Breast Cancer Screening Modalities Research Articles

Effectiveness of Breast Ultrasound for Breast Cancer Screening : A Systematic Review

Breast cancer is the most common cancer in Indonesia and has the highest mortality rate in women. Nowadays, the primary prevention is not clear yet to prevent the breast cancer. Secondary prevention with radiological modality is the only option to decrease the mortality of breast cancer. This study aims to identify ultrasound effectiveness and accuracy as a screening modality for early detection of breast cancer. Methods: A systematic review was analyzed using PRISMA without a meta-analysis method on articles published in 2014-2024. Articles searches were conducted using PubMed, Science Direct, and Google Scholar Database. Result: Based on the search results, seven eligible articles met the criteria for analysis. We found consistent evidence in 5 studies that breast cancer screening using ultrasound has high sensitivity (range sensitivity 68.9%-100%) and accuracy (range accuracy 0.687-0.999) compared to mammography alone. Breast ultrasound specificity is lower than mammography, ranging from 22% to 99.9%. The positive predictive value ranges from 4.3%-70%, and the negative predictive value of breast ultrasound ranges from 61.2%-100%. Conclusion: Breast Ultrasound can be an option as a breast cancer screening modality, especially in young women with dense breasts. It can be used in health facilities where mammography is unavailable.

Ultra-Low Frequency Transmitted Ultrasound Breast Imaging vs DBT (Digital Breast Tomosynthesis): A Patient-Reported Outcome Study.

Breast cancer screening remains a challenge in the United States. Many women do not get a mammogram because of pain associated with the exam, radiation exposure, false-positive results, and additional costs. Others who may benefit from annual screening do not qualify because of young age and radiation risk. We hypothesize that a novel volumetric transmitted breast ultrasound, Quantitative Transmission (QT) Scan may encourage more women to have annual breast cancer screening. Assessing results from patient-reported outcomes (PROs) may predict the value of newer, more desirable screening technologies. Pre- and post-menopausal women who qualified for breast cancer screening were enrolled in a prospective trial of Quantitative Transmission (QT Scan) vs traditional mammography via Digital Breast Tomosynthesis (DBT) Clinical Trials.gov NCT03052166. These women completed questionnaires to assess their experiences with QT Scan and DBT. Associations between QT Scan or DBT and differences in PRO scores were examined. A total of 430 subjects completed the PRO instrument analyzed. A total of 36 questions were asked, 34 were paired for both QT and DBT and two were asked regarding exclusively the QT Scan. Physical discomfort, perceptions of safety and low efficacy, false-positive results and additional out-of-pocket expenses were concerns identified as highest risk for opting out of screening mammography (differences between 2.1 and 2.9 indicate significant differences between means and standard deviations using the Cohen's d statistic). Student's T-test shows a significance level of <10 -10. Statistically significant differences in PROs between QT Scan and DBT were observed by 14 of the 17 paired experience questions (p<0.001). Significant differences in PROs were found between QT scan and DBT, indicating women have significantly more negative experiences with traditional mammography via DBT and are less inclined to undergo screening mammography. Identification of PRO differences could be used to help identify a more desirable breast cancer screening modality.

Comparing Personal Risk Communication with Generic Information on Breast Cancer Screening Attendance: A Randomized Controlled Trial

Objective: This study sought to discern the differential impacts of communicating individualized breast cancer risk versus disseminating generic breast cancer information on women's proclivity towards breast cancer screenings. Method: A structured experimental design was used, including 300 female volunteers aged between 40-69, who had not previously been diagnosed with breast cancer, in Erzincan. Data collection entailed a face-to-face administration of a 22-item questionnaire complemented by the ASSISTS scale. Participants were randomized into two conditions: one receiving a standardized briefing on breast cancer screening modalities, and the other being apprised of their lifetime risk of breast cancer development based on the modified Gail model. Results: Post-intervention, 55.8% (n=72) of the risk-informed cohort and 40.5% (n=41) of the generic information cohort expressed a willingness to undergo screening (p=0.022). Notably, the group informed of their personalized risk exhibited a more pronounced uptick in requests for breast self-examination, clinical breast examination, and mammography screening compared to their counterparts. Conclusion: The communication of personalized breast cancer risk proves to be a more efficacious and time-efficient strategy than generic information dissemination in bolstering screening participation rates.

