Early Breast Cancer Screening Research Articles

Exploring research progress in studying serum exosomal miRNA-21 as a molecular diagnostic marker for breast cancer.

Breast cancer is one of the most prevalent malignancies affecting women globally and poses a significant public health challenge. Early clinical detection plays a pivotal role in providing optimal treatment opportunities and favorable prognoses, crucial for reducing breast cancer mortality and enhancing patients' quality of life. Therefore, the timely identification and diagnosis of breast cancer are imperative. Conventional tumor markers, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 15-3 (CA15-3), serve as reliable methods for actively monitoring disease progression and have become a routine auxiliary diagnostic approach in clinical settings. However, these biomarkers exhibit limitations in sensitivity and specificity, particularly in the early screening and diagnosis of tumors, often yielding results inconsistent with clinical manifestations. In recent years, research has increasingly focused on exosomes released by tumor cells as potential new biomarkers for early stage breast cancer screening. Exosomes carry various components, including tumor-derived proteins, nucleic acids, and lipids. This paper delves into the specific utilization of serum exosomal microRNA-21 (miR-21) as a biomarker for early detection and diagnosis of breast cancer, evaluating its efficacy within this framework.

Auto-BCS: A Hybrid System for Real-Time Breast Cancer Screening from Pathological Images.

Breast cancer is recognized as a prominent cause of cancer-related mortality among women globally, emphasizing the critical need for early diagnosis resulting improvement in survival rates. Current breast cancer diagnostic procedures depend on manual assessments of pathological images by medical professionals. However, in remote or underserved regions, the scarcity of expert healthcare resources often compromised the diagnostic accuracy. Machine learning holds great promise for early detection, yet existing breast cancer screening algorithms are frequently characterized by significant computational demands, rendering them unsuitable for deployment on low-processing-power mobile devices. In this paper, a real-time automated system "Auto-BCS" is introduced that significantly enhances the efficiency of early breast cancer screening. The system is structured into three distinct phases. In the initial phase, images undergo a pre-processing stage aimed at noise reduction. Subsequently, feature extraction is carried out using a lightweight and optimized deep learning model followed by extreme gradient boosting classifier, strategically employed to optimize the overall performance and prevent overfitting in the deep learning model. The system's performance is gauged through essential metrics, including accuracy, precision, recall, F1 score, and inference time. Comparative evaluations against state-of-the-art algorithms affirm that Auto-BCS outperforms existing models, excelling in both efficiency and processing speed. Computational efficiency is prioritized by Auto-BCS, making it particularly adaptable to low-processing-power mobile devices. Comparative assessments confirm the superior performance of Auto-BCS, signifying its potential to advance breast cancer screening technology.

USING RESNET18 IN A DEEP-LEARNING FRAMEWORK AND ASSESSING THE EFFECTS OF ADAPTIVE LEARNING RATES IN THE IDENTIFICATION OF MALIGNANT BREAST MASSES IN MAMMOGRAMS

Breast cancer is a prevalent disease that primarily affects women globally, but it can also affect men. Early detection is crucial for better treatment outcomes, and mammography is a common screening method. Recommendations for mammograms vary by age and country. Early breast cancer screening is vital for timely interventions. This paper aims to introduce AI methods through deep learning approaches utilizing pretrained CNN-based models for the diagnosis of masses depicted in breast images. These masses may be either malignant or benign, necessitating distinct management strategies for each scenario. The pretrained model ResNet18 applied to a combined dataset of three datasets (INbreast+MIAS+DDSM) yielded the best estimated result, with an accuracy of 95% (94.90% precision, 94.91% recall and 94.91% F1-score).

Extraction of Informative Features in Mammography Images Based on the LBP Method

Mammography is crucial for early identification and screening of breast cancer. Mammogram images can identify and classify anomalies and explain breast tissue, as well as other useful information. A technique for obtaining informative signs to lower false positives in the detection of breast tumor areas is investigated in this research work. The key problem in this investigation is differentiating the area of the breast tumor from the area of normal tissue. This issue was resolved by using the LBP approach to extract informative characters from the region of interest (ROI) in the mammography images provided in the international mini-MIAS image dataset.

Electrochemical aptasensor fabricated by anchoring recognition aptamers and immobilizing redox probes on bipolar silica nanochannel array for reagentless detection of carbohydrate antigen 15-3.

