Sensitivity Of Mammography Research Articles

135 (PB-048) Poster - Finding patient-reported deterrents to adjunct Breast Cancer screening among patients with dense breast tissues. A cross sectional study in Pakistan

Background: Women with dense breast tissues are at a greater risk of developing cancer because of reduced sensitivity of mammography. In order to address this issue, there’s need to incorporate adjunct screening modalities for this particular population. We aim to identify patient’s characteristics such as age, socioeconomic status, personal risk of breast cancer etc associated with patient cited concerns about adjunct breast screening modalities.

Automated Assessment of Breast Positioning Quality in Screening Mammography.

Simple SummaryInadequate breast positioning quality is the main cause behind misdiagnosis of breast cancer in screening mammography. For this reason, the first step before any cancer diagnosis is to ensure that the acquired mammograms have adequate breast positioning quality according to predefined criteria. If this is not the case, the patient must return for a new mammography. In this study, we proposed an approach for an automatic assessment of breast positioning quality in screening mammography using Convolutional Neural Networks. Eventually, this approach is not intended to replace radiology technicians, but rather to assist them in identifying inadequately positioned mammograms in real time, reduce the number of returned patient and improve the efficiency of cancer detection. For each predefined criterion, a specific convolutional neural network was separately trained and then combined into an overall system that predicts whether the breast is well positioned or not, achieving an efficient accuracy of 96.5% for craniocaudal and 93.3% for mediolateral oblique images. Our approach differs from already available studies and commercial tools by taking into account more useful breast positioning criteria that have to be considered by the expert, and thus providing a more holistic assistance.Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria.

Opinions on the Assessment of Breast Density Among Members of the Society of Breast Imaging.

Dense breast decreases the sensitivity and specificity of mammography and is associated with an increased risk of breast cancer. We conducted a survey to assess the opinions of Society of Breast Imaging (SBI) members regarding density assessment. An online survey was sent to SBI members twice in September 2020. The survey included active members who were practicing radiologists, residents, and fellows. Mammograms from three patients were presented for density assessment based on routine clinical practice and BI-RADS fourth and fifth editions. Dense breasts were defined as heterogeneously or extremely dense. Frequencies were calculated for each survey response. Pearson's correlation coefficient was used to evaluate the correlation of density assessments by different definitions. The survey response rate was 12.4% (357/2875). For density assessments, the Pearson correlation coefficients between routine clinical practice and BI-RADS fourth edition were 0.05, 0.43, and 0.12 for patients 1, 2, and 3, respectively; these increased to 0.65, 0.65, and 0.66 between routine clinical practice and BI-RADS fifth edition for patients 1, 2, and 3, respectively. For future density grading, 79.0% (282/357) of respondents thought it should reflect both potential for masking and overall dense tissue for risk assessment. Additionally, 47.1% (168/357) of respondents thought quantitative methods were of use. Density assessment varied based on routine clinical practice and BI-RADS fourth and fifth editions. Most breast radiologists agreed that density assessment should capture both masking and overall density. Moreover, almost half of respondents believed computer or artificial intelligence-assisted quantitative methods may help refine density assessment.

Does artificial intelligence aid in the detection of different types of breast cancer?

BackgroundOn mammography many cancers may be missed even in retrospect either due to the breast density, the small size of the tumor or the subtle signs of cancer that are imperceptible. We aimed to compare the sensitivity of artificial intelligence (AI) to that of digital mammography in the detection of different types of breast cancer. Also, the sensitivity of AI in picking up the different breast cancer morphologies namely mass, pathological calcifications, asymmetry, and distortion was assessed. Tissue biopsy and pathology were used as the standard reference. The study included 123 female patients with 134 proved carcinoma. All patients underwent digital mammogram (DM) examination scanned with artificial intelligence algorithm.ResultsAI achieved higher sensitivity than mammography in detecting malignant breast lesions. The sensitivity of AI was 96.6%, and false negative rate was 3.4%, while mammography sensitivity was 87.3% and false negative rate 12.7%. Our study showed AI performed better than mammography in detecting ductal carcinoma in situ and invasive lobular carcinoma with sensitivity (100% and 96.6%) vs (88.9% and 82.2%) respectively. AI was more sensitive to detect cancers presented with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% and asymmetry and distortion 100% vs 84.6%, than mammography.ConclusionsAI showed potential values to overcome mammographic limitations in the detection of breast cancer even those with challenging morphology as invasive lobular carcinoma, ductal carcinoma in situ, tubular carcinoma and micropapillary carcinoma.

