Analysis of the value of the combined application of multiple ultrasound modalities in the early diagnosis of breast cancer

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

Introduction:Breast cancer is one of the most relevant malignancies among women. Early diagnosis and accurate staging of breast cancer is important for the selection of an appropriate therapeutic strategy and achieving a better outcome. Aim:This study aimed to explore the significance of some non-invasive biomarkers in the early diagnosis and staging of Egyptian breast cancer patients.Subjects and Methods:A total of 135 female patients with physically and pathologically confirmed breast cancer and 40 unrelated controls as well as 40 patients with benign breast mass were enrolled in this study. The malignant breast cancer group was further divided into four groups according to tumor size. Serum levels of carcinoembryonic antigen-related cell adhesion molecule-1 (CEACAM1), resistin and visfatin were determined by enzyme immunoassay. Results:Elevated levels of CEACAM1, resistin and visfatin were observed in breast cancer patients when compared with normal control and benign groups. The cutoff values, sensitivities and specificities of these biomarkers were appropriate for the discrimination of breast cancer from controls. Additionally, the serum levels of visfatin increased positively with tumor size and consequently with breast cancer stages. Conclusion:CEACAM1, resistin and visfatin are valuable in early diagnosis of breast cancer, with visfatin being preferentially used in staging.

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.

ObjectivesUnderstanding barriers to early diagnosis of symptomatic breast cancer among Black African, Black Caribbean and White British women in the UK.DesignIn-depth qualitative interviews using grounded theory methods to identify themes....

A 1H NMR-based metabonomic approach was applied to monitor the alterations of serum metabolic profiles in MMTV-PyMT transgenic mice to detect the dynamic changes associated with the pathological process and explore the early-stage biomarkers. The 1H NMR spectra of sera samples from four different stages in MMTV-PyMT mice including hyperplasia, adenoma, early carcinoma and late carcinoma stages were recorded and analyzed using multivariate statistical techniques. The results showed that the increased levels of lipid and lactate, and decreased leucine/isoleucine, valine, methionine, glutamine, creatine, PC/GPC, taurine and glucose were of significance for the early carcinoma stage. As the disease progressed (late carcinoma stage), the metabolic profiles changed significantly; some were negatively regulated compared with those at the early carcinoma stage, such as lipid, leucine/isoleucine, methionine and creatine, accompanied by other new metabolite changes of alanine, pyruvate, glutamate, citrate, aspartate, myo-inositol, 3-methylhistidine and formate. It is important to note that breast cancer patients and the early carcinoma stage of MMTV-PyMT mice had some similar metabolite changes, including lipid, lactate, glutamine, creatine, taurine and glucose, which were determined to be of great value for the early clinical diagnosis of breast cancer. The findings from this study provided valuable biomarkers for the early clinical diagnosis of breast cancer, and showed the potential power of integrating NMR techniques and pattern recognition methods for the analysis of the biochemical changes under certain pathophysiological conditions.

Background: Early diagnosis is a key determinant of breast cancer prognosis and survival. However, advanced stage presentation and delays in diagnosis are common problems in Ethiopia. More than half of the cases are diagnosed at advanced stages and the barriers to early diagnosis are not well studied in the country. Hence, this study aims to explore perceived barriers to early diagnosis of breast cancer in South and Southwestern Ethiopia. Methods: A qualitative study was conducted from March to April 2018 in six selected public hospitals located both in urban and rural settings. Twenty five purposefully selected breast cancer patients and health care providers from these public hospitals were interviewed. An in-depth interview was conducted using a topic guide by qualitative research experts. A thematic analysis was performed using Open Code software version 4.02. Results: Patient and health system related barriers are two main themes identified for barriers to early diagnosis of breast cancer. Patient related barriers included lack of awareness and knowledge of the disease, beliefs in traditional and religious means of treatments, lack of social and financial support to seek medical care. Health system related barriers included misdiagnosis of cases, long distance referrals due to service inaccessibility, high diagnostic costs, long waiting time, unavailability of screening and diagnostic tests. Conclusion: Early diagnosis of breast cancer is affected by a multitude of barriers in South and Southwestern Ethiopia. Hence, to increase early diagnosis of breast cancer, awareness campaigns and education about the disease, its prevention, and early detection are needed. Opportunities exist to improve early diagnosis and timely treatment by strengthening referral linkages of health care facilities in rural areas. Efforts are needed to decentralize the tertiary level oncology care and improve capacity in local health facilities. Citation Format: Sefonias Getachew, Adamu Addissie, Aragaw Tesfaw, Lesley Taylor, Eva Kantelhardt. Perceived barriers to early diagnosis of breast cancer in south and southwestern Ethiopia: Qualitative study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2315.