Timely, convenient, and efficient detection of carbohydrate antigen 15-3 (CA15-3) levels in serum holds significant importance in early screening, diagnostic assistance and prognosis prediction of breast cancer. The development of efficient and convenient electrochemical aptasensors with immobilized redox probes for label-free detection of CA15-3 is highly desirable. In this work, a bipolar silica nanochannel array film (bp-SNA) with two distinct functional domains including nanochannels and an outer surface was employed for the immobilization of recognition ligands and electrochemical redox probes, enabling the construction of a probe-integrated aptasensor for reagentless electrochemical detection of CA15-3. Cost-effective and readily available indium tin oxide (ITO) was used as the supporting electrode for sequential growth of a negatively charged inner layer (n-SNA) followed by a positively charged outer layer (p-SNA). The preparation process of bp-SNA is convenient. Functionalization of amino groups on the outer surface of bp-SNA was modified by aldehyde groups for covalent immobilization of recognition aptamers, further establishing the recognition interface. Within the nanochannels of bp-SNA, the electrochemical redox probe, tri (2,2'-dipyridyl) cobalt (II) (Co(bpy)3 2+) was immobilized, which experienced a dual effect of electrostatic attraction from n-SNA and electrostatic repulsion from p-SNA, resulting in high stability of the immobilized probes. The constructed aptasensor allowed for reagentless electrochemical detection of CA15-3 ranged from 0.001U/mL to 500 U/mL with a low detection limit (DL), 0.13mU/mL). The application of the constructed aptasensor for CA15-3 detection in fetal bovine serum was also validated. This sensor offers advantages of a simple and readily obtainable supporting electrode, easy bp-SNA fabrication, high probe stability and good stability.

Kesadaran Deteksi Dini Kanker Payudara di Komunitas

Breast cancer is one of the most common cancers that affects society, especially women. Cancer can grow in the lobes or glands that produce milk or in the ducts that carry milk from the lobes to the nipple. Breast cancer is the second leading cause of mortality in women. The aim is to describe awareness of early detection or screening of breast cancer in the community. Quantitative research method using observational analytics on secondary data on breast cancer screening at the Sukoharjo Health Center 01 Wonosobo from June to December 2022. The target achievement results carried out by the examination were still low at 24 (27.27%) people out of a total of 3% PKP of 88 people, which was determined by the Wonosobo District Health Service. Examination results showed that 20.83% of women had lumps in their breasts. The conclusion is that public awareness to carry out SADANIS examinations is still low. Future research can innovate to increase public awareness in early detection of breast cancer.

Breast cancer early detection in TP53 SNP protein sequences based on a new Convolutional Neural Network model

INTRODUCTION: Breast cancer (BC) is the most commonly occurring cancer and the second leading cause for women’s disease death. The BC cases are associated with genital mutations which are inherited from older generations or acquired overtime. If the diagnosis is done at the first stage, effects associated with certain treatments can be limited, costs can be saved and the diagnostic time can be minimized. This can also help specialists target the best treatment to increase the rate of cures. Nevertheless, its discovery in patients is very challenging due to silent symptoms aside from the fact the routine screening is not recommended for women under 40 years old.OBJECTIVES: Several efforts are aimed at the BC early detection using machine and deep learning systems. The proposed algorithms use different data types to distinguish between cancerous and non-cancerous cases; as: mammography, ultrasound and MRI (magnetic resonance imaging) images. Then, different learning tools were applied on this data for the classification task. Despite the classification rates which exceed 90%, the major drawback of all these methods is that they are applicable only after the appearance of the cancerous tumors, which reduces the cure rates.METHODS: We propose a new technique for early breast cancer screening. For the data, we focus on cancerous and non-cancerous SNP (Single Nucleotide Polymorphism) protein sequences of the TP53 gene in chromosome 17. This gene is shown to be linked to different single amino acid mutations on which we will shed light here. The method we propose transforms SNP textual sequences into digital vectors via coding. Then, RGB scalogram images are generated using the continuous wavelet transform. A pretreatment of color coefficients is applied to scalograms aiming at creating four different databases. Finally, a CNN deep learning network is used for the binary classification of cancerous and non-cancerous images.RESULTS: During the validation process, we reached good performance with specificity of 97.84%, sensitivity of 96.45%, an overall accuracy of 95.29% and an equal run time of 12 minutes 3 seconds. These values ensure the efficiency of our method.To enhance more these results, we used the ORB feature detection technique. Consequently, the classification rates have been improved to reach 95.9% as accuracyCONCLUSION: Our method will allow significant savings time and lives by detecting the disease in patients whose genetic mutations are beginning to appear.