Abstract 3380: Seven autoantibody panel, validated in two independent patient serum collections, can detect women with DCIS and invasive breast cancer

Abstract Mammography is essential to identify early breast cancer but the sensitivity is dependent on breast characteristics. A serum-based biomarker used along with mammography to identify lesions that need to be biopsied may improve the sensitivity of mammography and reduce the number of biopsies needed. We have identified a panel of autoantibodies found in early breast cancer. An autoantibody biomarker is ideal because antibody immunity can be detected with very low levels of antigen with direct antigen recognition by B cells resulting in clonal amplification of antigen specific plasma cells. Work in our laboratory has identified tumor-associated proteins present in pre-malignant tumors that are necessary for survival of human breast cancer cells across breast cancer subtypes. Increased autoantibodies to seven of these early tumor-associated proteins (PDIA6, KRT8, SERBP1, ARPC2, RRM2, AURKA, and NDC80) were present in the sera of women with DCIS and invasive breast cancer in two independent serum collections but not in control women with no known breast lesions. The presence of autoantibodies in the discovery set was evaluated in 185 individuals: 42 control women, 12 women with hyperplasia, 36 patients with fibroadenoma, 59 patients with ductal carcinoma in situ (DCIS), and 36 patients with invasive breast cancer (IBC). The validation set was evaluated in 228 individuals: 50 control patients, 50 patients with fibroadenoma, 18 patients with hyperplasia, 50 patients with DCIS, and 60 patients with IBC, 20 with hormone receptor positive (HR+) HER2 negative, 20 with HER2+, and 20 triple negative (TN). We defined a positive autoantibody response as over 2 standard deviations above the mean found in control women. All seven autoantibodies could predict patients with hyperplasia, fibroadenoma, DCIS, and IBC in both the discovery and validation sets. For example, the seven-antibody panel could identify DCIS from control women with AUC of 0.95 (95% CI 0.912 to 0.995, p<0.0001) and validation set AUC 0.63 (95% CI 0.52 to 0.74, p=0.026). The seven-antibody panel could identify IBC from control women with AUC of 0.85 (95% CI 0.73 to 0.97, p<0.0001) and validation 0.70 (95% CI 0.60 to 0.79, p<0.0001). Furthermore, the seven-antibody panel could identify women with each of the breast cancer subtypes as compared to control women. Women with HR+HER2- breast cancer could be detected with AUC 0.69 (95% CI 0.55 to 0.83 p=0.014), women with HER2+ breast cancer could be detected with AUC 0.69 (95% CI 0.52 to 0.90, p=0.02), and women with triple negative breast cancer could be detected with AUC 0.69 (95% CI 0.55 to 0.83, p=0.002) in the validation set. Future studies will evaluate this panel in a prospective study at the time of mammography to identify a serum biomarker that will improve sensitivity of mammography. Citation Format: Sasha Elizabeth Stanton, Jason Schlumbohm, Erik Ramos, Charles Drescher, Jeffrey Marks, Mary L. Disis. Seven autoantibody panel, validated in two independent patient serum collections, can detect women with DCIS and invasive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3380.

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

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

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice

ObjectiveLegislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. MethodsThis institutional review board–approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. ResultsWe identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). DiscussionA DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.

Association Between Lifestyle Changes, Mammographic Breast Density, and Breast Cancer.

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

Sensitivity and specificity of screening mammography without clinical breast examination among Japanese women aged 40-49years: analysis of data from the J-START results.

