasts showed no statistically significant difference between the two screening interventions. However, the number of false-positive results and biopsy rates were higher in women receiving additional ultrasonography for breast cancer screening. None of the included studies analysed whether the higher number of screen-detected cancers in the intervention group resulted in a lower mortality rate compared to mammography alone. Randomised controlled trials or prospective cohort studies with a longer observation period are needed to assess the effects of the two screening interventions on morbidity and mortality.

Challenges and Changes of the Breast Cancer Screening Paradigm

Since four decades mammography is used for early breast cancer detection in asymptomatic women and still remains the gold standard imaging modality. However, population screening programs can be personalized and women can be divided into different groups based on risk factors and personal preferences. The availability of new and evolving imaging modalities, for example, digital breast tomosynthesis, dynamic-contrast-enhanced magnetic resonance imaging (MRI), abbreviated MRI protocols, diffusion-weighted MRI, and contrast-enhanced mammography leads to new challenges and perspectives regarding the feasibility and potential harms of breast cancer screening. The aim of this review is to discuss the current guidelines for different risk groups, to analyze the recent published studies about the diagnostic performance of the imaging modalities and to discuss new developments and future perspectives. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 6.

Determining whether the diagnostic value of B-ultrasound combined with contrast-enhanced ultrasound and shear wave elastography in breast mass-like and non-mass-like lesions differs: a diagnostic test

Background

Mass-like (ML) and non-mass-like (NML) are two manifestations of breast lesions on ultrasound. Contrast-enhanced ultrasound (CEUS) can make up for the limitation of B-ultrasound (US) in the observation of focal blood flow, and shear wave elastography (SWE) can supplement the hardness information of the lesion. The present study aimed to analyze the characteristic manifestations of US, CEUS, and SWE in NML and ML breast and evaluate whether the diagnostic performance of these three ultrasound techniques differs in terms of differentiating between benign and malignant breast lesions.

Methods

From January to August 2021, 382 patients (417 breast lesions) underwent US, CEUS, and SWE examinations. Of these, 204 women (218 breast lesions) were included in our study due to subsequent biopsy or surgery with pathological findings. The patients were divided into ML and NML groups according to the ultrasound characteristics, and the differences in multimodal ultrasound performance between benign and malignant NML and benign and malignant ML breast lesions were compared. The diagnostic performance of US, US + CEUS, US + SWE, US + CEUS + SWE for ML, NML and all breast lesions was evaluated by analyzing sensitivity, specificity and area under receiver operating characteristic (ROC) curve (AUC).

Results

Pathologically, the 218 lesions included 96 malignant and 122 benign breast lesions. The sensitivity and specificity of US + CEUS + SWE in all lesion groups, ML group and NML group were 92.7% and 90.2%, 95.9% and 90.3%, 91.3% and 79.3%, respectively. In all breast group, AUCs of US + CEUS, US + SWE, US + CEUS + SWE were statistically different from AUC of US (P=0.0010, 0.0001, 0.0001). In the ML group, the AUC of US + CEUS, US + SWE, US + CEUS + SWE were statistically different from that of US (P=0.0120, 0.0008, 0.0002). In the NML group, there was a statistical difference between US + SWE and US AUC (P=0.0149).

Conclusions

US, CEUS, and SWE have an important diagnostic value for benign and malignant ML and NML breast lesions. Multimodal ultrasound combined with US, CEUS, and SWE can improve the diagnostic efficacy in distinguishing between benign and malignant ML and NML lesions.

Cost-effectiveness evaluation of risk-based breast cancer screening in Urban Hebei Province

To evaluate the implementations of Cancer Screening Program in Urban Hebei and to model the cost-effectiveness of a risk-based breast Cancer Screening Program. Women aged 40-74 years were invited to participate the Cancer Screening Program in Urban Hebei form 2016 to 2020 by completing questionnaires to collect information about breast cancer exposure. Clinical screening including ultrasound and mammography examination were performed. We developed a Markov model to estimate the lifetime costs and benefits, in terms of quality-adjusted life years (QALY), of a high-risk breast Cancer Screening Program. Nine screening strategies and no screening were included in the study. The age-specific incidence, transition probability data and lifetime treatment costs were derived and adopted from other researches. Average cost-effectiveness ratios (ACERs) were estimated as the ratios of the additional costs of the screening strategies to the QLYG compared to no screening. Incremental cost-effectiveness ratios (ICERs) were calculated based on the comparison of a lower cost strategies to the next more expensive and effective strategies after excluding dominated strategies and extendedly dominated strategies. ICERs were used to compare with a willingness-to-pay (WTP) threshold. Sensitivity analysis was explored the influence factors. A total of 84,029 women completed a risk assessment questionnaire, from which 20,655 high-risk breast cancer females were evaluated, with a high-risk rate of 24.58%. There were 13,392 high-risk females completed the screening program, with participation rate was 64.84%. Undergoing ultrasound, mammography and combined screening, the suspicious positive detection rates were 15.00%, 9.20% and 19.30%, and the positive detection rates were 2.11%, 2.76% and 3.83%, respectively. According to the results by Markov model, at the end of 45 cycle, the early diagnosis rates were 55.53%, 60.68% and 62.47% underwent the annual screening by ultrasound, mammography and combined, the proportion of advanced cancer were 17.20%, 15.85% and 15.36%, respectively. Different screening method and interval yield varied. In the exploration of various scenarios, annual ultrasound screening is the most cost-effective strategy with the ICER of ¥116,176.15/QALY. Sensitivity analyses demonstrated that the results are robust. Although it was not cost effective, combined ultrasound and mammography screening was an effective strategy for higher positive detection rate of breast cancer. High-risk population-based breast cancer screening by ultrasound annually was the most cost-effective strategy in Urban Hebei Province.