SIFT-DBT: SELF-SUPERVISED INITIALIZATION AND FINE-TUNING FOR IMBALANCED DIGITAL BREAST TOMOSYNTHESIS IMAGE CLASSIFICATION.

Digital Breast Tomosynthesis (DBT) is a widely used medical imaging modality for breast cancer screening and diagnosis, offering higher spatial resolution and greater detail through its 3D-like breast volume imaging capability. However, the increased data volume also introduces pronounced data imbalance challenges, where only a small fraction of the volume contains suspicious tissue. This further exacerbates the data imbalance due to the case-level distribution in real-world data and leads to learning a trivial classification model that only predicts the majority class. To address this, we propose a novel method using view-level contrastive Self-supervised Initialization and Fine-Tuning for identifying abnormal DBT images, namely SIFT-DBT. We further introduce a patch-level multi-instance learning method to preserve spatial resolution. The proposed method achieves 92.69% volume-wise AUC on an evaluation of 970 unique studies.

Abstract 6078: Circulating tumor cell-based early detection of breast cancer

Abstract Although mammograms have been a useful tool since the first half of the 20th century and have an acceptable sensitivity (87%) in the general population, that drops to &amp;lt;50% in women with dense breasts making it an ineffective screening tool in this population. Cell free DNA (cfDNA) has proven to be an inadequate alternative in this population, and while MRI is acceptable, it is expensive. Astrin Biosciences has developed a blood-based assay that detects circulating tumor cells (CTCs) at a high sensitivity to serve as an alternative screening method to mammography in breast cancer early detection, specifically in women with high-density breasts. Astrin’s technology integrates holographic imaging, machine learning, and microfluidics to identify CTCs in the blood stream. Omics analysis can then be performed on the collected cells to further differentiate patients with cancer from matched healthy controls. Astrin has initiated a small cohort study to investigate the sensitivity and specificity of CTC detection in women with early-stage breast cancer. Astrin’s small cohort study, including patients with ductal carcinoma in situ, stage 1, and stage 2 breast cancer patients, demonstrated &amp;gt; 85% sensitivity and specificity. A large cohort study is being conducted to validate these preliminary results. The small cohort study demonstrates the ability of Astrin Bioscience’s platform to detect cancer cells at an early stage, particularly in breast cancer. This technology will drastically increase the effectiveness of breast cancer screening modalities available to patients. Additionally, since it is a blood-based assay, it can be performed at shorter intervals in high-risk populations than mammograms which require a full patient appointment. Citation Format: Justin M. Drake, Kaylee J. Kamalanathan, Catalina Galeano-Garces, Nathaniel Bristow, Nicholas Heller, Mahdi Ahmadi, Olivia Hedeen, Alexa Hesch, Grant Schaap, Joel Hapke, Jeff Miller, Ivo Babris, Tuan Le, Tony Clacko, Jiarong Hong, Badri Konety, Jayant Parthasarathy. Circulating tumor cell-based early detection of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6078.

Case Presentation where MRI shows Superiority as A Modality for Breast Cancer Screening

Background: Common modalities for breast cancer screening include regular clinical breast exams (73%), annual breast sonograms (73%) for high-risk groups, and mammograms (71.5%) recommended every 3 years for high-risk groups. Despite a higher sensitivity of 89.4%, MRI is underutilized, especially among high-risk groups with the means for the test. Kenyan guidelines recommend mammography for normal-risk populations, omitting MRI for routine screening in average-risk populations. This study explores an intriguing case of a 60-year-old post-menopausal lady, with no hormone replacement history, three parities, and a smoking habit. She presented with a left-sided benign cyst but revealed an ominous, undetected lesion on the right breast, emphasizing the limitations of conventional screening modalities. Method: The patient was admitted for abdominal pain due to gastroenteritis and a breast lump on the left breast for 3 months with a strong family history of breast cancer, so she requested screening for the same. In the process, we found axillary and mediastinal lymph nodes, but the left breast showed a simple harmless cystic lesion. The right breast confirmed a lesion with irregular borders that looked suspicious, and a biopsy confirmed ductal carcinoma of the right breast. Results: The right breast that had no symptoms or signs, and no abnormality on mammogram, ultrasound, or CT scan, had a grade 3a Ductal Cancer in its initial stage that was seen on a simultaneous MRI. Conclusion: MRI could be a better choice for screening early breast cancer in high-risk groups and in those who can afford the test.