Current breast cancer screening programs worldwide offer only mammography for asymptomatic women, without clinical breast examination (CBE). The sensitivity and specificity of true asymptomatic screening mammography are important, especially in Japan. This study used data from the Japan Strategic Anti-cancer Randomized Trial (J-START) results, and then calculated the sensitivity and specificity of screening mammography without CBE among Japanese women aged 40-49years. The sensitivity and specificity of modern film mammography, performed biennially, among Japanese women aged 40-49years were 71.7% and 92.6%, respectively. The sensitivity of mammography alone was 47.4%. From the secondary analysis, the sensitivity of mammography alone was 44.1% for women with dense breasts and 34.8% for women with non-dense breasts. The sensitivity of mammography alone of 47.4% is too low. The low sensitivity of screening mammography for true asymptomatic Japanese women aged 40-49years places this population at an elevated risk, which can justify performing adjunctive ultrasonography.

Role of magnetic resonance imaging in detection of metastatic axillary lymph nodes in breast cancer patients

Background: Magnetic resonance imaging has been evolved as a very important tool in diagnosing the axillary lymph nodes pre-operatively. In recent studies, in women with various risk profiles, the sensitivity ranges between 81% and 100%, which is approximately twice as high as the sensitivity of mammography. Methods: This prospective study was performed at PGIMS, Haryana from December 2018 to March 2020. Total 30 patients (n=30) were included in the study. All the patients presenting with clinically palpable breast lump, underwent triple assessment test. All the patient underwent modified radical mastectomy and the final histopathological report of the specimen was then compared with the preoperative clinical and MRI assessment of the metastatic axillary lymph nodes. Results: This study aimed at detection of axillary lymph node metastasis in breast malignancies by breast MRI. The sensitivity of this MRI came to be about 80.00%, specificity of about 80%, positive predictive value of about 95.24%, negative predictive value of about 44.44% and accuracy of about 80.0% with 95% confidence interval.Conclusions: Magnetic resonance imaging is a very important investigation in detection of breast malignancies, their depth, muscles and skin involvement as well as axillary lymph node detection. Axillary lymph nodes can although be detected by clinical examination, but problem lies in their sensitivity and specificity as axillary lymph nodes of small size cannot be detected by clinical examination.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women.

High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women. We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System. Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times). This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Diagnostic Accuracy of Combining Sonoelastography with Mammography in Solid Breast Lesions Keeping Histopathology as Gold Standard

<p><strong>Background and objective:</strong> Breast cancer is the most common malignancy in females. Early diagnosis can help in reducing the morbidity and mortality associated with this disease. Sonoelastography has shown to be a valuable tool in early detection of breast cancer. The aim of the study was to evaluate the diagnostic accuracy of combining sonoelastography with mammography in diagnosing malignant solid breast lesions taking histopathology as a gold standard.<br /><strong>Methods:</strong> A descriptive cross-sectional study was carried out at the Department of Radiology, Shaikh Zayed Medical Complex/Federal Post-Gradaute Medical Institute (FPGMI), Lahore, Pakistan during six months period from 1st September, 2018 to 31st March, 2019. Female patients > 35 years of age and presenting with suspected malignant breast masses were included. Mammography and sonoelastography of the lesions were done. Finally, the patients were referred for tru cut biopsy. Diagnostic accuracy was calculated for mammography, sonoelastography and their combination.<br /><strong>Results:</strong> A total of 200 female patients were enrolled with a mean age of 50.8 (± 8.6) years. Mammographic findings suggestive of malignancy (BI-RADS category IV and V) were present in 96 (48%) cases. Elastography showed finding of malignancy (Score 4 and 5) in 96 (48%) cases. Combined sonoelastography and mammography diagnosed malignancy in 112 (56%) cases. Histopathologically confirmed malignancy was seen in 110 (55%) cases. The sensitivity and specificity of mammography (76.3 % and 77.7%) and sonoelastography (77.3% and 87.7%), were comparable. The combination of mammography and sonoelastography showed a higher sensitivity (90.9%) and specificity (86.6%). The combination showed a diagnostic accuracy of 89% followed by sonoelastography (82%) and mammography (77%) alone.<br /><strong>Conclusion:</strong> Combination of sonoelastography and mammography can significantly enhance the diagnostic accuracy of breast carcinoma. Females may be offered mammography or ultrasonography in combination with sonoelastography for routine testing.</p>