Breast cancer segmentation using a hybrid AttendSeg architecture combined with a gravitational clustering optimization algorithm using mathematical modelling

Abstract Breast cancer diagnosis relies on breast ultrasound (BUS) and the early breast cancer screening saves lives. Computer-aided design (CAD) tools diagnose tumours via BUS tumour segmentation. Thus, breast cancer analysis automation may aid radiologists. Early detection of breast cancer might help the patients to survive and in context with this many approaches have been demonstrated by different researches, however, some of the works are weak in the segmentation of breast cancer images. to tackle these issues, this study propose a novel Hybrid Attendseg based gravitational clustering optimization (HA-GC) method which is utilized to segment breast cancer as normal malignant, and benign. For this we have taken the dataset known as breast ultrasound (BUS) images. This method constructively segments the breast cancer images. Prior to the segmentation, pre-processing is carried out which can be used to normalize the images incorporated with the removal of unwanted noises and format the images Optimization selects the best qualities. An experiment is conducted and compared the results with the parameters such as Dice coefficient, Jacquard, Precision, and Recall and attained over 90% and ensures the usage of present work in the segmentation of breast cancer images.

A multispectral transmission image cluster analysis method based on “Terrace compression Method” and window function

Multispectral transmission imaging has a great potential for the early screening of breast cancer due to the low cost, safety and ease in operation. Accurate detection of heterogeneity is important for the diagnosis of breast disease. The low contrast and unclear heterogeneity boundaries of transmission images can lead to difficulties in recognition and segmentation. Therefore, we propose a clustering segmentation method of multispectral transmission images based on “terrace compression method” and window transformation. The images are preprocessed by the frame accumulation to improve the signal-to-noise ratio. After that, the “terrace compression method” is used to compress the images nonlinearly, to reduce data redundancy and then to improve the edge information of heterogeneities. Afterwards, the window function is used to eliminate the redundant information of the background and to reduce the influence of background noise on clustering. Finally, the processed images at each wavelength are transformed into multidimensional data for cluster analysis. The multispectral transmission images of breast phantom are acquired for experimental validation. Then, compared the method with common clustering segmentation methods (including K-means, K-means++, Mean-shift, Gaussian Mixture). The results showed that this processing method can effectively segment and classify the three heterogeneities in the breast phantom. Among these methods, the method proposed in the paper has the best segmentation and classification results for the three types of heterogeneities in the breast phantom. The Dice coefficients of all the heterogeneities segmentation reached more than 0.84 and increased by a maximum of 1.08 times as compared to the common clustering methods. The applications of the terrace compression method and the grayscale window transformation improved the effect of image clustering segmentation.

CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis

Background and objectiveComputer-aided detection (CADe) of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) is crucial in the early diagnosis of breast cancer. Although convolutional neural network (CNN)-based detection models have achieved excellent performance in medical lesion detection, they are subject to some limitations in MC detection: 1) Most existing models employ the feature pyramid network (FPN) for multi-scale object detection; however, the rough feature sharing between adjacent layers in the FPN may limit the detection ability for small and low-contrast MCs; and 2) the MCs region only accounts for a small part of the annotation box, so the features extracted indiscriminately within the whole box may easily be affected by the background. In this paper, we develop a novel CNN-based CADe method to alleviate the impacts of the above limitations for the accurate and rapid detection of MCs in DBT. MethodsThe proposed method has two parts: a novel context attention pyramid network (CAPNet) for intra-layer MC detection in two-dimensional (2D) slices and a three-dimensional (3D) aggregation procedure for aggregating 2D intra-layer MCs into a 3D result according to their connectivity in 3D space. The proposed CAPNet is based on an anchor-free and one-stage detection architecture and contains a context feature selection fusion (CFSF) module and a microcalcification response (MCR) branch. The CFSF module can efficiently enrich shallow layers’ features by the complementary selection of local context features, aiming to reduce the missed detection of small and low-contrast MCs. The MCR branch is a one-layer branch parallel to the classification branch, which can alleviate the influence of the background region within the annotation box on feature extraction and enhance the ability of the model to distinguish MCs from normal breast tissue. ResultsWe performed a comparison experiment on an in-house clinical dataset with 648 DBT volumes, and the proposed method achieved impressive performance with a sensitivity of 91.56 % at 1 false positive per DBT volume (FPs/volume) and 93.51 % at 2 FPs/volume, outperforming other representative detection models. ConclusionsThe experimental results indicate that the proposed method is effective in the detection of MCs in DBT. This method can provide objective, accurate, and quick diagnostic suggestions for radiologists, presenting potential clinical value for early breast cancer screening.