DOTnet 2.0: Deep learning network for diffuse optical tomography image reconstruction

Breast cancer is the most common cancer worldwide. The standard imaging modality for breast cancer screening is X-ray mammography, which suffers from low sensitivities in women with dense breasts and can potentially cause cancers despite a low radiation dosage. Diffuse Optical Tomography (DOT) is a noninvasive imaging technique that can potentially be employed to improve breast cancer early detection. However, conventional model-based algorithms for reconstructing DOT images usually produce low-quality images with limited resolution and low reconstruction accuracy. We propose to integrate deep neural networks (DNNs) with the conventional DOT reconstruction methods. This hybrid framework significantly enhances image quality. The DNNs have been trained and tested with sample data derived from clinically relevant breast models. The sample dataset contains blood vessel structures from breast structures and artificially created vessels using the Lindenmayer-system algorithm. By comparing the hybrid reconstruction with the ground truth image, we demonstrated a multi scale - structural similarity index measure (MS-SSIM) score of 0.80–0.90. Whereas using conventional reconstruction, MS-SSIM provided a much inferior score of 0.36–0.59. In terms of DOT image quality, both qualitative and quantitative assessments of the reconstructed images signify that the hybrid approach is superior to conventional methods. This improvement suggests that DOT can potentially become a viable alternative to breast cancer screening, providing a step towards the next-generation device for optical mammography.

PCOS Detection and Breast Cancer Detection

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder that affects people with ovaries, typically during their reproductive years. Detecting PCOS is crucial for timely intervention and management. PCOS detection involves a multifaceted approach, encompassing both clinical and laboratory methods. Clinical assessment of PCOS often begins with a comprehensive medical history, physical examination, and evaluation of symptoms, such as irregular menstruation, excess hair growth, and acne. Transvaginal ultrasound is commonly employed to visualize the ovaries and identify the characteristic cysts. Blood tests, measuring hormone levels like testosterone, luteinizing hormone (LH), and follicle-stimulating hormone (FSH), aid in the diagnosis. Additionally, insulin resistance and glucose tolerance tests may be conducted, as PCOS is frequently associated with metabolic disturbances. Breast Cancer Detection: Breast cancer is a significant global health concern, and early detection is essential for improved prognosis and survival rates. Various techniques are employed for breast cancer detection, combining clinical assessment, imaging, and biopsy procedures. Clinical breast examination involves a physical assessment by a healthcare professional to identify any palpable lumps, changes in breast shape, or skin abnormalities. Mammography, a low-dose X-ray, is a primary imaging modality for breast cancer screening. Other imaging techniques, such as ultrasound and magnetic resonance imaging (MRI), are used for supplemental evaluation. In cases where abnormalities are detected, a biopsy is performed to confirm the presence of cancer cells. Biopsies may include fine-needle aspiration, core needle biopsy, or surgical biopsy, providing definitive evidence of malignancy. Advances in technology, such as digital mammography and the development of artificial intelligence algorithms for image analysis, continue to enhance the precision and efficiency of breast cancer detection, thereby contributing to early diagnosis and effective treatment.

3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net

Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of 93.65%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.65\\%$$\\end{document} with 8.6 false positives is achieved on unseen test data at best.

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps.

microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model's ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

Breast Cancer After Reduction Mammoplasty: A Population-Based Analysis of Incidence, Treatment and Screening Patterns.

