Using Morphological Operation and Watershed Techniques for Breast Cancer Detection

Survey of machine learning algorithms for breast cancer detection using mammogram images

Optimization and normalization strategies for long term untargeted HILIC-LC-qTOF-MS based metabolomics analysis: Early diagnosis of breast cancer

Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images (MRIs), mammograms, X-rays, ultrasounds, and histopathological images, but magnetic resonance (MR) and mammogram imaging have proved to present the promising results. The proposed paper has presented the results of classification algorithms over Breast Cancer (BC) mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia. This paper has developed a novel approach called the novel spectral extraction algorithm (NSEA) that uses feature extraction and fusion by using local binary pattern (LBP) and bilateral algorithms, as well as a support vector machine (SVM) as a classifier. The NSEA with the SVM classifier demonstrated a promising accuracy of 94% and an elapsed time of 0.68 milliseconds, which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes, logistic regression, K-Nearest Neighbor (KNN), Gaussian Discriminant Analysis (GDA), AdaBoost and Extreme Learning Machine (ELM). ELM produced the comparative accuracy of 94% however has a lower elapsed time of 1.35 as compared to SVM. Adaboost has produced a fairly well accuracy of 82%, KNN has a low accuracy of 66%. However Logistic Regression, GDA and Naïve Bayes have produced the lowest accuracies of 47%, 43% and 42%.

Simple SummaryEarly diagnosis of breast cancer greatly increases the chance of cure and survival from the disease. The mammogram is widely used for early detection of breast cancer, but its effectiveness and accuracy have been a concern for a long time as well as its inability in detecting small cancers, especially in women with dense breast tissues. Therefore, it is an unmet clinical need to develop a simple, convenient test to overcome the shortcomings of mammography. Liquid biopsy, which is based on the analysis of body fluids, has attracted much attention in the search for cancer biomarkers. Recent advances in analytical techniques have gradually made it possible to detect breast cancer early through a biomarker analysis of blood, nipple aspirate fluid, sweat, urine, tears, or the breath. We envision that a simple blood or breath test holds great promise as a biomarker for early detection of breast cancer in the near future.Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of “omics” strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( >1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

Tumor antigens, expressed early in disease, which can induce specific antibody response could serve as biomarkers for early detection of human breast cancer. However, the ability to obtain sera from women prior to the development of breast cancer is limited, which makes it difficult to identify early antigens directly from breast cancer patients. Transgenic mouse models have been proved to demonstrate similar antigen repertoire to breast cancer patients. Therefore, we propose to identify early tumor antigens in neu transgenic mice and use them as diagnostic targets for early detection of human breast cancer. We have collected serial sera from neu transgenic mice, sera collected before the animals develop palpable tumor “pre-diagnostic sera”. With serological analysis of recombinant cDNA expression libraries (SEREX) approach, we were able to identify nine tumor antigens from pre-diagnostic sera, Rpl5, TNFaip3/A20, Pdhx, Otud6b, Stk39, Zfp238, Dnajc10, Lgals8 and Vps35. These antigens demonstrated detectable IgG antibody response prior to tumor development in neu transgenic mice. IgM is the first antibody to be produced during a humoral immune response, so we questioned whether including antigen specific IgM with IgG responses could achieve a better diagnostic performance. Firstly, we analyzed receiver-operating-characteristic (ROC) curves of these antigens by IgG and IgM ELISA analysis in neu transgenic mice and 26 FVB control mice. By combining IgG and IgM ELISA, we were able to achieve a higher AUC: Otud6b and Stk39 show an AUC of 0.882, and adding Lgals8 IgG ELISA to this panel can achieve an AUC of 0.924 in discriminating cases from control mice. We documented that these autoantibodies are also detectable in the serum of women with breast cancer and then screened a panel of sera derived from the Women's Health Initiative cohort (samples obtained 6-18 months prior to the development of breast cancer) and case matched controls. We evaluated serum for the presence of IgG and IgM specific autoantibodies to Pdhx, Otud6b, and Stk39. With both IgG and IgM ELISA analysis, a panel combining the three antigens demonstrates an AUC of 0.633 in patients diagnosed within 5 month and an AUC of 0.688 in patients diagnosed within 5-18 month of the blood draw. In summary, we conclude that tumor antigens identified in neu transgenic mouse model can be used for early detection and diagnosis of breast cancer in women. The combination of antigen specific IgG and IgM antibodies may enhance the sensitivity of the assay for early detection. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2683. doi:10.1158/1538-7445.AM2011-2683

: An exploratory study was conducted in which 25 African-American female breast cancer survivors were interviewed. The purpose of this exploratory study was to identify barriers to early detection of breast cancer among African-American females over the age of 55. It was designed to result in the articulation of hypotheses for further study. The literature review confirmed that there are disparities in rates of early diagnosis of breast cancer among African-American women between the ages of 55 and %O. Based on the findings from the literature review, a semi-structured interview protocol was developed to explore beliefs and practices around breast cancer and breast cancer screening among a small sample of up to 25 African-American breast cancer survivors over the age of 55. For most of them, breast cancer was detected and treated during its early stage. There was a sense among them that breast cancer was not something that the women expected to encounter, even for those with family histories of the disease. Emerging from these 25 interviews were several interesting issues for future research, one of which was relating to spirituality.