Diagnostic Accuracy of MR Mammography in Comparison with Digital Mammography and Sonomammography

Background. Even though the sensitivity of contrast-enhanced breast magnetic resonance mammography (CE-MRM) is consistently high in the range of 94-100%, conventionally, digital mammography and sonomammography continue as standard imaging modalities for the detection and evaluation of breast disease.
 The objective of the study was to detect additional lesions that go undetected by routine digital mammography and sonomammography using CE-MRM.
 Materials and Methods. In a prospective study, 68 patients who came for screening diagnostic mammogram and had breast lesions of Breast Imaging-Reporting and Data System category 3-6 were evaluated. All patients underwent bilateral digital mammography and targeted high-frequency sonomammography of the primary lesion. Those patients who were thought to possibly have breast cancer and to be candidates for surgical management were offered bilateral CE-MRM.
 Results. In this prospective study, we included 68 patients (mean age - 50.6 years, range - 30-73 years). A total of 74 lesions were evaluated. In detecting these lesions, digital mammography had a sensitivity of 40.0%, specificity of 100% and diagnostic accuracy of 63.5%. CE-MRM sensitivity was found to be 71.7%, specificity - 96.6% and diagnostic accuracy - 83.7%. Among the 27 additional lesions detected by CE-MRM, histopathological evaluation confirmed only 19, indicating the sensitivity of 100%, specificity of 85.4%, positive predictive value of 67.8%, negative predictive value of 100%, diagnostic accuracy of 89.2%.
 Conclusions. The diagnostic accuracy of CE-MRM was found to be 83.7%, with a specificity of 96.6%. CE-MRM detected 19 additional lesions that were undetected by either digital mammography or ultrasonography. CE-MRM is sensitive in detecting additional malignant lesions which are not detected by other imaging modalities.

Ultrasonography increases sensitivity of mammography for diagnosis of multifocal, multicentric breast cancer using 356 whole breast histopathology as a gold standard

AbstractObjectiveThe purpose of this study is to evaluate effectiveness of ultrasonography and mammography for the detection of multifocal, multicentric breast cancers.Materials and MethodsA retrospective study was performed in 356 patients who underwent mastectomy. Preoperative diagnosis of malignant foci by ultrasonography and mammography were reviewed and compared with final pathological reports of the whole breast which was considered a gold standard.ResultsSeventy‐six patients (21.3%) had multifocal, multicentric cancers diagnosed from whole breast histopathology. Overall sensitivity for detection of additional tumour was 25.4% for mammography, 44% for ultrasonography. The sensitivity for combined methods was 50.8% (p < .05).ConclusionUsing mammography and ultrasonography for preoperative diagnosis of breast cancer will miss almost half of cases with multifocal multicentric cancers. To achieve curative outcome when performing breast conserving surgery, preoperative investigation with only mammography and ultrasonography may be inadequate.

A Randomized Trial Comparing Breast Cancer Incidence and Interval Cancers after Tomosynthesis Plus Mammography versus Mammography Alone.

Background Adding digital breast tomosynthesis (DBT) to digital mammography (DM) improves breast cancer screening sensitivity, but how this impacts mortality and other end points is unknown. Purpose To compare interval and overall breast cancer incidence after screening with DBT plus DM versus DM alone. Materials and Methods In this prospective trial (RETomo), women attending screening were randomized to one round of DBT plus DM (experimental arm) or to DM (control arm). All were then rescreened with DM after 12 months (women aged 45-49 years) or after 24 months (50-69 years). The primary outcome was interval cancer incidence. Cumulative incidence up to the subsequent screening round plus 9 months (21- and 33-month follow-up for women aged 45-49 and 50-69, respectively) was also reported. Ductal carcinomas in situ are included. Subgroup analyses by age and breast density were conducted; 95% CIs computed according to binomial distribution are reported. Results Baseline cancer detection was higher in the DBT plu DM arm than DM arm (101 of 13 356 women vs 61 of 13 521 women; relative detection, 1.7 [95% CI: 1.2, 2.3]). The mean age ± standard deviation for the women in both arms was 55 years ± 7. Interval cancer incidence was similar in the two arms (21 vs 22 cancers; relative incidence, 0.97 [95% CI: 0.53, 1.8]). Cumulative incidence remained higher in the DBT plus DM arm in women over 50 (153 vs 124 cancers; relative incidence, 1.2 [95% CI: 0.99, 1.6]), while it was similar in the two arms in women aged 45-49 (36 vs 41 cancers; relative incidence, 0.89 [95% CI: 0.57, 1.4]). Conclusion In women younger than 50 years, the benefit of early diagnosis seemed to be appreciable, while for women over age 50, the higher sensitivity of tomosynthesis plus mammography was not matched by a subsequent reduction in cancers at the next screening examination or in the intervening interval. Clinical trial registration no. NCT02698202 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Ray in this issue.