The risk of breast cancer may be decreased in women who undergo reduction mammoplasty. The purpose of this study was to describe the incidence and treatment of breast cancer after reduction mammoplasty and to better understand the use of breast cancer screening modalities in these patients. This population-based retrospective analysis utilized the Discharge Abstract Database held by the Canadian Institute for Health Information and the National Ambulatory Care Reporting System to identify all women aged 20 years or older who underwent reduction mammoplasty in Alberta, Canada. The incidence and treatment of breast cancer were compared among patients who underwent reduction mammoplasty and age-sex-matched controls. Imaging utilization, including the use of mammography, ultrasound, and breast biopsy, was also compared. Between 2003 and 2007, 8021 patients over 20 years old underwent reduction mammoplasty in Alberta. Patients were followed for an average of 12.6 years. Eighty-nine (1.1%) patients who underwent reduction mammoplasty developed breast cancer after surgery, compared to 453 (1.9%) controls (P < 0.0001). Among patients diagnosed with breast cancer, there was no difference in patient and tumor characteristics. Women who underwent reduction mammoplasty were more likely to undergo mastectomy for cancer (41.6% vs 1.5%; P < 0.0001) and were more likely to undergo mammography (66.7% vs 58.7%; P < 0.0001), ultrasound (29.2% vs 26.2%; P < 0.0001) and biopsy for benign disease (7.2% vs 6%, P < 0.0001) compared to controls. Despite an increased frequency of breast cancer screening, the incidence of breast cancer is lower after reduction mammoplasty compared with women who did not undergo breast reduction. After a diagnosis of breast cancer, surgical treatment patterns differ between groups, whereby mastectomy is more common after reduction mammoplasty.

Economic evaluation of supplemental breast cancer screening modalities to mammography or digital breast tomosynthesis in women with heterogeneously and extremely dense breasts and average or intermediate breast cancer risk in US healthcare

ObjectiveTo evaluate the cost-effectiveness of supplemental breast imaging modalities for women with heterogeneously and extremely dense breasts and average or intermediate risk of breast cancer (BC) in the USA, and analyze capacity requirements for supplemental magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM).MethodsClinical and economic outcomes for supplemental imaging modalities including full- and abbreviated-protocol MRI (Fp-MRI, Ab-MRI), CEM, and ultrasound (U/S) as add-on to x-ray mammography (XM) or digital breast tomosynthesis (DBT), were compared to XM or DBT alone, in a decision tree linked to a Markov chain validated by comparison with a microsimulation analysis. A Delphi panel supplemented model input parameters from the literature. A capacity model evaluated the number of additional daily scans and scanners required for Fp-MRI and CEM.ResultsCompared to XM or DBT alone, all supplemental imaging protocols were cost-effective. Both Fp- and Ab-MRI, and to a lesser extent CEM and U/S, yielded superior clinical outcomes to XM or DBT. Compared to XM alone, U/S and Ab-MRI had the lowest incremental cost-effectiveness ratios (ICER). For U/S, the ICER was $23,394 for the average-risk population and $13,241 for the intermediate-risk population. For CEM, the ICER was $38,423 and $23,772, respectively. For the extremely dense subpopulation with intermediate risk, supplemental screening requirements could be accommodated by conducting one Fp-MRI scan per day per existing general scanner.ConclusionsWhile ultrasound had the lowest ICER, MRI and CEM demonstrated the best clinical outcomes, compared to XM or DBT alone for women with dense breasts and intermediate and high risk. Existing MRI scanner capacity has the potential to meet most of the supplemental screening needs of this population.

Barriers to breast cancer screening in a developing middle eastern country: A nationwide survey.