Subsequent female breast cancer risk associated with anthracycline chemotherapy for childhood cancer

Anthracycline-based chemotherapy is associated with increased subsequent breast cancer (SBC) risk in female childhood cancer survivors, but the current evidence is insufficient to support early breast cancer screening recommendations for survivors treated with anthracyclines. In this study, we pooled individual patient data of 17,903 survivors from six well-established studies, of whom 782 (4.4%) developed a SBC, and analyzed dose-dependent effects of individual anthracycline agents on developing SBC and interactions with chest radiotherapy. A dose-dependent increased SBC risk was seen for doxorubicin (hazard ratio (HR) per 100 mg m−2: 1.24, 95% confidence interval (CI): 1.18–1.31), with more than twofold increased risk for survivors treated with ≥200 mg m−2 cumulative doxorubicin dose versus no doxorubicin (HR: 2.50 for 200–299 mg m−2, HR: 2.33 for 300–399 mg m−2 and HR: 2.78 for ≥400 mg m−2). For daunorubicin, the associations were not statistically significant. Epirubicin was associated with increased SBC risk (yes/no, HR: 3.25, 95% CI: 1.59–6.63). For patients treated with or without chest irradiation, HRs per 100 mg m−2 of doxorubicin were 1.11 (95% CI: 1.02–1.21) and 1.26 (95% CI: 1.17–1.36), respectively. Our findings support that early initiation of SBC surveillance may be reasonable for survivors who received ≥200 mg m−2 cumulative doxorubicin dose and should be considered in SBC surveillance guidelines for survivors and future treatment protocols.

Breast cancer diagnosis using Orca predation optimization algorithm

Early detection and classification of breast cancer can be facilitated to initiate the most effective treatment. As the second leading cause of death among women, early breast cancer screening is essential for reducing mortality rates. In this context, Convolutional neural networks (CNNs) are the ideal candidate for increasing the rate of identification and classification of tumours with efficiency, particularly in medical imaging. This research proposes a hybridised CNN with the Orca Predation Optimization Algorithm (OPOA) as a novel classification model for the effective detection of abnormalities in breast cancer diagnosis. Specifically, the OPOA technique is used to determine the optimal hyperparameter values for the hybrid CNN architecture being deployed. As the pretrained CNN model, the suggested model utilizeds a ResNet50 residual network. It merged OPOA with the ResNet50 residual network to construct the OPOA-ResNet-50 Architecture. The experimental validation of the proposed OPOA-ResNet-50 model utilising the datasets of curated breast imaging subset of DDSM (CBIS-DDSM) shown improved classification accuracy of 99.04%, specificity of 98.56%, and sensitivity of 97.78% in comparison to the baseline techniques. The results also revealed that the proposed under mammographic image analysis society (MIAS) OPOA-ResNet-50 model demonstrated superior classification accuracy of 98.64%, specificity of 98.79%, and sensitivity of 98.82% compared to the benchmarked methods. The adopted OPOA algorithm is determined to achieve more optimal hyperparameter values for the ResNet50 architecture than the comparative algorithms Improved Marine Predator Optimization Algorithm (IMPOA), Whale Optimization Algorithm (WOA), Harris hawk’s optimization (HHO), and gravitational search algorithm (GSA).