BackgroundBreast cancer is the second leading cause of cancer related deaths in women worldwide. Reports about the early diagnosis of breast cancer are suggestive of an improved clinical outcome and overall survival rate in cancer patients. Therefore, cancer screening biomarker for early detection and diagnosis is urgently required for timely treatment and better cancer management. In this context, we investigated an association of cancer testis antigen, A-Kinase anchor protein 4 (AKAP4) with breast carcinoma.Methodology/FindingsWe first compared the AKAP4 gene and protein expression in four breast cancer cells (MCF7, MDA-MB-231, SK-BR3 and BT474) and normal human mammary epithelial cells. In addition, 91 clinical specimens of breast cancer patients of various histotypes including ductal carcinoma in situ, infiltrating ductal carcinoma and infiltrating lobular carcinoma and 83 available matched adjacent non-cancerous tissues were examined for AKAP4 gene and protein expression by employing in situ RNA hybridization and immunohistochemistry respectively. Humoral response against AKAP4 was also investigated in breast cancer patients employing ELISA. Our in vitro studies in all breast cancer cells revealed AKAP4 gene and protein expression whereas, normal human mammary epithelial cells failed to show any expression. Using in situ RNA hybridization and immunohistochemistry, 85% (77/91) tissue specimens irrespective of histotypes, stages and grades of breast cancer clinical specimens revealed AKAP4 gene and protein expression. However, matched adjacent non-cancerous tissues failed to display any AKAP4 gene and protein expression. Furthermore, humoral response was observed in 79% (72/91) of total breast cancer patients. Interestingly, we observed that 94% (72/77) of breast cancer patients found positive for AKAP4 protein expression generated humoral response against AKAP4 protein.ConclusionsCollectively, our data suggests that AKAP4 may be used as serum based diagnostic test for an early detection and diagnosis of breast cancer and may be a potential target for immunotherapeutic use.

Breast cancer is a serious potential health problem for all women. The current screening and imaging techniques such as X-ray mammography, clinical examination, ultrasound, and MRI, even combined in some way, provide only 73% accuracy in detecting breast cancer. This motivates researchers with biomedical background to investigate new techniques to advance the screening techniques aiming for early detection of breast cancer. In particular, the electromagnetic (EM) community has been researching potential techniques for breast cancer detection. Potential EM techniques are discussed in this paper.

Simple SummaryBreast cancer screening is associated with benefits, such as mortality reduction and improved quality of life, and harms, such as false-positive results, overdiagnoses, and costs. Novel screen tests could be considered to reduce the harms and increase the benefits of screening. Liquid biopsies have been proposed as a novel method for the early detection of breast cancer. However, studies show that liquid biopsies based on cell-free DNA have a low sensitivity for early-stage breast cancer. Using the microsimulation model MISCAN-Fadia, we model the benefits, harms, and costs of the early detection of breast cancer using liquid biopsies for varying levels of liquid biopsy sensitivity and specificity. We found that liquid biopsies are unlikely to be an alternative to digital mammography, given the test performance based on a CCGA substudy. When liquid biopsies are unable to detect the precursor lesion of breast cancer—ductal carcinoma in situ (DCIS)—they need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. We estimated a maximum liquid biopsy price of USD 187, which is substantially lower than currently listed prices.Breast cancer screening is associated with harms, such as false-positives and overdiagnoses, and, thus, novel screen tests can be considered. Liquid biopsies have been proposed as a novel method for the early detection of cancer, but low cell-free DNA tumor fraction might pose a problem for the use in population screening. Using breast cancer microsimulation model MISCAN-Fadia, we estimated the outcomes of using liquid biopsies in breast cancer screening in women aged 50 to 74 in the United States. For varying combinations of test sensitivity and specificity, we quantify the impact of the use of liquid biopsies on the harms and benefits of screening, and we estimate the maximum liquid biopsy price for cost-effective implementation in breast cancer screening at a cost-effectiveness threshold of USD 50,000. We investigate under what conditions liquid biopsies could be a suitable alternative to digital mammography and compare these conditions to a CCGA substudy. Outcomes were compared to digital mammography screening, and include mortality reduction, overdiagnoses, quality-adjusted life-years (QALYs), and the maximum price of a liquid biopsy for cost-effective implementation. When liquid biopsies are unable to detect DCIS, a large proportion of overdiagnosed cases is prevented but overall breast cancer mortality reduction and quality of life are lower, and costs are higher compared to digital mammography screening. Liquid biopsies prices should be restricted to USD 187 per liquid biopsy depending on test performance. Overall, liquid biopsies that are unable to detect ductal carcinoma in situ (DCIS) need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. Liquid biopsies might be more suitable as an addition to digital mammography than as an alternative.

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.

