Deciphering key chemotherapeutic drug targets within tyrosine metabolism for breast cancer and advancing a wide-ranging diagnostic strategy.

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

Background: Early-onset breast cancer is typically defined as a diagnosis of disease before age 40-45. Prior studies have shown interesting associations between race and obesity on the incidence and prognosis of breast cancer. Data from the SEER program at the National Cancer Institute has shown increased mortality amongst African American women (0.2%) compared to Non-Hispanic White (NHW) women (0.1%) who were diagnosed with breast cancer at age 45 years or younger. This is despite a similar risk of diagnosis between racial groups of this age range (1.27%, 1.27%). Other large studies have shown that African American patients tended to present with more advanced disease had worse clinical outcomes. Our study sought to determine the impact of race and obesity on the diagnosis and prognosis of early onset breast cancer amongst the patient population at Georgetown University Hospital (GUH). Methods: We compiled a database of all new patients, 652 in total, seen at Lombardi Cancer Center at Medstar GUH from 10/2020- 6/2022. We reviewed patient charts in the EMR and documented age at diagnosis, race, ethnicity, stage at diagnosis, and BMI. The BMI was categorized by underweight (BMI < 18.5), healthy weight (BMI 18.5-24.9), overweight (BMI 25-25.9), obese (BMI 30-34.9), and morbidly obese (BMI ≥ 35). Stage at diagnosis was determined by comparing the pathology report with the NCCN guidelines version 4.2022. We identified 136 patients who were diagnosed at GUH at age 45 years old or younger and compared our findings to SEER data from 2017-2019. Results: Out of 136 patients who met our age criteria, 131 had racial data available: 29 AA (0.22), 68 NHW (0.52), 4 Hispanic (0.03), 30 Other (0.23). Out of the 34 patients with advanced disease (stage 3 and 4) 16 were NHW (0.47), 4 were AA (0.12), 2 were Hispanic (0.06) and 12 were Other (0.35). 131 patients had BMI data available: 5 underweight (0.04), 56 healthy weight (0.43), 41 overweight (0.31), 21 obese (0.16), and 8 morbidly obese (0.06). 18 patients with advanced disease were overweight or obese, out of which 11 were NHW (0.61), 3 were AA (0.16), 3 were Other (0.16), and 1 was Hispanic (0.06). Conclusion: In contrast with the SEER data, our study found that the patient population at GUH had a much higher proportion of Non-Hispanic White (NHW) patients who were diagnosed with advanced disease compared to African American (AA) patients. It also found a higher proportion of NHW patients amongst those who were diagnosed with advanced stage disease and were overweight or obese compared to AA patients. This data suggests that in our patient population, Non-Hispanic White women who were overweight or obese at the time of diagnosis of early onset breast cancer (≤ 45 years old) were more likely to be diagnosed with advanced stage disease than women in other demographic groups. It is possible that this discrepancy is related to racial variations in receptor status. Further investigation is required to correlate these associations in clinical practice with other known demographic variables and disease-specific risk factors in order to better understand how they impact the diagnosis and prognosis of early-onset breast cancer. Citation Format: Austin Kordic, Amanda Reyes, Nadia Ashai. Evaluating the Impact of Race and Body Mass Index on the Diagnosis and Prognosis of Early Onset 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 P4-03-42.

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.

BackgroundThe incidence of malignancies in kidney transplant recipients is increasing. Breast cancer is a common malignancy after kidney transplantation and can be more aggressive in kidney transplant recipients than in the general population. In this study, we evaluated the incidence and prognosis of breast cancer in kidney transplant recipients.FindingsBetween 1993 and 2013, 750 kidney transplant patients were followed-up at our center. Since 1999, annual physical examination, mammography, and breast ultrasonography have been performed for such patients. Diagnostic studies, including core needle or mammotome biopsy, were performed for suspected malignancies. Patients with malignant neoplasm were administered the appropriate treatment and followed-up to assess tumor response and symptoms.Nine patients were diagnosed with breast cancer during the follow-up period. The mean age at the initial detection of the breast cancer was 47.7 ± 8.4 years. The mean interval from transplantation to diagnosis was 148.7 ± 37.1 months. Of the 9 patients, 8 were detected through the screening test; 7 were treated with breast conservative surgery and 1 was treated with modified radical mastectomy. The cancer stages were 0 (n = 2), I (n = 6), and II (n = 1). The incidence of breast cancer tended to be unchanged with time between transplantation and diagnosis, inconsistent with the increase in the duration of immunosuppression.ConclusionAnnual screening tests are crucial in the early diagnosis of breast cancer. Early treatment of breast cancer can result in an excellent prognosis in kidney transplant recipients.

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

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....

With the increasing incidence of breast cancer worldwide, early diagnosis and treatment of breast cancer has become the key to improving patient survival and quality of life. As a powerful data analysis tool, machine learning is increasingly widely used in the medical field, especially in disease prediction and assisted diagnosis. This paper aims to design and implement a machine learning-based breast cancer prediction system to improve the early diagnosis rate of breast cancer and reduce medical costs. Through an in-depth analysis of the global incidence of breast cancer and the current application of machine learning in the medical field, this study clarified the importance of breast cancer prediction and the problems existing in the existing prediction system. This paper further discusses the theoretical basis of machine learning in breast cancer detection, evaluates the advantages and disadvantages of commonly used machine learning algorithms, and reviews the latest research progress in this field at home and abroad. In the part of system design and implementation, the architecture design, data flow and processing process of the prediction system, as well as the method of data preprocessing and feature selection are introduced in detail. In addition, this paper also constructs a machine learning model suitable for breast cancer prediction, and carries out systematic implementation and testing through the actual development environment. In the discussion section, the applicability of machine learning model in breast cancer prediction is analyzed, the causes of model inefficiency are discussed, and the corresponding solutions are proposed. Finally, the paper summarizes the content of the full text, points out the limitations of the research, and puts forward the direction of future research. The results of this study not only provide a new technical means for the early diagnosis of breast cancer, but also provide valuable experience for the application of machine learning in the medical field.

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.

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.

Deep Learning-Based Multi-Label Tissue Segmentation and Density Assessment from Mammograms

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.

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%.

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.

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.

Introduction: Breast cancer is the most common cancer of women worldwide. Early diagnosis of it has a very important role in its management. Breast self-examination (BSE) is a key to the early diagnosis of breast cancer. Materials and Methods: A community-based cross-sectional study was conducted on 300 females of Jaipur city. This study was conducted by a house-to-house survey through a systematic random sampling technique in the field practice area of the Urban Health Training Centre (UHTC) of SMS medical college, Jaipur (Rajasthan), India. A predesigned semi-structured questionnaire containing predesigned questions regarding knowledge and practice of BSE was used to collect data. A Chi-square test was used to find out associations. Results: Only 18% of females were aware of BSE and 5.7% of the females were practicing BSE. Health professionals (31.03%) were the main source of knowledge. Only 50% of females who have heard the name of BSE, knew that it is performed by self. Awareness and practice of BSE both were found to be associated with religion, education, socioeconomic status, and occupation and there was no association with age and marital status. Females with higher education and socioeconomic status were more aware of BSE. The most common (94.69%) reason for not practicing BSE was the lack of awareness of steps followed by ‘find it unnecessary’. Conclusion: As knowledge and practice of BSE were observed very poor and considering the important role that can be played by BSE in the early diagnosis and management of breast cancer, there is an urgent need to implement and reinforce BSE in the existing cancer awareness and screening programs. IEC activities regarding BSE also motivated proper knowledge of BSE.