e22517 Background: Breast cancer imposes a significant global health burden not only due to being constantly ranked as the leading malignancy in females, but also due to the immense resulting morbidity and mortality. Despite the presence of screening modalities, the uptake is relatively low due to unidentified barriers in many countries and regions. The Identification of these barriers towards screening is of paramount importance to tackle the health burden imposed by breast cancer, especially in countries like Jordan, a developing middle eastern country. Methods: A self-reporting survey consisting of comprehensive questions regarding the participant’s demographics, medical history, knowledge about breast cancer and the screening modalities was distributed to females aged 25 years and above with no prior history of breast cancer who are residing across the different regions of Jordan. The first consecutive 790 participants were enrolled, and data analysis was performed to identify the key barriers. Results: The mean age of the enrolled study population was 41 ± 9 years old with the majority being married (82.8%), have underwent a higher level of education (86.5%), are non-smokers (75.2%), and are medically insured (72.4%). 209 participants (26.5%) had reported a family history of breast cancer, while 83.4% of the participants reported awareness about breast cancer screening modalities with breast self-examination being the most commonly known modality of screening in 39.2% of the participants. Only 22.4% of the participants were aware of the 3 available screening methods. The majority of the participants (57.1%) did not undergo previous screening. When the participants who did not undergo previous breast cancer screening were compared with those who did, the participants without prior screening were more likely to report the lack of knowledge towards screening modalities (27.7% vs 8.0%) (p-value &lt; 0.001), fear of the results (22.6% vs 13.9%) (p-value = 0.002), fear of pain during or after from the test (10.4% vs 5.9%) (p-value = 0.024), and feeling of embarrassment provoked by the screening test (15.7% vs 6.5%) (p-value &lt; 0.001) as key barriers. On the contrary, there was no statistically significant difference between the two groups when assessing financial aspect of screening uptake as a potential barrier (p-value = 0.860). Conclusions: Despite the considerably high percentage of participants being aware of breast cancer screening modalities, an alarmingly low rate of screening uptake was witnessed due to several barriers. The collective identification of lack of knowledge towards screening modalities and the nature of the available tests, in addition to the fear of the results and pain following the test, and feeling of embarrassment undoubtedly mandates the formulation of comprehensive awareness campaigns that effectively tackle these barriers.

The usefulness of addition of breast-specific gamma imaging to mammography in women with dense breast

BackgroundMammography (MG) has been adopted as a screening modality for breast cancer. However, the diagnostic yield was reported to decrease in women with dense breasts in MG. Several modalities have been introduced to improve the drawbacks. Breast-specific gamma imaging (BSGI) is a new technique in nuclear medicine imaging that could support breast cancer diagnosis. The aim of this study was to evaluate whether the addition of BSGI according to MG category could improve the accuracy of diagnosis and reduce unnecessary studies or biopsies.ResultsFrom February 2013 to December 2018, 548 patients with 628 breast lesions were enrolled in this retrospective study. The performances of BSGI and MG were evaluated for detecting breast cancer. We classified subgroups by adding the results of BSGI for BI-RADS category 0 and 4a lesions on MG. For each subgroup, diagnostic performance was calculated in overall and dense/non-dense. Factors associated with false-negative BSGI were evaluated. The sensitivity of BSGI (88.26%) was comparable to that of MG (87.95%) (P > 0.05). Specificity (81.44%) and AUC (0.85) of BSGI were significantly superior to those of MG (66.83% and 0.77, respectively). In the subgroup analysis of BSGI plus MG, the sensitivity of BSGI + MG0 and BSGI + MG4a were 95.98% and 94.64%, respectively. And specificities were 69.80% and 77.23%, respectively. Sensitivity and AUC of subgroups increased significantly compared to those of MG alone in overall and dense breasts. A nodule ≤ 10 mm and a low Ki-67 showed significant association with the false negativity of BSGI.ConclusionsApplying BSGI to MG, notably for breast lesions with BI-RADS category 0 or 4a, could improve the diagnostic performance, even in dense breasts.

Meta-Analysis of Supplemental Breast Cancer Screening Modalities in Women with Dense Breasts and Negative Mammography.