Reproductive health for housewives in the work area of the Passo Health Center is carried out based on problems from the partner group, namely low knowledge and minimal information about women's reproductive health, especially cervical cancer and breast cancer as well as skills in practicing or performing the BSE method resulting in low awareness of the importance women's reproductive health, this is shown through the low behavior of early detection carried out by housewives both the IVA test for early detection of cervical cancer and the application of the BSE method for early detection of breast cancer. With the aim of increasing the knowledge, skills and health of housewives in the target group regarding reproductive health, especially cervical cancer and breast cancer as a prevention effort. As well as increasing the knowledge of mothers about basic clinical information on cervical cancer and breast cancer, methods of detection as prevention and treatment methods, the number of mothers who are detected and can be treated. The solutions applied to the above problems are (1). Counseling to increase the knowledge of housewives about women's reproductive health (cervical Ca and breast Ca); (2). perform early detection of cervical cancer and early detection (screening) of breast cancer. The method used for the implementation of the solution is (1). Lectures, questions and answers and discussions both audio (sound) and visual (pictures and video) to increase mothers' knowledge about women's reproductive health; (2). Visual inspection method with acetic acid, also known as visual inspection with acetic acid (IVA test) for early detection of cervical cancer in housewives of the target group; Evaluation is carried out for each implementation of activities to encourage understanding of housewives to behave healthily towards reproductive health.

Breast cancer is the leading cause of cancer mortality in Brazilian women. The new Brazilian guidelines for early detection of breast cancer were drafted on the basis of systematic literature reviews on the possible harms and benefits of various early detection strategies. This article aims to present the recommendations and update the summary of evidence, discussing the main controversies. Breast cancer screening recommendations (in asymptomatic women) were: (i) strong recommendation against mammogram screening in women under 50 years of age; (ii) weak recommendation for mammogram screening in women 50 to 69 years of age; (iii) weak recommendation against mammogram screening in women 70 to 74 years of age; (iv) strong recommendation against mammogram screening in women 75 years or older; (v) strong recommendation that screening in the recommended age brackets should be every two years as opposed to shorter intervals; (vi) weak recommendation against teaching breast self-examination as screening; (vii) absence of recommendation for or against screening with clinical breast examination; and (viii) strong recommendation against screening with magnetic resonance imaging, ultrasonography, thermography, or tomosynthesis alone or as a complement to mammography. The recommendations for early diagnosis of breast cancer (in women with suspicious signs or symptoms) were: (i) weak recommendation for the implementation of awareness-raising strategies for early diagnosis of breast cancer; (ii) weak recommendation for use of selected signs and symptoms in the current guidelines as the criterion for urgent referral to specialized breast diagnosis services; and (iii) weak recommendation that every breast cancer diagnostic workup after the identification of suspicious signs and symptoms in primary care should be done in the same referral center.

Most of the currently established techniques for breast cancer have inherent disadvantages, such as destructive effects, image accuracy, and others. In addition, mammography and nuclear methods have a potential for producing harmful ionization effects in humans. Another technique for breast cancer detection is microwave imaging radar, which has the advantage of a less hazardous modality and is a non-destructive method. However, the weakness of the received backscattered signal is a major concern for this technique. Furthermore, there is a limitation to the size of tumour that can be detected. Therefore, this study analyzes the feasibility of ultra wideband (UWB) and microwave forward scattering radar (FSR) system for breast cancer detection. Two parameters which are UWB FSR radar cross section (UWB FS RCS) and received Doppler frequency profile were investigated. The results had shown a significant difference in terms of RCS amplitude of more than 2 dBm2 between normal and cancerous breast which can be used as a clue to detect the presence of tumor. The simulation shows a cancerous breast having Doppler frequency higher than the Doppler value in normal breast. Key words: Early breast cancer detection, ultra wideband (UWB) radar imaging.

Introduction: The use of social media by healthcare professionals to create disease awareness has been increased worldwide. It is easily available and accessible to the larger number of populations and hence helps in bridging the gap between doctor and patient. Breast cancer is the leading cause of cancer among women in India and the main reason for higher mortality among them is the late presentation to the surgeons, owing to various myths and social and physical taboos. We conducted a study to assess the awareness of breast cancer among the women in general population and also to check the effectiveness of WhatsApp in improving women's knowledge regarding the risk reduction, myths, early detection and treatment of breast cancer. The study was a pre-post health educational study including 146 wom Methods: en. Multiple WhatsApp groups were created to share information regarding breast cancer awareness, early detection, risk factors, common myths, self-breast examination and treatment of breast cancer in the form of audio, video, text and images. The group was active for 4-5 days as an interactive session. Data were collected through google forms. We noticed a signicant improvement in the knowledge of women regard Results: ing the common myths regarding breast cancer and the symptoms of breast cancer. There was signicant increase in performance of self-breast examination from 14.4% to 84.6% (p- <0.001). Most of the women found the video of self-breast examination useful. The commonly Conclusion: used social networking site like WhatsApp can be used as a tool to assess the awareness of breast cancer among women. This also helped in exchange of the knowledge among women, which in turn helped in the risk reduction and early detection of breast cancer. This app is a useful tool that can be incorporated into health education strategies that focus on breast cancer.