BACKGROUND: To date, there are no effective methods for early diagnosis and screening of breast cancer. High-tech methods, such as magnetic resonance imaging and contrasted computed tomography, as well as positron emission computed tomography have high resolution, but their high cost does not allow the use of these techniques for screening and primary diagnosis.
 AIM: To improve the quality and efficiency of diagnostic measures for breast cancer through a personalized approach based on an analysis of a set of risk factors.
 MATERIALS AND METHODS: Data from the population cancer registry of the Altai Territory, created at the Altai Regional Oncology Center (Barnaul, Russia), were used. To date, the register includes information on 308 550 patients with malignant neoplasms, including 31 783 women with breast cancer.
 Based on the method of targeted prevention by A.F. Lazarev “Method for determining the risk of breast cancer according to Lazarev A.F.” (Patent No. 2651131) an “Automated program for early diagnosis of breast cancer” was developed. The program significantly reduces the time for the formation of groups of high cancer risk precancers and increases the efficiency of breast cancer detection, and also makes it possible to develop a set of targeted preventive measures personally for each patient. Testing of this algorithm included testing of 512 patients, as a result of which a high-risk precancer group was formed. In the established register, patients underwent a complex of in-depth examinations (ultrasound examination, mammography, magnetic resonance imaging with dynamic contrast, and puncture of tumors if indicated).
 RESULTS: The precancer group at high risk of developing breast cancer consisted of 92 patients, in-depth examination revealed 7 patients with established breast cancer, which amounted to 7.6%. All cases of breast cancer were detected in stages I and II.
 CONCLUSION: Targeted diagnostics using the “Automated program for early diagnosis of breast cancer” allows to improve the quality and efficiency of diagnostic measures for breast cancer identification through personalized approach, using multiple risk factors.

Large language models (LLMs) have emerged in medical image analysis and can provide accurate and personalized medical services for doctors and patients. However, by simply utilizing textual information and ignoring other modal details such as images, LLMs fail to achieve high accuracy in the early diagnosis of breast cancer and thus have not yet been seamlessly integrated into the clinical practice of breast cancer diagnosis. Therefore, this study proposes that the Parallel Multimodal Language Model (PMLM) combines images and text, integrates visual and semantic information in text for early screening and diagnosis of breast cancer, and improves the accuracy of early screening and diagnosis, while also enhancing health system access. In addition, existing multimodal diagnostic methods are evaluated. The final experimental results reveal that the PMLM achieves an F1 of 0.87 [95% CI: 0.85–0.89] and an Area Under Curve (AUC) of 0.90 [95% CI, 0.89, 0.92] in the early diagnosis of breast cancer, both of which exceeded those of the existing baseline model.

IntroductionBreast cancer is the most commonly diagnosed cancer in women of all ethnic groups in the UK. The largest single ethnic groups in the UK are white, Indian, Pakistani, black...