HomeRadiology: Imaging CancerVol. 5, No. 3 PreviousNext Research HighlightsMeta-Analysis of Supplemental Breast Cancer Screening Modalities in Women with Dense Breasts and Negative MammographyEvelyn Tran, Kimberly RayEvelyn Tran, Kimberly RayEvelyn TranKimberly RayPublished Online:May 5 2023https://doi.org/10.1148/rycan.239012MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Highlighted ArticleHussein H, Abbas E, Keshavarzi S, et al. Supplemental breast cancer screening in women with dense breasts and negative mammography: a systematic review and meta-analysis. Radiology 2023;306(3):e221785. Link, Google ScholarArticle HistoryPublished online: May 05 2023 FiguresReferencesRelatedDetailsRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 5, No. 3 Metrics Altmetric Score PDF download

Abstract 3029: Barriers to optimal breast cancer screening modalities for high-risk patients in the primary care setting

Abstract Background: Annual screening breast MRIs are endorsed by current breast cancer screening guidelines as an adjunctive screening modality for high-risk patients (defined by more than 20% lifetime risk) in addition to a screening mammogram. Recent studies showed screening breast MRI remained underutilized despite the current recommendations. On the other hand, validated breast cancer risk models for identifying high-risk patients also remained under-recognized among primary care providers. This study aims to identify the potential barriers for primary care providers (PCPs) to using breast cancer risk models and referring high-risk patients to breast MRI for screening. Methods: An anonymous survey was distributed via email to the PCPs and OB/GYN providers in the network of Mount Auburn Hospital, a community teaching hospital affiliated with Harvard Medical School in Cambridge, Massachusetts. The survey included questions regarding breast cancer screening in average-risk and high-risk patients, providers’ understanding of breast cancer risk models, and current referral status of breast MRIs and high-risk/genetic clinics. Results: The survey was sent to 104 providers, and 43 responses were received. When asked about performing breast cancer risk assessments, 58% reported “Always (91-100%)” and 33% said “Often (71-90%)”. For factors to consider when performing risk assessments, most of the providers considered personal medical history (98%), family history (100%), genetic syndrome history (86%), and previous breast biopsy results (90%). When asked about breast cancer risk models, 58% of the providers had not heard of or used one. 33% of the providers have used the Gail model, and 26% have used the Tyrer-Cuzick model. 51% of the providers correctly identified high-risk as lifetime risk &amp;gt;20%. 56% correctly answered annual mammograms with MRI for breast cancer screening for high-risk patients. The majority of the providers (84%) would prefer to refer patients to the high-risk clinic for risk assessment and screening management. For barriers to referring to MRI, the insurance approval process (49%) was reported to be the largest barrier. 72% of the providers prefer not ordering it and defer to the high-risk/genetic clinic for ordering the test. Conclusions: Our survey has shown different barriers to breast cancer screening in high-risk patients in the primary care setting, including knowledge and logistic barriers. Further education for PCPs is needed for correctly identifying high-risk patients, utilizing risk models, and referring high-risk patients for breast MRI screening. Citation Format: Yu Jen Jan, Prudence B. Lam. Barriers to optimal breast cancer screening modalities for high-risk patients in the primary care setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3029.

A Clinical Prediction Model for Breast Cancer in Women Having Their First Mammogram

Digital mammography is the most efficient screening and diagnostic modality for breast cancer (BC). However, the technology is not widely available in rural areas. This study aimed to construct a prediction model for BC in women scheduled for their first mammography at a breast center to prioritize patients on waiting lists. This retrospective cohort study analyzed breast clinic data from January 2013 to December 2017. Clinical parameters that were significantly associated with a BC diagnosis were used to construct predictive models using stepwise multiple logistic regression. The models' discriminative capabilities were compared using receiver operating characteristic curves (AUCs). Data from 822 women were selected for analysis using an inverse probability weighting method. Significant risk factors were age, body mass index (BMI), family history of BC, and indicated symptoms (mass and/or nipple discharge). When these factors were used to construct a model, the model performance according to the Akaike criterion was 1387.9, and the AUC was 0.82 (95% confidence interval: 0.76-0.87). In a resource-limited setting, the priority for a first mammogram should be patients with mass and/or nipple discharge, asymptomatic patients who are older or have high BMI, and women with a family history of BC.