Evaluation of Different Diagnostic Modalities in Breast Cancer - A Systematic Review

Background: Most common cancer in women that is 32 % of all newly diagnosed cancers is breast cancer. Breast cancer develops when cells of your breast multiply and divide uncontrollably, resulting in a lump of tissue known as a tumor. Screening type of mammography has played a key role in reducing breast cancer mortality. Aim: To evaluate role of three diagnostic modalities in the breast cancer. Methods: The data bases PubMed and other search engines were searched with the key words: breast cancer, mammography, ultrasound, MRI, sensitivity, specificity, from 1999 to 2020. For inclusion and exclusion of studies we screened the most relevant and irrelevant outcomes. Studies having information of breast cancer, mammography, ultrasound, MRI, sensitivity, specificity was included. Meta-analysis were excluded. Results: In total 39 studies with breast cancer the overall mean sensitivity of ultrasound, mammography and MRI were 76.81, 74 and 88.18 respectively. Overall mean specificity of ultrasound, mammography and MRI 79.65, 87 and 75.17 respectively. SD of Sensitivity and Specificity of MRI is 10.57 and 16.27. SD of Sensitivity and Specificity of mammography is 20.92 and 16.43. SD of Sensitivity and Specificity of ultrasound is 23.71 and 20.05. Conclusions: MRI, because of its limitations cannot be used routinely for diagnosing breast cancer. Mammography is readily available screening tool for breast cancer however it has low sensitivity for diagnosis of breast cancer and requires complimentary imaging. Ultrasound can be used as baseline modality as it saves time and safe in all conditions. Keywords: Breast Cancer, Mammography, Ultrasound, MRI, Sensitivity, Specificity

The impact of breast density notification on rescreening rates within a population-based mammographic screening program

BackgroundHigh participation in mammographic screening is essential for its effectiveness to detect breast cancers early and thereby, improve breast cancer outcomes. Breast density is a strong predictor of breast cancer risk and significantly reduces the sensitivity of mammography to detect the disease. There are increasing mandates for routine breast density notification within mammographic screening programs. It is unknown if breast density notification impacts the likelihood of women returning to screening when next due (i.e. rescreening rates). This study investigates the association between breast density notification and rescreening rates using individual-level data from BreastScreen Western Australia (WA), a population-based mammographic screening program.MethodsWe examined 981,705 screening events from 311,656 women aged 40+ who attended BreastScreen WA between 2008 and 2017. Mixed effect logistic regression was used to investigate the association between rescreening and breast density notification status.ResultsResults were stratified by age (younger, targeted, older) and screening round (first, second, third+). Targeted women screening for the first time were more likely to return to screening if notified as having dense breasts (Percentunadjusted notified vs. not-notified: 57.8% vs. 56.1%; Padjusted = 0.016). Younger women were less likely to rescreen if notified, regardless of screening round (all P < 0.001). There was no association between notification and rescreening in older women (all P > 0.72).ConclusionsBreast density notification does not deter women in the targeted age range from rescreening but could potentially deter younger women from rescreening. These results suggest that all breast density notification messaging should include information regarding the importance of regular mammographic screening to manage breast cancer risk, particularly for younger women. These results will directly inform BreastScreen programs in Australia as well as other population-based screening providers outside Australia who notify women about breast density or are considering implementing breast density notification.