Evaluation of Lee-Carter model to breast cancer mortality prediction in China and Pakistan

Background

Precise breast cancer-related mortality forecasts are required for public health program and healthcare service planning. A number of stochastic model-based approaches for predicting mortality have been developed. The trends shown by mortality data from various diseases and countries are critical to the effectiveness of these models. This study illustrates the unconventional statistical method for estimating and predicting the mortality risk between the early-onset and screen-age/late-onset breast cancer population in China and Pakistan using the Lee-Carter model.

Methods

Longitudinal death data for female breast cancer from 1990 to 2019 obtained from the Global Burden of Disease study database were used to compare statistical approach between early-onset (age group, 25-49 years) and screen-age/late-onset (age group, 50-84 years) population. We evaluated the model performance both within (training period, 1990-2010) and outside (test period, 2011-2019) data forecast accuracy using the different error measures and graphical analysis. Finally, using the Lee-Carter model, we predicted the general index for the time period (2011 to 2030) and derived corresponding life expectancy at birth for the female breast cancer population using life tables.

Results

Study findings revealed that the Lee-Carter approach to predict breast cancer mortality rate outperformed in the screen-age/late-onset compared with that in the early-onset population in terms of goodness of fit and within and outside forecast accuracy check. Moreover, the trend in forecast error was decreasing gradually in the screen-age/late-onset compared with that in the early-onset breast cancer population in China and Pakistan. Furthermore, we observed that this approach had provided almost comparable results between the early-onset and screen-age/late-onset population in forecast accuracy for more varying mortality behavior over time like in Pakistan. Both the early-onset and screen-age/late-onset populations in Pakistan were expected to have an increase in breast cancer mortality by 2030. whereas, for China, it was expected to decrease in the early-onset population.

Conclusion

The Lee-Carter model can be used to estimate breast cancer mortality and so to project future life expectancy at birth, especially in the screen-age/late-onset population. As a result, it is suggested that this approach may be useful and convenient for predicting cancer-related mortality even when epidemiological and demographic disease data sets are limited. According to model predictions for breast cancer mortality, improved health facilities for disease diagnosis, control, and prevention are required to reduce the disease's future burden, particularly in less developed countries.

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts

OBJECTIVES

To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts.

METHODS

430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden's index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity.

RESULTS

The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden's index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve.

CONCLUSION

Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

Sonographic prediction of intraductal papillary carcinoma with partially cystic breast lesions

BACKGROUND

Intraductal papillary carcinoma (IDPC) is a rare but fatal disease. Preoperative ultrasound diagnosis of IDPC remains challenging and meaningful. The aim of the study was to determine an effective ultrasound model to predict intraductal papillary carcinoma (IDPC) in patients with partially cystic breast lesions on ultrasound.

METHODS

We reviewed female patients with breast nodules who underwent biopsy or surgery between 2004 and 2019, and pathological results were used as the reference standard. We finally included 21 IDPC patients with partially cystic lesions on preoperative ultrasound matched to 40 patients with intraductal papilloma. The association of ultrasound features with IDPC was analysed.

RESULTS

Posterior echo enhancement (P < 0.001), tumour size (P = 0.002), irregular shape (P = 0.003), wide base (P = 0.003), solid-mainly component (P = 0.013), rich Doppler flow (P < 0.001) and multiple lesions (P = 0.044) were associated with IDPC by univariate analysis. Based on univariate analysis, variables were included in the regression analysis to obtain independent factors. The regression analysis showed that microcalcification, multiple lesions, posterior echo enhancement, wide base of solid components and rich colour Doppler flow were predictors for IDPC (P < 0.001). The collective model of the independent factors (microcalcification, multiple lesions, posterior echo enhancement, wide base of solid components and rich colour Doppler flow) could predict IDPC with an area under the curve (AUC) of 0.99 (95% CI 0.95-1.00). The collective model had a better net benefit demonstrated by the decision curve.