Clinical Value of cfDNA Content in Peripheral Blood of Patients with Triple-Negative Breast Cancer

Objective: To explore the value of circulating free (cfDNA) content in the clinical diagnosis and treatment of triple-negative breast cancer (TNBC). Methods: A total of 39 TNBC patients, 45 non-TNBC patients, and 50 healthy individuals admitted to the Baoding First Central Hospital during 2019–2022 were recruited. The clinical data, peripheral blood cfDNA concentration, and clinicopathological indicators of the patients were observed and analyzed. Results: The difference in clinical indicators such as age, age range, tumor size, clinical stage, and lymph node metastasis between patients with TNBC and non-TNBC was insignificant (P > 0.05). The cfDNA concentrations (ng/mL) of the TNBC group, non-TNBC group, and healthy group were 24.12 ± 4.98, 15.36 ± 4.12, and 3.12 ± 1.02, respectively, and they are statistically different (P < 0.05). The difference in cfDNA concentration was insignificant between TNBC patients with tumors ≤ 2 cm and > 2 cm (P > 0.05) but was significant between TNBC patients with clinical stages I+II and III+IV (P < 0.05). The cfDNA concentration in TNBC patients with lymph node metastasis was significantly higher than those without lymph node metastasis (P < 0.05). Conclusion: cfDNA has an important application value in the diagnosis and treatment of breast cancer. By detecting the cfDNA level and its gene variation, valuable information about the progress and treatment effects of breast cancer can be obtained. This non-invasive detection method has a wide range of applications and can be used for early screening, auxiliary diagnosis, efficacy evaluation, and recurrence monitoring of breast cancer.

Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework.

Breast cancer is one of the most prevalent cancers in women nowadays, and medical intervention at an early stage of cancer can significantly improve the prognosis of patients. Breast ultrasound (BUS) is a widely used tool for the early screening of breast cancer in primary care hospitals but it relies heavily on the ability and experience of physicians. Accordingly, we propose a knowledge tensor-based Breast Imaging Reporting and Data System (BI-RADS)-score-assisted generalized inference model, which uses the BI-RADS score of senior physicians as the gold standard to construct a knowledge tensor model to infer the benignity and malignancy of breast tumors and axes the diagnostic results against those of junior physicians to provide an aid for breast ultrasound diagnosis. The experimental results showed that the diagnostic AUC of the knowledge tensor constructed using the BI-RADS characteristics labeled by senior radiologists achieved 0.983 (95% confidential interval (CI) = 0.975-0.992) for benign and malignant breast cancer, while the diagnostic performance of the knowledge tensor constructed using the BI-RADS characteristics labeled by junior radiologists was only 0.849 (95% CI = 0.823-0.876). With the knowledge tensor fusion, the AUC is improved to 0.887 (95% CI = 0.864-0.909). Therefore, our proposed knowledge tensor can effectively help reduce the misclassification of BI-RADS characteristics by senior radiologists and, thus, improve the diagnostic performance of breast-ultrasound-assisted diagnosis.

Analysis of the morbidity of breast cancer in Ukraine and the city of Kyiv

One of the main causes of mortality worldwide is malignant neoplasms, and in Ukraine, breast cancer ranks first among all cancer diseases and mortality among women. With the help of official epidemiological data, it is possible to estimate the level and forecast the costs of the prevention and treatment of breast cancer in women.
 The purpose of the study was to analyze epidemiological indicators of breast cancer incidence and mortality among the female population in Ukraine and the city of Kyiv.
 The study used data on the incidence and mortality from breast cancer in women of the National Cancer Registry of Ukraine for 2011–2021. Analytical-comparative, epidemiological, graphic, and mathematical-statistical methods were used.
 The results of the analysis of epidemiological indicators showed that the largest number of sick women was registered in 2012 – 17,746, and the smallest in 2020 – 12,736. The highest growth rate of the chain coefficient of decrease/increase in the incidence and mortality rate of breast cancer in women was in 2015 – 23% and 8%, respectively. The lowest number of registered cases of breast cancer for 2016–2020 in Ukraine is observed in the Western region, and the largest in the Northern region. The highest mortality rate was recorded in women in the city of Kyiv and in Ukraine as a whole from breast cancer in 2012 (18.2 and 16.9 per 100,000 population, respectively). In general, the mortality trend in the city of Kyiv is higher than in Ukraine. The results of the analysis showed that breast cancer is most often detected for the first time at the I–II stage of the disease. The largest number of such patients was found in Ukraine in 2015 – 78.6% of the total number of cases.
 Taking into account the analyzed epidemiological data on the incidence and mortality of women from breast cancer in Ukraine and the city of Kyiv, it is necessary to conduct social programs for early screening of breast cancer at the state level and provide such patients with effective anticancer drugs at all stages of treatment.