Breast cancer is the most common and second most common cause of cancer in women. [3] Early diagnosis of breast cancer can provide better treatment and thus increase survival rates. Data classification using machine learning is widely used in cancer diagnosis and early cancer detection. The purpose of this literature review is to focus on the use of machine learning in classifying existing data in the early detection and diagnosis of breast cancer. When much scientific literature is examined, it is clearly seen that there are many methods for the diagnosis of breast cancer. The aim of this study is to provide a comprehensive review and recommendations on cancer screening and diagnosis. "Breast cancer type classification using machine learning."[1]Provides an overview of current research on multiple breast cancer cases using data mining techniques to improve breast cancer diagnosis and prognosis.. Key Words: breast cancer, machine learning, artificial neural networks, decision tree, support vector machine nearest neighbor, healthcare system, Wisconsin breast cancer database

BackgroundEarly diagnosis is a key determinant of breast cancer prognosis and survival. More than half of breast cancer cases are diagnosed at an advanced stage in Ethiopia, and the barriers to early diagnosis in this country are not well understood. We aimed to identify the perceived barriers to early diagnosis of breast cancer from the perspective of patients and health care providers in south and southwestern Ethiopia.MethodsA qualitative study was conducted from March to April 2018 using in-depth interviews of breast cancer patients and breast cancer health care providers from six public hospitals located in urban and rural areas of south and southwestern Ethiopia. All participants provided verbal consent before participating. A thematic analysis was performed using Open Code 4.02.ResultsTwelve breast cancer patients and thirteen health care providers were included in the study. Patient and health-system related barriers to early diagnosis of breast cancer were identified. Patient-related barriers were lack of knowledge and awareness of breast cancer, belief in traditional medicine and religious practices for treatment, and lack of social and financial support to seek care at a medical facility. Health-system related barriers were misdiagnosis of breast cancer, long distance to referral facilities, high cost of diagnostic services, long waiting time for diagnostic tests, and lack of screening and diagnostic tests in local facilities.ConclusionsEarly diagnosis of breast cancer is affected by multiple barriers in south and southwestern Ethiopia. Awareness campaigns and education about the disease, prevention, and early detection are needed to increase early diagnosis of breast cancer. Opportunities exist to improve early diagnosis and timely treatment in rural areas.

The impact of tyrosine metabolism on the early diagnosis and treatment of breast cancer remains unclear. This underlines importance of exploring its mechanisms. This study conducted an integrated analysis of breast cancer transcriptome data from the TCGA and GEO databases, utilizing differential expression analysis, enrichment analysis, immune infiltration analysis, single-cell RNA sequencing analysis, hdWGCNA analysis, and molecular docking to investigate the role of tyrosine metabolism in breast cancer and its relationship with chemotherapy response. The co-expression prognosis model of tyrosine metabolism developed in this study demonstrated superior performance in the prognostic assessment of breast cancer, achieving an AUC value of 0.735, surpassing traditional clinical indicators. The identified two key genes(MAOA, MAOB) and their interaction network showed significant value in the diagnosis and prognosis of breast cancer. Moreover, the early diagnosis model "Extra Trees (BO)" developed using machine learning algorithms exhibited excellent stability and generalization capability. These findings not only highlight the critical role of tyrosine metabolism in regulating the tumor immune microenvironment but also mark Monoamine oxidase A (MAOA) and Monoamine oxidase B (MAOB) as important potential biomarkers linking immunotherapy and chemotherapy. This research provides an effective model for the prognostic assessment and early diagnosis of breast cancer, opening new avenues for research into the precision treatment of breast cancer and the management of chemotherapy side effects.

Simultaneous, rapid, and quantitative detection of multiple tumor markers is extremely important for the prevention and early diagnosis of cancers. In this study, we established a microfluidic-based indirect competitive chemiluminescence enzyme immunoassay for simultaneous detection of cancer antigen 15-3 (CA15-3) and progesterone receptor membrane components 1 (PGRMC1) for diagnosis of breast cancer. CA15-3 and PGRMC1 could be simultaneously detected on the chip. On the chip, the linear ranges of CA15-3 and PGRMC1 were 10 pg/mL–100 ng/mL and 20 pg/mL–358 ng/mL, respectively, whereas the limits of detection (LOD) of CA15-3 and PGRMC1 were 3 pg/mL and 5 pg/mL, respectively. Compared with the enzyme-linked immunosorbent assay (ELISA, LOD = 20 pg/mL for CA15-3; LOD = 10 pg/mL for PGRMC1), our method could simultaneously detect multiple samples with higher sensitivity, which could greatly improve the detection efficiency and meet the requirement for early diagnosis of breast cancer.

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.

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