Structured illumination microscopy for cancer identification in diagnostic breast biopsies.

Breast cancer is the second most common cancer diagnosed in women in the US with almost 280,000 new cases anticipated in 2023. Currently, on-site pathology for location guidance is not available during the collection of breast biopsies or during surgical intervention procedures. This shortcoming contributes to repeat biopsy and re-excision procedures, increasing the cost and patient discomfort during the cancer management process. Both procedures could benefit from on-site feedback, but current clinical on-site evaluation techniques are not commonly used on breast tissue because they are destructive and inaccurate. Ex-vivo microscopy is an emerging field aimed at creating histology-analogous images from non- or minimally-processed tissues, and is a promising tool for addressing this pain point in clinical cancer management. We investigated the ability structured illumination microscopy (SIM) to generate images from freshly-obtained breast tissues for structure identification and cancer identification at a speed compatible with potential on-site clinical implementation. We imaged 47 biopsies from patients undergoing a guided breast biopsy procedure using a customized SIM system and a dual-color fluorescent hematoxylin & eosin (H&E) analog. These biopsies had an average size of 0.92 cm2 (minimum 0.1, maximum 4.2) and had an average imaging time of 7:29 (minimum 0:22, maximum 37:44). After imaging, breast biopsies were submitted for standard histopathological processing and review. A board-certified pathologist returned a binary diagnostic accuracy of 96% when compared to diagnoses from gold-standard histology slides, and key tissue features including stroma, vessels, ducts, and lobules were identified from the resulting images.

The role of clinical breast examination and fine needle aspiration cytology in early detection of breast cancer: A cross-sectional study nested in a cohort in a low-resource setting.

BACKGROUND: Breast cancer is prevalent worldwide, with disparities in screening, diagnosis, treatment outcomes, and survival. In Africa, the majority of women are diagnosed at advanced stages, affecting treatment outcomes. Screening is one of the best strategies to reduce mortality rates caused by this cancer. Yet in a resource-constrained setting, there is limited access to screening and early detection services, which are available only at a few referral hospitals. OBJECTIVES: We aimed to evaluate the prevalence and screening results of breast cancer using clinical breast examination coupled with fine needle aspiration cytology in a resource-constraint setting. DESIGN: A combined cross-sectional and cohort study. METHODS: Women at risk of developing breast cancer in the Kilimanjaro region of Tanzania were invited, through public announcements, to their primary healthcare facilities. A questionnaire was used to assess the participants' characteristics. The women received a clinical breast examination, and detectable lesions were subjected to a confirmatory fine needle aspiration cytology or an excisional biopsy. Preliminary data from this ongoing breast cancer control program were extracted and analyzed for this study. RESULTS: A total of 3577 women were screened for breast cancer; their mean age was 47 ± 7.53 years. About a third of them (1145, 32%) were practicing self-breast examination at least once a month. Of 200 (5.6%) with abnormal clinical breast examination, 18 (9%) were confirmed to be breast cancer, making the prevalence to be 0.5%. The vast majority of participants with breast cancer (13, 72.2%) had early disease stages, and infiltrating ductal carcinoma, no special type, was the most common (15, 83.3%) histopathology subtype. Hormonal receptor status determination results indicated that 11 (61.1%), 7 (38.9%), and 5 (27.8%) of the tumors overexpressed estrogen receptor, progesterone receptor, and human epidermal receptor-2, respectively. CONCLUSION: Our study demonstrates 5.6% of Tanzanian women have abnormal clinical breast examination findings, with 9% having breast cancer. Nearly three-quarters (72.2%) of breast cancer screened for early disease were detected in the early disease stages. This finding suggests that organized screening with clinical breast examination coupled with fine needle aspiration cytology, which is a simple and cost-effective screening method, has the potential to improve early detection and outcomes for breast cancer patients in a resource-constraint setting.

Shared decision-making for supporting women's decisions about breast cancer screening.