Sensitivity of Contrast-Enhanced Breast MRI vs X-ray Mammography Based on Cancer Histology, Tumor Grading, Receptor Status, and Molecular Subtype: A Supplemental Analysis of 2 Large Phase III Studies.

Background:The impact of certain tumor parameters on the sensitivity of imaging tools is unknown. The purpose was to study the impact of breast cancer histology, tumor grading, single receptor status, and molecular subtype on the sensitivity of contrast-enhanced breast magnetic resonance imaging (CE-BMRI) vs X-ray mammography (XRM) to detect breast cancer.Materials and Methods:We ran a supplemental analysis of 2 global Phase III studies which recruited patients with histologically proven breast cancers. The sensitivity of CE-BMRI vs XRM to detect cancer lesions with different histologies, tumor grading, single receptor status, and molecular subtype was compared. Six blinded readers for each study evaluated the images. Results were summarized as the “Mean Reader.” For each reader, sensitivity was defined as the proportion of detected lesions vs the total number of lesions identified by the standard of reference. Two-sided 95% confidence intervals were calculated for within-group proportions, and for the difference between CE-BMRI and XRM, using a normal approximation to the binomial distribution.Results:In 778 patients, 1273 cancer lesions were detected. A total of 435 patients had 1 lesion, 254 had 2 lesions, and 77 had 3 or more lesions. The sensitivity of CE-BMRI was significantly higher compared with XRM irrespective of the histology. The largest difference was seen for invasive lobular carcinoma (22.3%) and ductal carcinoma in situ (19%). Across all 3 tumor grades, the sensitivity advantage of CE-BMRI over XRM ranged from 15.7% to 18.5%. Contrast-enhanced breast magnetic resonance imaging showed higher sensitivity compared with XRM irrespective of single receptor expressions (15.3%-19.4%). The sensitivities for both imaging methods were numerically higher for the more aggressive ER– (estrogen receptor), PR– (progesterone receptor), and HER2+ (human epidermal growth factor receptor 2) tumors. Irrespective of molecular subtype, sensitivity of CE-BMRI was 14.8% to 18.9% higher compared with XRM.Conclusions:Contrast-enhanced breast magnetic resonance imaging showed significantly higher sensitivity compared with XRM independent of tumor histology, tumor grading, single receptor status, and molecular subtype.Trial Registration: ClinicalTrials.gov: NCT01067976 and NCT01104584.

Ultrasound-Guided Needle Biopsy of Suspected Microcalcifications in the Breast.

Background:Sonography plays an important role in characterizing breast masses and in guiding needle core biopsies and wire localizations of suspicious sonographis abnormalities.Objective:The aim of this study was to show the possibilities of high frequency ultrasound devices in the presentation of microcalcifications of the breast, and the use of these possibilities in performing needle biopsy under ultrasound control.Methods:This was a retrospective study conducted from May 2017 to December 2020 on 32 women, 29-86 years of age using mammograms and ultrasound to show suspected microcalcifications (radiological findings of BI RADS 4B and 4C), and needle biopsy led by ultrasound that confirmed the diagnosis of breast cancer. Patients with suspected microcalcifications on mammography that had previously had the diagnosis of breast cancer confirmed in the same or contralateral breast were excluded from the study. Histology results from each core biopsy and surgical excision were reviewed. The positive predictive values of sonography and mammography for this population were calculated, and the sensitivity, specificity, and negative predictive value of sonography were determined. For analysis of the agreement of ultrasound findings with mammography the McNemar x2-test for dependent samples was used.Results:The sensitivity of mammography in the detection of microcalcifications in this study was 100%. The sensitivity of the ultrasound apparatus with a high frequency probe in the detection of microcalcifications after mammography examination in this study was 87.55%, while the specificity was 42.85%.Conclusion:Ultrasonic devices with high-frequency probes enable the display of accumulations of microcalcifications previously verified by mammography, and thus enable the performance of needle biopsy of suspected microcalcifications under ultrasonic control. An alternative is the much more complicated and significantly more expensive stereotactic biopsy under the control of mammography.

Strengths and challenges of the artificial intelligence in the assessment of dense breasts.

High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms. Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.