CONCLUSION

Ultrasonic features may be an applicable model for predicting IDPC with partially cystic breast lesions on ultrasound and has a better potential to facilitate decision-making preoperatively.

Translation, Adaptation, and Validation of the Modified Thai Version of Champion's Health Belief Model Scale (MT-CHBMS)

BACKGROUND

While breast cancer is the leading cause of cancer death among Thai women, breast self-examination (BSE), mammography, and ultrasound use are still underutilized. There is a need to assess women's beliefs about breast cancer and screening in different cultural settings. As a result, a tool to measure the beliefs that influence breast-cancer-screening practices is needed. Champion's Health Belief Model Scale (CHBMS) is a valid and reliable tool for assessing individuals' attitudes toward breast cancer and screening methods, but it has not been validated in Thai women. The study aimed to translate and validate the CHBMS for breast self-examination and mammography among Thai women and to modify the original scale by adding ultrasound items for breast cancer screening. In addition, the purpose of this study was to create a modified Thai version of the CHBMS which could be used to better understand patients' beliefs regarding breast cancer screening in Thailand, in order to develop practical and effective interventions suited to their beliefs.

METHODS

The CHBMS was translated into Thai, validated by a panel of experts, back-translated, modified by adding content about ultrasound for screening breast cancer, and pretested. Confirmatory factor analysis was used with a sample of 130 Thai women aged 40 to 70 years old.

RESULT

The final MT-CHBMS consisted of 64 items determining ten subscales: susceptibility, seriousness, benefits-breast self-examination, benefits-mammogram, barriers-BSE, barriers-mammogram, confidence, health motivation, benefits-ultrasound, and barriers-ultrasound. The MT-CHBMS demonstrated excellent internal consistency. The ten-factor model was best fitted to the data.

CONCLUSION

The MT-CHBMS was found to be a reliable and valid tool for measuring individuals' attitudes toward breast cancer and screening methods. The scale could be easily used by healthcare providers to determine the beliefs before planning appropriate interventions to increase early detection.

Breast ultrasound in Chinese hospitals: A cross-sectional study of the current status and influencing factors of BI-RADS utilization and diagnostic accuracy

BACKGROUND

With the growing demand for breast screening in public health services and clinical care, ultrasound departments in China are facing tremendous challenges.

METHODS

A cross-sectional nationwide survey was conducted in 5,460 departments providing ultrasound diagnoses in mainland China from 2020 to 2021. The survey included general information about the ultrasound department, the characteristics of sonologists, the use of Breast Imaging Reporting and Data System (BI-RADS) templates, and the diagnostic accuracy rate of breast cancer ultrasound.

FINDINGS

There were on average 2.25 sonologists per 10,000 patients in mainland China per year. The average utilization rate of BI-RADS in Chinese hospitals was 87.02%. The GDP per capita of the province (P = 0.008), whether the hospital was specialized (P = 0.002) or a Tier 3 facility (P < 0.001), the percentage of doctors with master's and doctoral degrees (P < 0.001) and doctors ≤35 years (P = 0.005) were significantly and independently associated with the utilization rate of BI-RADS. The average diagnostic accuracy rate of breast cancer ultrasound in Chinese hospitals was 73.64%, and we observed significant positive associations between GDP per capita (P = 0.02), BI-RADS utilization rate (P = 0.019), and breast cancer ultrasound diagnostic accuracy.

INTERPRETATION

The utilization of BI-RADS templates effectively improved the diagnostic accuracy of ultrasound. Moreover, the survey summarized the current situation of departments and sonologists providing breast ultrasound diagnosis in mainland China, which helped monitor the development of the discipline and provide information for administrators to meet the growing demand.

FUNDING

This work was supported by Natural Science Foundation of Beijing (7202156) and Foundation of ihecc (2019-C-0646-2).

Comparison of ultrasound and mammography for early diagnosis of breast cancer among Chinese women with suspected breast lesions: A prospective trial

BACKGROUND

Ultrasound is more widely used than mammography for early diagnosis of breast cancer in China as most Chinese women have small and dense mammary glands. This study compared the diagnostic performance of ultrasound and mammography for breast cancer among Chinese women with suspected breast lesions.

METHODS

From November 2019 to November 2021, we compared the results of ultrasound and mammography for breast lesion diagnosis in 2737 consecutive participants with suspected breast lesions; all patients underwent biopsies. We measured the sensitivity, specificity, and diagnostic accuracy separately.