BACKGROUND: In breast cancer screening programmes, women may have discussions with a healthcare provider to help them decide whether or not they wish to join the breast cancer screening programme. This process is called shared decision-making (SDM) and involves discussions and decisions based on the evidence and the person's values and preferences. SDM is becoming a recommended approach in clinical guidelines, extending beyond decision aids. However, the overall effect of SDM in women deciding to participate in breast cancer screening remains uncertain. OBJECTIVES: To assess the effect of SDM on women's satisfaction, confidence, and knowledge when deciding whether to participate in breast cancer screening. SEARCH METHODS: We searched the Cochrane Breast Cancer Group's Specialised Register, CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform on 8 August 2023. We also screened abstracts from two relevant conferences from 2020 to 2023. SELECTION CRITERIA: We included parallel randomised controlled trials (RCTs) and cluster-RCTs assessing interventions targeting various components of SDM. The focus was on supporting women aged 40 to 75 at average or above-average risk of breast cancer in their decision to participate in breast cancer screening. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed studies for inclusion and conducted data extraction, risk of bias assessment, and GRADE assessment of the certainty of the evidence. Review outcomes included satisfaction with the decision-making process, confidence in the decision made, knowledge of all options, adherence to the chosen option, women's involvement in SDM, woman-clinician communication, and mental health. MAIN RESULTS: We identified 19 studies with 64,215 randomised women, mostly with an average to moderate risk of breast cancer. Two studies covered all aspects of SDM; six examined shortened forms of SDM involving communication on risks and personal values; and 11 focused on enhanced communication of risk without other SDM aspects. SDM involving all components compared to control The two eligible studies did not assess satisfaction with the SDM process or confidence in the decision. Based on a single study, SDM showed uncertain effects on participant knowledge regarding the age to start screening (risk ratio (RR) 1.18, 95% confidence interval (CI) 0.61 to 2.28; 133 women; very low certainty evidence) and frequency of testing (RR 0.84, 95% CI 0.68 to 1.04; 133 women; very low certainty evidence). Other review outcomes were not measured. Abbreviated forms of SDM with clarification of values and preferences compared to control Of the six included studies, none evaluated satisfaction with the SDM process. These interventions may reduce conflict in the decision made, based on two measures, Decisional Conflict Scale scores (mean difference (MD) -1.60, 95% CI -4.21 to 0.87; conflict scale from 0 to 100; 4 studies; 1714 women; very low certainty evidence) and the proportion of women with residual conflict compared to control at one to three months' follow-up (rate of women with a conflicted decision, RR 0.75, 95% CI 0.56 to 0.99; 1 study; 1001 women, very low certainty evidence). Knowledge of all options was assessed through knowledge scores and informed choice. The effect of SDM may enhance knowledge (MDs ranged from 0.47 to 1.44 higher scores on a scale from 0 to 10; 5 studies; 2114 women; low certainty evidence) and may lead to higher rates of informed choice (RR 1.24, 95% CI 0.95 to 1.63; 4 studies; 2449 women; low certainty evidence) compared to control at one to three months' follow-up. These interventions may result in little to no difference in anxiety (MD 0.54, 95% -0.96 to 2.14; scale from 20 to 80; 2 studies; 749 women; low certainty evidence) and the number of women with worries about cancer compared to control at four to six weeks' follow-up (RR 0.88, 95% CI 0.73 to 1.06; 1 study, 639 women; low certainty evidence). Other review outcomes were not measured. Enhanced communication about risks without other SDM aspects compared to control Of 11 studies, three did not report relevant outcomes for this review, and none assessed satisfaction with the SDM process. Confidence in the decision made was measured by decisional conflict and anticipated regret of participating in screening or not. These interventions, without addressing values and preferences, may result in lower confidence in the decision compared to regular communication strategies at two weeks' follow-up (MD 2.89, 95% CI -2.35 to 8.14; Decisional Conflict Scale from 0 to 100; 2 studies; 1191 women; low certainty evidence). They may result in higher anticipated regret if participating in screening (MD 0.28, 95% CI 0.15 to 0.41) and lower anticipated regret if not participating in screening (MD -0.28, 95% CI -0.42 to -0.14). These interventions increase knowledge (MD 1.14, 95% CI 0.61 to 1.62; scale from 0 to 10; 4 studies; 2510 women; high certainty evidence), while it is unclear if there is a higher rate of informed choice compared to regular communication strategies at two to four weeks' follow-up (RR 1.27, 95% CI 0.83 to 1.92; 2 studies; 1805 women; low certainty evidence). These interventions result in little to no difference in anxiety (MD 0.33, 95% CI -1.55 to 0.99; scale from 20 to 80) and depression (MD 0.02, 95% CI -0.41 to 0.45; scale from 0 to 21; 2 studies; 1193 women; high certainty evidence) and lower cancer worry compared to control (MD -0.17, 95% CI -0.26 to -0.08; scale from 1 to 4; 1 study; 838 women; high certainty evidence). Other review outcomes were not measured. AUTHORS' CONCLUSIONS: Studies using abbreviated forms of SDM and other forms of enhanced communications indicated improvements in knowledge and reduced decisional conflict. However, uncertainty remains about the effect of SDM on supporting women's decisions. Most studies did not evaluate outcomes considered important for this review topic, and those that did measured different concepts. High-quality randomised trials are needed to evaluate SDM in diverse cultural settings with a focus on outcomes such as women's satisfaction with choices aligned to their values.

Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review.

Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker, helps classify subtypes and guide chemotherapy decisions. This review explores how artificial intelligence (AI) can optimize Ki-67 assessment, improving precision and workflow efficiency in BC management. The study presents a critical analysis of the current state of AI-powered Ki-67 assessment. Results demonstrate high agreement between AI and standard Ki-67 assessment methods highlighting AI's potential as an auxiliary tool for pathologists. Despite these advancements, the review acknowledges limitations such as the restricted timeframe and diverse study designs, emphasizing the need for further research to address these concerns. In conclusion, AI holds promise in enhancing Ki-67 assessment's precision and workflow efficiency in BC diagnosis. While challenges persist, the integration of AI can revolutionize BC care, making it more accessible and precise, even in resource-limited settings.

Fine tuning deep learning models for breast tumor classification.

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Implementation of a clinical breast exam and referral program in a rural district of Pakistan.

BACKGROUND: The role of clinical breast examination (CBE) for early detection of breast cancer is extremely important in lower-middle-income countries (LMICs) where access to breast imaging is limited. Our study aimed to describe the outcomes of a community outreach breast education, home CBE and referral program for early recognition of breast abnormalities and improvement of breast cancer awareness in a rural district of Pakistan. METHODS: Eight health care workers (HCW) and a gynecologist were educated on basic breast cancer knowledge and trained to create breast cancer awareness and conduct CBE in the community. They were then deployed in the Dadu district of Pakistan where they carried out home visits to perform CBE in the community. Breast cancer awareness was assessed in the community using a standardized questionnaire and standard educational intervention was performed. Clinically detectable breast lesions were identified during home CBE and women were referred to the study gynecologist to confirm the presence of clinical abnormalities. Those confirmed to have clinical abnormalities were referred for imaging. Follow-up home visits were carried out to assess reasons for non-compliance in patients who did not follow-through with the gynecologist appointment or prescribed imaging and re-enforce the need for follow-up. RESULTS: Basic breast cancer knowledge of HCWs and study gynecologist improved post-intervention. HCWs conducted home CBE in 8757 women. Of these, 149 were warranted a CBE by a physician (to avoid missing an abnormality), while 20 were found to have a definitive lump by HCWs, all were referred to the study gynecologist (CBE checkpoint). Only 50% (10/20) of those with a suspected lump complied with the referral to the gynecologist, where 90% concordance was found between their CBEs. Follow-up home visits were conducted in 119/169 non-compliant patients. Major reasons for non-compliance were a lack of understanding of the risks and financial constraints. A significant improvement was observed in the community's breast cancer knowledge at the follow-up visits using the standardized post-test. CONCLUSIONS: Basic and focused education of HCWs can increase their knowledge and dispel myths. Hand-on structured training can enable HCWs to perform CBE. Community awareness is essential for patient compliance and for early-detection, diagnosis, and treatment.