Machine learning based Breast Cancer screening: trends, challenges, and opportunities

ABSTRACT Although breast cancer (BC) deaths have decreased over time, it remains the second leading cause of cancer-related deaths among women. With the technical advancement of artificial intelligence (AI) and availability of healthcare data, many researchers have attempted to employ machine learning (ML) techniques to gain a better understanding of this disease. The present study was a systematic literature review (SLR) of the use of machine learning techniques in breast cancer screening (BCS) between 2011 and 2021. A total of 66 papers were selected and analysed to address nine criteria: year of publication, publication venue, paper type, BCS modality, empirical type, ML technique, performance, advantages and disadvantages, and dataset used. The results showed that mammography was the most frequently used BCS modality, and that classification was the most used ML objective. Moreover, of the six investigated ML techniques, convolutional neural network models scored the highest median accuracy with 96.67%, followed by adaptive boosting (88.9%).

Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis.

Background The best supplemental breast cancer screening modality in women at average risk or intermediate risk for breast cancer with dense breast and negative mammogram remains to be determined. Purpose To conduct systematic review and meta-analysis comparing clinical outcomes of the most common available supplemental screening modalities in women at average risk or intermediate risk for breast cancer in patients with dense breasts and mammography with negative findings. Materials and Methods A comprehensive search was conducted until March 12, 2020, in Medline, Epub Ahead of Print and In-Process and Other Non-Indexed Citations; Embase Classic and Embase; Cochrane Central Register of Controlled Trials; and Cochrane Database of Systematic Reviews, for Randomized Controlled Trials and Prospective Observational Studies. Incremental cancer detection rate (CDR); positive predictive value of recall (PPV1); positive predictive value of biopsies performed (PPV3); and interval CDRs of supplemental imaging modalities, digital breast tomosynthesis, handheld US, automated breast US, and MRI in non-high-risk patients with dense breasts and mammography negative for cancer were reviewed. Data metrics and risk of bias were assessed. Random-effects meta-analysis and two-sided metaregression analyses comparing each imaging modality metrics were performed (PROSPERO; CRD42018080402). Results Twenty-two studies reporting 261 233 screened patients were included. Of 132 166 screened patients with dense breast and mammography negative for cancer who met inclusion criteria, a total of 541 cancers missed at mammography were detected with these supplemental modalities. Metaregression models showed that MRI was superior to other supplemental modalities in CDR (incremental CDR, 1.52 per 1000 screenings; 95% CI: 0.74, 2.33; P < .001), including invasive CDR (invasive CDR, 1.31 per 1000 screenings; 95% CI: 0.57, 2.06; P < .001), and in situ disease (rate of ductal carcinoma in situ, 1.91 per 1000 screenings; 95% CI: 0.10, 3.72; P < .04). No differences in PPV1 and PPV3 were identified. The limited number of studies prevented assessment of interval cancer metrics. Excluding MRI, no statistically significant difference in any metrics were identified among the remaining imaging modalities. Conclusion The pooled data showed that MRI was the best supplemental imaging modality in women at average risk or intermediate risk for breast cancer with dense breasts and mammography negative for cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hooley and Butler in this issue.

Thermography as a Breast Cancer Screening Technique: A Review Article.

Globally, breast cancer is the most frequently occurring cancer in women and is the reason for more disability-adjusted life years lost than any other type of cancer. Hence, early screening plays a vital role in reducing breast cancer mortality. Although mammography is the standard procedure used for screening and diagnosis of breast cancer, it still has some limitations. Other methods used for screening include ultrasound and clinical breast examination. Despite its limitations, mammography is the gold standard for screening breast malignancy. Another emerging method for screening is thermography. With recent technological advances, breast cancer screening through thermography has demonstrated several advantages over existing modalities. For this review, a literature search was performed using databases such as PubMed, Google Scholar, and ScienceDirect. The keywords searched included breast cancer, early detection, breast cancer screening, mammography, and thermography. This review discusses the benefits of thermography showing that it can be a significant modality for breast cancer screening. The recent developments in thermal sensors, imaging protocols, and computer-aided software diagnostics hold great promise for making this technique a mainstream screening method for cancer. Moreover, the use of artificial intelligence and thermal imaging to detect early-stage breast cancer can provide impressive results. Therefore, thermography will be a promising technology for the early detection of breast cancer.