RESULTS

Among the 2737 participants, 2844 breast lesions were detected, including 1935 (68.0%) breast cancers and 909 (32.0%) benign lesions. Of the breast cancers, ultrasound detected 1851 (95.7%), whereas mammography detected 1527 (78.9%). The sensitivity of ultrasound for breast cancer diagnosis was significantly higher than that of mammography (95.7% vs. 78.9%, p < 0.001), whereas the specificity was significantly lower than that of mammography (42.9% vs. 62.3%, p < 0.001). The receiver operating characteristic curves revealed that ultrasound was more accurate in detecting breast cancer than mammography (76.8% vs. 71.3%, p < 0.001). Age, body mass index, and breast density did not influence ultrasound sensitivity and accuracy.

CONCLUSIONS

Ultrasound is more sensitive and accurate than mammography and detects more breast cancers with a lower specificity.

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

Background

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

Methods

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

Results

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

Conclusion

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

Long-term survival of screen-detected synchronous and metachronous bilateral non-palpable breast cancer among Chinese women: a hospital-based study (2003-2017)

Purpose

Screen-detected unilateral non-palpable breast cancer (NPBC) shows favorable prognosis, whereas bilateral breast cancer (BBC), especially synchronous BBC (SBBC) manifests worse survival than unilateral breast cancer (BC). It remains unclear whether screen-detected bilateral NPBC has compromised survival and requires intensified treatment or favorable prognosis and needs de-escalating therapy.

Methods

From 2003 to 2017, 1,075 consecutive NPBC patients were retrospectively reviewed. There were 988 patients with unilateral NPBC (UniNPBC), and 87 patients with ipsilateral NPBC + any contralateral BC [(N + AnyContra) PBC], including 32 patients with bilateral NPBC (BiNPBC) and 55 patients with ipsilateral NPBC + contralateral palpable cancer [(N + Contra) PBC]. Median follow-up time was 91 (48–227) months. Clinicopathological characteristics were compared between UniNPBC and BBC, whereas relapse-free survival (RFS) and overall survival (OS) among BBC subgroups. RFS and OS factors of BBC were identified.

Results

Compared to UniNPBC, patients with screen-detected bilateral BC had more invasive (85.1%, 74.8%), ER negative (26.4%, 17.1%), PR negative (36.8%, 23.5%), triple-negative (21.6%, 8.5%) BC as well as less breast conserving surgery (17.2%, 32.4%), radiotherapy (13.8%, 32.0%) and endocrine therapy (71.3%, 83.9%). 10 year RFS and OS rates of (N + AnyContra) PBC (72.8%, 81.5%), (N + Contra) PBC (60.6%, 73.9%), and synchronous (N + Contra) PBC (58.1%, 70.1%) were significantly compromised compared to UniNPBC (91.0%, 97.2%). RFS factors of BBC included pN3 (p = 0.048), lymphovascular invasion (p = 0.008) and existence of contralateral palpable interval BC (p = 0.008), while the OS relevant factor was pN3 (p = 0.018).

Conclusion

Screen-detected bilateral NPBC including SynBiNPBC and MetaBiNPBC showed good prognosis as UniNPBC so that the therapy of BiNPBC could be de-escalated and optimized according to UniNPBC. Contrarily, screen-detected ipsilateral NPBC with contralateral palpable BC [(N + Contra) PBC] manifested unfavorable survival worse than UniNPBC and synchronous (N + Contra) PBC had the worst survival among all subgroups, implying that these were actually bilateral interval BC and required intensified treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-022-06747-5.

Development and evaluation of the screening performance of a low-cost high-risk screening strategy for breast cancer

OBJECTIVE

To develop and evaluate the screening performance of a low-cost high-risk screening strategy for breast cancer in low resource areas.

METHODS

Based on the Multi-modality Independent Screening Trial, 6 questionnaire-based risk factors of breast cancer (age at menarche, age at menopause, age at first live birth, oral contraceptive, obesity, family history of breast cancer) were used to determine the women with high risk of breast cancer. The screening performance of clinical breast examination (CBE), breast ultrasonography (BUS), and mammography (MAM) were calculated and compared to determine the optimal screening method for these high risk women.