Method for enhancing transmission image of breast obtained in visible and near-infrared bands

Multispectral transmission imaging is expected to be a new method for early screening of breast cancer. For the transmission images of the breast, which have a very low signal-to-noise ratio, the frame accumulation technique can be used to substantially improve the signal-to-noise ratio, but it will cause data redundancy. Terraced compression method not only reduces the redundancy of data, but also enhances the contrast of the image, so it is well suited for gray-scale compression of image. However, due to factors such as uneven tissue thickness and unevenly distributed illumination, the images contain trend item, which affect the effectiveness of the terraced compression method. Therefore, this paper obtains and eliminates the trend items contained in the images by polynomial fitting to ensure the effectiveness of the terraced compression method. The results of processing transmission images of breasts in the visible and near-infrared bands obtained from clinical trials show that the method of this paper can effectively remove trend item and enhance the contrast of the transmissive images. This can promote the application of multispectral imaging technology for early detection of breast cancer.

KRT15 in early breast cancer screening and correlation with HER2 positivity, pathological gradeand N stage.

Objective: This study was designed to explore KRT15 dysregulation and its correlation with clinical characteristics among ductal carcinoma in situ (DCIS), DCIS with microinvasion (DCIS-MI)and invasive breast cancer (IBC) patients. Methods: KRT15 from lesion samples of 50 DCIS patients, 48 DCIS-MI patientsand 50 IBC patients was detected by immunohistochemistry. Results: KRT15 discriminated IBC patients from DCIS patients (area under the curve [AUC] =0.895; 95% CI=0.836-0.954) and DCIS-MI patients (AUC=0.707; 95% CI=0.606-0.808). In DCIS patients, KRT15 was negatively correlated with pathological grade (p=0.015). In DCIS-MI patients, KRT15 was positively related to estrogen receptor positivity but negatively associated with Ki-67 (both p<0.05). In IBC patients, KRT15 was negativelylinked to HER2 positivity, histological grade, N stageand tumor node metastasis stage (all p<0.05). Conclusion: KRT15 assessmentmay help with early breast cancer screening.

Evaluating knowledge, attitudes, and practices toward breast cancer and breast cancer screening among females in Arab countries of the Middle East: A systematic literature review.

e18761 Background: Among all types of cancer, breast cancer (BC) is the most diagnosed cancer in women and is the leading cause of cancer-related deaths in females globally. Developing countries possess approximately 60% to 70% of all worldwide breast cancer deaths. Despite the increasing efforts to expand awareness and encourage screening, breast cancer is still the most frequently diagnosed form of the disease in Arab countries. In this study, we aim to systematically review the knowledge, attitudes, and practices of middle-eastern Arab women toward the recognition of early breast cancer symptoms, risk factors, and screening. Methods: A literature search was conducted on PubMed and Scopus to identify relevant literature. Search terms included “Knowledge” OR “attitude” OR “practice” AND “breast cancer” AND “Arab region” OR “middle east region”, and other relevant and equivalent terms. All English language studies which achieved the inclusion criteria were included. Each article was independently screened by two researchers in two levels (title/abstract and full-text screening). Data extracted included, but was not limited to, country, study design, study population, sample size, level of knowledge, attitudes, practices, conclusions, and limitations for each study. Results: Out of the 1182 publications identified, 14 cross-sectional studies were included with respondents aged 15 and above. Study populations varied from females attending healthcare centers, to country residents and students. Five studies reported the level of overall BC knowledge, where respondents with low levels of knowledge ranged from 19.7% to 67%. The most chosen risk factors for BC were personal or family history (n = 5), and prior radiation (n = 2). A breast lump or mass was the most chosen warning sign (n = 5), followed by an armpit lump (n = 2). Regarding screening, women who had ever performed breast self-examination (BSE) ranged from 30.3% in Yemen to 57.5% in Oman, while those who consistently performed BSE ranged from 17.4% to 27.9%. Women who had ever undergone mammography ranged from 1.6% to 40%. Barriers to screening included system-related barriers (n = 2), feeling healthy (n = 2), fear of the results (n = 2), and others. Better screening practices were mostly associated with a higher level of education (n = 5), and personal or family history (n = 4). Conclusions: The majority of Arab women in the middle east had heard about breast cancer, but had a low level of knowledge, attitudes, and practices toward breast cancer and its screening methods, which was also variable among different counties. Multiple barriers to screening were found. Such findings can raise the need for programs that elevate knowledge, and provide accessible screening programs that solve for the barriers to screening in the Arab region.