Biological Roles and Clinical Applications of Exosomes in Breast Cancer: A Brief Review.

Breast cancer (BC) is a global health risk for women and has a high prevalence rate. The drug resistance, recurrence, and metastasis of BC affect patient prognosis, thus posing a challenge to scientists. Exosomes are extracellular vesicles (EVs) that originate from various cells; they have a double-layered lipid membrane structure and contain rich biological information. They mediate intercellular communication and have pivotal roles in tumor development, progression, and metastasis and drug resistance. Exosomes are important cell communication mediators in the tumor microenvironment (TME). Exosomes are utilized as diagnostic and prognostic biomarkers for estimating the treatment efficacy of BC and have the potential to function as tools to enable the targeted delivery of antitumor drugs. This review introduces recent progress in research on how exosomes influence tumor development and the TME. We also present the research progress on the application of exosomes as prognostic and diagnostic biomarkers and drug delivery tools.

Exploring the Immunological Profile in Breast Cancer: Recent Advances in Diagnosis and Prognosis through Circulating Tumor Cells.

This review offers a comprehensive exploration of the intricate immunological landscape of breast cancer (BC), focusing on recent advances in diagnosis and prognosis through the analysis of circulating tumor cells (CTCs). Positioned within the broader context of BC research, it underscores the pivotal role of the immune system in shaping the disease's progression. The primary objective of this investigation is to synthesize current knowledge on the immunological aspects of BC, with a particular emphasis on the diagnostic and prognostic potential offered by CTCs. This review adopts a thorough examination of the relevant literature, incorporating recent breakthroughs in the field. The methodology section succinctly outlines the approach, with a specific focus on CTC analysis and its implications for BC diagnosis and prognosis. Through this review, insights into the dynamic interplay between the immune system and BC are highlighted, with a specific emphasis on the role of CTCs in advancing diagnostic methodologies and refining prognostic assessments. Furthermore, this review presents objective and substantiated results, contributing to a deeper understanding of the immunological complexity in BC. In conclusion, this investigation underscores the significance of exploring the immunological profile of BC patients, providing valuable insights into novel advances in diagnosis and prognosis through the utilization of CTCs. The objective presentation of findings emphasizes the crucial role of the immune system in BC dynamics, thereby opening avenues for enhanced clinical management strategies.

Metaplastic breast cancer with a unique presentation and complete response to chemotherapy: a case report.

BACKGROUND: Metaplastic breast carcinomas are a rare variant group of breast carcinomas. They are usually high-grade and triple-negative tumors. They often present with large primary tumor sizes. However, the involvement of axillary lymph nodes is infrequent at the time of diagnosis. Metaplastic breast carcinomas are associated with a worse prognosis and a poorer response to chemotherapy in comparison with other non-metaplastic triple-negative breast cancers. Up until this point, there are no specific treatment recommendations for metaplastic breast carcinomas beyond those intended for invasive breast cancer in general. CASE PRESENTATION: A 40-year-old woman complained of a palpable mass in her left axilla. On ultrasonography, the mass was solid, spindle-shaped, hypoechoic with regular borders, and exhibited decreased vascularity. At first, the mass appeared to be of a muscular origin. There was not any clinical nor ultrasonic evidence of a primary breast tumor. On magnetic resonance imaging, the axillary mass was a well-defined with regular borders, measuring 24 × 35 mm. Needle biopsy showed a spindle cell tumor with mild to moderate atypia. The subsequent surgical resection revealed a spindle cell neoplasm within a lymph node, favoring a metastatic origin of the tumor. The tumor cells lacked expression of estrogen, progesterone, and HER2 receptors. PET-CT scan indicated pathological uptake in the left breast. Accordingly, the patient was diagnosed with metaplastic breast cancer that had metastasized to the axillary lymph node. She commenced a combined chemotherapy regimen of doxorubicin and cyclophosphamide. After six treatment cycles, she underwent left modified radical mastectomy with axillary lymph node dissection. Pathological examination of the specimens revealed a total burn-out tumor in the breast due to excellent treatment response. There were no residual tumor cells. All dissected lymph nodes were free of tumor. At the one-year follow-up, the patient showed no signs of tumor recurrence. CONCLUSION: This report sheds light on a distinctive presentation of metaplastic breast carcinoma, emphasizing the need for vigilance in diagnosing this rare and aggressive breast cancer variant. In addition, the patient's remarkable response to chemotherapy highlights potential treatment avenues for metaplastic breast cancer.