RESULTS

A total of 94 breast cancers were detected among 31,720 asymptomatic Chinese women aged 45-65 years. Due to significantly higher detection rates (DRs) and suitable coverage of the population, high risk women were defined as those with any of 6 risk factors. Among high risk women, the DR for BUS [3.09/1,000 (33/10,694)] was similar to that for MAM [3.18/1,000 (34/10,696)], while it was significantly higher than that for the CBE [1.73/1,000 (19/10,959), P = 0.002]. Compared with MAM, BUS showed significantly higher specificity [98.64% (10,501/10,646) vs. 98.06% (10,443/10,650), P = 0.001], but no significant differences in sensitivity [68.75% (33/48) vs. 73.91% (34/46)], positive prediction values [18.54% (33/178) vs. 14.11% (34/241)], and negative prediction values [99.86% (10,501/10,516) vs. 99.89% (10,443/10,455)]. Further analyses showed no significant difference in the percentages of early stage breast cancer [53.57% (15/28) vs. 50.00% (15/30)], lymph node involvement [22.73% (5/22) vs. 28.00% (7/25)], and tumor size ≥ 2 cm [37.04% (10/27) vs. 29.03% (9/31)] between BUS and MAM. Subgroup analyses stratified by breast densities or age at enrollment showed similar results.

CONCLUSIONS

The low-cost high-risk screening strategy based on 6 questionnaire-based risk factors was an easy-to-use method to identify women with high risk of breast cancer. Moreover, BUS and MAM had comparable screening performances among high risk women.

Application of S-detect combined with virtual touch imaging quantification in ultrasound for diagnosis of breast mass

OBJECTIVES

Ultrasound is a safe and timely diagnosis method commonly used for breast lesion, however it depends on the operator to a certain degree. As an emerging technology, artificial intelligence can be combined with ultrasound in depth to improve the intelligence and precision of ultrasound diagnosis and avoid diagnostic errors caused by subjectivity of radiologists. This paper aims to investigate the value of artificial intelligence S-detect system combined with virtual touch imaging quantification (VTIQ) technique in the differential diagnosis of benign and malignant breast masses by conventional ultrasound (CUS). respectively, and AUCs for them were 0.74, 0.86, 0.79, and 0.94, respectively. The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic specificity of CUS+VTIQ was higher than that of CUS (P<0.05). The diagnostic accuracy and AUC of CUS+S-detect+VTIQ were higher than those of S-detect or VTIQ applied to CUS alone (P<0.05). The sensitivities of CUS for senior radiologists, CUS for junior radiologists, CUS+S-detect+VTIQ for senior radiologists, and CUS+S-detect+VTIQ for junior radiologists were 60.00%, 80.00%, 72.73%, and 90.00%, respectively, when diagnosing benign and malignant breast masses in 50 randomly selected cases. The specificities for them was 66.67%, 76.67%, 80.00%, and 81.25%, respectively. The accuracies for them was 64.00%, 78.00%, 80.00%, and 88.00%, respectively. The AUCs for them were 0.63, 0.78, 0.88, and 0.80, respectively. Compared with the CUS for junior radiologists, the CUS+S-detect+VTIQ for junior radiologists had higher sensitivity, specificity, and accuracy (all P<0.05). The consistency of the combined application of S-detect and VTIQ for diagnosing breast masses at 2 different times was good among junior radiologists (Kappa=0.800).

METHODS

CUS, S-detects, and VTIQ were used to differentially diagnose benign and malignant breast masses in 108 cases, and the final pathological results were referred to as the gold standard for classifying breast masses. The diagnostic efficacy were evaluated and compared, among the 3 methods and among S-detect applied to CUS (CUS+S-detect), VTIQ applied to CUS (CUS+VTIQ), and S-detect combined with VTIQ applied to CUS (CUS+S-detect+VTIQ). Fifty cases were acquired randomly from the collected breast masses, and 2 radiologists with different years of experience (2 and 8 years) used S-detect combined with VTIQ for the ultrasonic differential diagnosis of benign and malignant breast masses.

RESULTS

The differences in sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) of the 3 diagnostic methods of CUS, S-detect, and VTIQ were not statistically significant (all P>0.05). The sensitivities of CUS, CUS+Sdetect, CUS+VTIQ, and CUS+S-detect+VTIQ were 78.57%, 92.86%, 69.05%, and 95.24%, respectively, the specificities for them were 69.70%, 78.79%, 87.88%, and 92.42%, respectively, the accuracies for them were 73.15%, 84.26%, 80.56%, and 93.52%.

CONCLUSIONS

S-detect combined with VTIQ when applied to CUS can overcome the shortcomings of separate applications and complement each other, especially for junior radiologists, and can more effectively improve the diagnostic efficacy of ultrasound for benign and malignant breast masses.

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

BACKGROUND

Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists.

METHODS

A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model's ability to assist the radiologists using two different methods.

RESULTS

The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities.