The Role of the Health Belief Model in Explaining Why Symptomatic Iranian Women Hesitate to Seek Early Screening for Breast Cancer: a Qualitative Study.

Early detection of breast cancer (BC) is important to increase the likelihood of treatment and improve the quality of life and survival. The study explored why symptomatic women linger to seek early screening for BC diagnosis based on the health belief model (HBM). In this qualitative study, 20 participants were selected using the purposive sampling method (9 health professionals and 11 female patients BC). Data were collected using in-depth, semi-structured interviews in 2019. Transcribed interview data were analyzed using the directed content analysis informed by HBM. According to the participants' experiences, although most patients comprehended the extent of the disease, they did not see themselves at risk of BC. Some were not adequately aware of the benefits of early diagnosis and did not have the self-efficacy required for early presentation. The main barriers to the early presentation included lack of awareness, financial problems, embarrassment over the clinical examination, and inadequate access to specialized centers. According to HBM, while designing and implementing educational programs, it is recommended to focus on reinforcing perceived susceptibility, perceived benefits, and perceived self-efficacy, providing access to facilities, and removing possible barriers and cultural issues to encourage women for timely referral for BC screening.

Abstract P1-05-14: ctDNA IN BREAST MILK FOR EARLY DETECTION OF PREGNANCY ASSOCIATED BREAST CANCER

Abstract The potential of cell-free tumor DNA (ctDNA) for early tumor detection in asymptomatic patients is yet to be established. In the case of pregnancy associated breast cancer (BC), early detection is of special interest, since it is an entity of special aggressiveness due to a delay in diagnosis, along with the negative effect of mammary gland involution when BC is diagnosed during the postpartum period (PPBC). Indeed, PPBC has double metastatic risk and worst prognosis. With a potential applicability for cancer screening during breastfeeding, here we explored the presence of ctDNA in breast milk (BM) from women with BC associated to pregnancy. Matched samples from breast tumor, plasma and BM from a cohort of 14 women diagnosed during pregnancy or breastfeeding were analysed by droplet digital PCR and a targeted next generation sequencing panel (NGS). Thirteen patients had early-stage disease (11% Stage I, 61% Stage II and 28% Stage III) whilst one was diagnosed at advanced stage. BM harboured ctDNA, since mutations present in the tumor tissue were detected in 86% of the cases by ddPCR and in 71,4% by NGS (difference owing to technique sensitivity). Matched plasma samples had detectable ctDNA levels in only 8% of the cases. In one of the patients, a BM sample collected 18 months prior to BC diagnosis revealed the presence of a pathogenic PIK3CA mutation later detected in the surgically removed tumor. With the ultimate goal of applying the NGS in BM as a technique for early detection of BC in the postpartum period, we have collected samples from healthy volunteers and patients at high risk of developing BC (defined as women becoming pregnant at &amp;gt;40 years or carriers of germ-line pathogenic variants associated with BC -ie: BRCA1, BRCA2, PALB2, RAD51C/D). The application of NGS in BM as a technique for early detection of BC in the postpartum period, identified in a high-risk woman (criteria of enrolment was the age, 46yo) an AKT1 pathogenic mutation in the right-sided BM anticipating by 6 months the radiological diagnosis of a Luminal A tumor, stage pT1bN0M0. In summary, our data provides evidence that ctDNA in BM is highly prevalent even at initial tumor stages, and could be exploited for early breast cancer screening during breastfeeding. Citation Format: Cristina Saura, Carolina Ortiz, Enrique Javier Arenas, Judit Matito, Anna Suñol-Camas, Octavi Cordoba, Alex Martinez-Sabadell, Itziar Garcia-Ruiz, Ignacio Miranda, Clara Morales-Comas, Estela Carrasco Lopez, Cristina Viaplana, Vicente Peg, Paolo Nuciforo, Neus Bayo, Josep Maria Miquel, Marina Gomez-Rey, Guillermo Villacampa, Silvia Arevalo, Javier Carmona, Martín Espinosa-Bravo, Judith Balmaña, Rodrigo Dienstmann, Joaquin Arribas, Josep Tabernero, miriam sanso, Ana Vivancos. ctDNA IN BREAST MILK FOR EARLY DETECTION OF PREGNANCY ASSOCIATED BREAST CANCER [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-14.