CONCLUSIONS

The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

The Clinical Application of Combined Ultrasound, Mammography, and Tumor Markers in Screening Breast Cancer among High-Risk Women

In order to explore the clinical application value of color Doppler ultrasound (CDUS), mammography (MAM), and serum tumor marker carbohydrate antigen 153 (CA153) in screening breast cancer (BC) for high-risk women, a total of 38,241 women were surveyed by epidemiological questionnaire on BC high-risk factors. A total of 10,821 cases were screened, accounting for 28.30%. They were randomly divided into US, MAM, and CA153 and combined examination group which has no significant difference in high-risk factors. Breast cancer in high-risk population was screened by CDUS, MAM, and CA153 and combined examination. CA153 was detected by electroluminescence method. The positive detection rate of BC was 360.41/100,000 (39/10,821). The overall difference in the positive detection rate of BC among 10,821 cases in all age groups was statistically significant. The sensitivity and negative predictive value of combined examination were significantly improved compared with each single examination. Combined examination for BC screening can significantly improve the sensitivity of BC early diagnosis and reduce the missed diagnosis rate.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

OBJECTIVES

To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists.

METHODS

From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed.

RESULTS

After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05).

CONCLUSIONS

Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

Breast cancer screening and early diagnosis in Chinese women

Breast cancer is the most common malignant tumor in Chinese women, and its incidence is increasing. Regular screening is an effective method for early tumor detection and improving patient prognosis. In this review, we analyze the epidemiological changes and risk factors associated with breast cancer in China and describe the establishment of a screening strategy suitable for Chinese women. Chinese patients with breast cancer tend to be younger than Western patients and to have denser breasts. Therefore, the age of initial screening in Chinese women should be earlier, and the importance of screening with a combination of ultrasound and mammography is stressed. Moreover, Chinese patients with breast cancers have several ancestry-specific genetic features, and aiding in the determination of genetic screening strategies for identifying high-risk populations. On the basis of current studies, we summarize the development of risk-stratified breast cancer screening guidelines for Chinese women and describe the significant improvement in the prognosis of patients with breast cancer in China.

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study

PURPOSE

To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.

METHODS

Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites.

RESULTS

A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157).

CONCLUSIONS

Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.

Diagnostic value of Doppler imaging for malignant non-mass breast lesions: with different diagnostic criteria for older and younger women: first results

OBJECTIVE

To evaluate and optimize the additional diagnostic value of Doppler imaging for malignant NMLs detected by US.

MATERIALS AND METHODS

The characteristics of 233 NMLs in Doppler imaging were analyzed, and different Adler grades of intralesional vessels were selected as the diagnostic cutoffs on Doppler imaging: grade 1 in the full cohort and in women < 40 years, and grade 0 in women ≥40 years. The diagnostic performance of US and US + Doppler imaging were calculated and compared with that of mammography.

RESULTS

The AUC of US + Doppler was larger than that of US alone in each group (P < 0.001). In the full cohort, addition of Doppler imaging increased specificity of US, but decreased sensitivity. However, by use of different diagnostic cutoffs in the two subgroups, it was possible to achieve high sensitivity and specificity simultaneously, which were 100% and 75.8% in women < 40 years, 94.7% and 69.5% in women ≥40 years, respectively. The AUC + Doppler was comparable to that of mammography in the full cohort and in women ≥40 years. In women < 40 years, the AUC of the combination was larger than that of mammography (P < 0.001).

CONCLUSION

Doppler imaging, with different Adler grades used as cutoffs in older versus younger women, can improve the specificity of US for the diagnosis of malignant NMLs without losing sensitivity. In younger women, US + Doppler imaging may be better than mammography.

Gail Model Improves the Diagnostic Performance of the Fifth Edition of Ultrasound BI-RADS for Predicting Breast Cancer: A Multicenter Prospective Study

RATIONALE AND OBJECTIVES

The sonographic appearance of benign and malignant breast nodules overlaps to some extent, and we aimed to assess the performance of the Gail model as an adjunctive tool to ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) for predicting the malignancy of nodules.

MATERIALS AND METHODS

From 2018 to 2019, 2607 patients were prospectively enrolled by 35 health care facilities. An individual breast cancer risk was assessed by the Gail model. Based on B-mode US, color Doppler, and elastography, all nodules were evaluated according to the fifth edition of BI-RADS, and these nodules were all confirmed later by pathology.

RESULTS

We demonstrated that the Gail model, age, tumor size, tumor shape, growth orientation, margin, contour, acoustic shadowing, microcalcification, presence of duct ectasia, presence of architectural distortion, color Doppler flow, BI-RADS, and elastography score were significantly related to breast cancer (all p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve (AUC) for combining the Gail model with the BI-RADS category were 95.6%, 91.3%, 85.0%, 97.6%, 92.8%, and 0.98, respectively. Combining the Gail model with the BI-RADS showed better diagnostic efficiency than the BI-RADS and Gail model alone (AUC 0.98 vs 0.80, p < 0.001; AUC 0.98 vs 0.55, p < 0.001) and demonstrated a higher specificity than the BI-RADS (91.3% vs 59.4%, p < 0.001).

CONCLUSION

The Gail model could be used to differentiate malignant and benign breast lesions. Combined with the BI-RADS category, the Gail model was adjunctive to US for predicting breast lesions for malignancy. For the diagnosis of malignancy, more attention should be paid to high-risk patients with breast lesions.

Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma

Objectives

Mucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA.

Materials and Methods

The study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA.

Results

The nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000–0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively.

Conclusion

Our study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.

Evaluation of Different Breast Cancer Screening Strategies for High-Risk Women in Beijing, China: A Real-World Population-Based Study

BACKGROUND

Mammography-based breast cancer screening has been widely implemented in many developed countries. Evidence was needed on participation and diagnostic performance of population-based breast cancer screening using ultrasound in China.

METHODS

We used data from the Cancer Screening Program in Urban China in Beijing from 2014 to 2019 and was followed up until July 2020 by matching with the Beijing Cancer Registry database. Eligible women between the ages of 45 and 69 years were recruited from six districts and assessed their risk of breast cancer through an established risk scoring system. Women evaluated to be at high risk of breast cancer were invited to undergo both ultrasound and mammography. Participation rates were calculated, and their associated factors were explored. In addition, the performance of five different breast cancer screening modalities was evaluated in this study.

RESULTS

A total of 49,161 eligible women were recruited in this study. Among them, 15,550 women were assessed as high risk for breast cancer, and 7,500 women underwent ultrasound and/or mammography as recommended, with a participation rate of 48.2%. The sensitivity of mammography alone, ultrasound alone, combined of ultrasound and mammography, ultrasound for primary screening followed by mammography for triage, and mammography for preliminary screening followed by ultrasound for triage were19.2%, 38.5%, 50.0%, 46.2%, and 19.2%, and the specificity were 96.1%, 98.6%, 94.7%, 97.6%, 95.7%, respectively. The sensitivity of combined ultrasound and mammography, ultrasound for primary screening followed by mammography for triage, was significantly higher than mammography alone (p=0.008 and p=0.039). Additionally, ultrasound alone (48,323 RMB ($7,550)) and ultrasound for primary screening followed by mammography for triage (55,927 RMB ($8,739)) were the most cost-effective methods for breast cancer screening than other modalities.

CONCLUSIONS

Ultrasound alone and ultrasound for primary screening and mammography are superior to mammography for breast cancer screening in high-risk Chinese women.

Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study

Background

Screening of breast cancer in asymptomatic women is important to evaluate for early diagnosis. In China ultrasound is a more frequently used method than mammography for the detection of breast cancer. The objectives of the study were to provide evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women.

Methods

Breast ultrasound examinations including the parenchymatous pattern of cytopathological confirmed breast cancer (n = 541) and age-matched cytopathological not confirmed breast cancer (n = 849) women were retrospectively reviewed by seven sonographer physicians. According to compositions of ducts, the thickness of the breast, diameter of ducts, fat lobules, and fibro glandular tissues, the breast parenchymatous pattern was categorized into heterogeneous (high percentage of fatty tissues), ductal (the inner diameters of ducts > 50% of the thick mass of the breast), mixed (the inner diameters of ducts was 50% of the thick mass of the breast), and fibrous categories (a dense classification of the breast).

Results

Heterogeneous (p < 0.0001, OR = 3.972) and fibrous categories (p < 0.0001, OR = 2.702) were higher among women who have cytopathological confirmed breast cancer than those who have not cytopathological confirmed breast cancer. The heterogeneous category was high-risk ultrasonographic examination category followed by the fibrous category. Agreements between sonographer physicians for categories of ultrasonic examinations were fair to good (Cohen’s k = 0.591).

Conclusions

Breast cancer risk in Chinese asymptomatic women differ according to the ultrasonographic breast parenchymal pattern.

Level of Evidence: III.

Technical efficacy stage: 2.

Ultrasound-Based Nomogram for Distinguishing Malignant Tumors from Nodular Sclerosing Adenoses in Solid Breast Lesions

OBJECTIVES

Nodular sclerosing adenoses (NSAs) and malignant tumors (MTs) may coexist and are often classified into the same Breast Imaging Reporting and Data System (BI-RADS) category. We aimed to build and validate an ultrasound-based nomogram to distinguish MT from NSA for building a precise sequence of biopsies.

MATERIALS AND METHODS

The training cohort included 156 patients (156 masses) with NSA or MT at one study institution. We used best subset regression to determine the predictors for building a nomogram from ultrasonic characteristics and patients' age. Model performance and clinical utility were evaluated using Brier score, concordance (C)-index, calibration curve, and decision curve analysis. The independent validation cohort consisted of 162 patients (162 masses) from a separate institution.

RESULTS

Through best subset regression, we selected 6 predictors to develop nomogram: age, calcification, echogenic rim, vascularity distribution, tumor size, and thickness of breast parenchyma. Brier score and C-index of the nomogram in the training cohort were 0.068 and 0.967 (95% confidence interval [CI]: 0.941-0.993), respectively. In addition, calibration curve demonstrated good agreement between prediction and pathological result. In the validation cohort, the nomogram still obtained a favorable C-index score of 0.951 (95% CI: 0.919-0.983) and fine calibration. Decision curve analysis showed that the model was clinically useful.

CONCLUSIONS

If multiple NSA and MT masses are present in the same patient and are classified into the same BI-RADS category, our nomogram can be used as a supplement to the BI-RADS category for accurate biopsy of the mass most likely to be MT.

The Distinct Performances of Ultrasound, Mammograms, and MRI in Detecting Breast Cancer in Patients With Germline Pathogenic Variants in Cancer Predisposition Genes

A proportion of up to 10% of breast cancer resulted from hereditary germline pathogenic variants (GPVs) in cancer predisposition genes (CPGs), which been demonstrated distinct clinical features and imaging manifestations. However, the performance of imaging modalities for breast cancer surveillance in CPG mutation-carriers is still unclear, especially in Asian women. A population of 3002 breast cancer patients who received germline genetic testing of CPGs was enrolled from three hospitals in China. In total, 343 (11.6%) patients were found to harbor GPVs in CPGs, including 137 (4.6%) in BRCA1 and 135 (4.6%) in BRCA2. We compared the performances of ultrasound, mammograms, MRI, and the combining strategies in CPG mutation carriers and non-carriers. As a result, the ultrasound showed a higher detection rate compared with mammograms regardless of the mutation status. However, its detection rate was lower in CPG mutation carriers than in non-carriers (93.2% vs 98.0%, P=2.1×10^-4), especially in the BRCA1 mutation carriers (90.9% vs 98.0%, P=2.0×10^-4). MRI presented the highest sensitivity (98.5%) and the lowest underestimation rate (14.5%) in CPG mutation carriers among ultrasound, mammograms, and their combination. Supplemental ultrasound or mammograms would add no significant value to MRI for detecting breast cancer (P>0.05). In multivariate logistic regression analysis, the family or personal cancer history could not replace the mutation status as the impact factor for the false-negative result and underestimation. In summary, clinicians and radiologists should be aware of the atypical imaging presentation of breast cancer in patients with GPVs in CPGs.

A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting

Background

Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety.

Methods

A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation.

Results

The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR =4.588, P=0.004), US features [margin (OR =2.916, P=0.019), shape (irregular vs. oval, OR =2.474, P=0.044; round vs. oval, OR =1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR =7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR =6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups.

Conclusions

We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.

Comparison of Automated Breast Ultrasound and Hand-Held Breast Ultrasound in the Screening of Dense Breasts

OBJECTIVE

 To compare hand-held breast ultrasound (HHBUS) and automated breast ultrasound (ABUS) as screening tool for cancer.

METHODS

 A cross-sectional study in patients with mammographically dense breasts was conducted, and both HHBUS and ABUS were performed. Hand-held breast ultrasound was acquired by radiologists and ABUS by mammography technicians and analyzed by breast radiologists. We evaluated the Breast Imaging Reporting and Data System (BI-RADS) classification of the exam and of the lesion, as well as the amount of time required to perform and read each exam. The statistical analysis employed was measures of central tendency and dispersion, frequencies, Student t test, and a univariate logistic regression, through the odds ratio and its respective 95% confidence interval, and with p < 0.05 considered of statistical significance.

RESULTS

 A total of 440 patients were evaluated. Regarding lesions, HHBUS detected 15 (7.7%) BI-RADS 2, 175 (89.3%) BI-RADS 3, and 6 (3%) BI-RADS 4, with 3 being confirmed by biopsy as invasive ductal carcinomas (IDCs), and 3 false-positives. Automated breast ultrasound identified 12 (12.9%) BI-RADS 2, 75 (80.7%) BI-RADS 3, and 6 (6.4%) BI-RADS 4, including 3 lesions detected by HHBUS and confirmed as IDCs, in addition to 1 invasive lobular carcinoma and 2 high-risk lesions not detected by HHBUS. The amount of time required for the radiologist to read the ABUS was statistically inferior compared with the time required to read the HHBUS (p < 0.001). The overall concordance was 80.9%. A total of 219 lesions were detected, from those 70 lesions by both methods, 126 only by HHBUS (84.9% not suspicious by ABUS) and 23 only by ABUS.

CONCLUSION

 Compared with HHBUS, ABUS allowed adequate sonographic study in supplemental screening for breast cancer in heterogeneously dense and extremely dense breasts.