Advanced-stage breast cancer diagnosis and its determinants in Ethiopia: a systematic review and meta-analysis.

INTRODUCTION: Worldwide, breast cancer is the primary cause of illness and death. Unless early detected and treated breast cancer is a life-threatening tumor. Advanced-stage presentation is greatly linked with short survival time and increased mortality rates. In Ethiopia nationally summarized evidence on the level of advanced-stage breast cancer diagnosis is scarce. Therefore, this systematic review and meta-analysis aimed to determine the pooled prevalence of advanced-stage breast cancer diagnosis and its determinants in Ethiopia. METHOD: By following PRISMA guidelines, a systematic review and meta-analysis were carried out. To include relevant publications, a broad literature search was conducted in the African Online Journal, PubMed, Google Scholar, and Embase which are published until last search date; June 15, 2023. To prevent further duplication this review was registered in PROSPERO database with ID no of CRD42023435096. To determine the pooled prevalence, a weighted inverse variance random effect model was applied. I2 statistics and the Cochrane Q-test were computed to determine heterogeneity. To evaluate publication bias, a funnel plot, and Egger's regression test were used. RESULT: A total of 924 articles were sought and finally 20 articles were included in this review. The pooled prevalence of advanced-stage breast cancer diagnosis in Ethiopia was 72.56% (95%CI; 68.46-76.65%). Use of traditional medicine as first choice (AOR = 1.32, 95% CI: (1.13-1.55)), delay of > 3 months in seeking care (AOR = 1.24, 95% CI: (1.09-1.41)), diagnosis or health system delay of > 2 months (AOR = 1.27, 95% CI: (1.11-1.46)), rural residence (AOR = 2.04, 95% CI: (1.42 - 2.92)), and chief complaint of a painless breast lump (AOR = 2.67, 95% CI: (1.76-4.06)) were significantly associated to advanced-stage diagnosis. CONCLUSION: In Ethiopia, more than two-thirds of breast cancer cases are diagnosed at an advanced stage. Use of traditional medicine before diagnostic confirmation, delay in seeking care, health system delay, rural residence, and chief complaint of painless breast lump were positively associated with an advanced-stage diagnosis. Policymakers and program designers give great focus to those delays so as to seek and access modern diagnosis and treatment as early as possible specifically focusing on those who are rurally residing.

Circulating tumor DNA: from discovery to clinical application in breast cancer.

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.

OBJECTIVE: The aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics. METHODS: A systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated. RESULTS: This study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83-0.92) and 0.79 (95% CI 0.72-0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1-5.7), 0.15 (95% CI: 0.10-0.22), and 29.0 (95% CI 15-55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87-0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes. CONCLUSION: MRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients' medical outcomes and quality of life.

Clinical and economic outcomes of adding [18F]FES PET/CT in estrogen receptor status identification in metastatic and recurrent breast cancer in the US.

BACKGROUND AND OBJECTIVES: Correct identification of estrogen receptor (ER) status in breast cancer (BC) is crucial to optimize treatment; however, standard of care, involving biopsy and immunohistochemistry (IHC), and other diagnostic tools such as 2-deoxy-2-[18F]fluoro-D-glucose or 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), can yield inconclusive results. 16α-[18F]fluoro-17ß-fluoroestradiol ([18F]FES) can be a powerful tool, providing high diagnostic accuracy of ER-positive disease. The aim of this study was to estimate the budget impact and cost-effectiveness of adding [18F]FES PET/CT to biopsy/IHC in the determination of ER-positive status in metastatic (mBC) and recurrent breast cancer (rBC) in the United States (US). METHODS: An Excel-based decision tree, combined with a Markov model, was developed to estimate the economic consequences of adding [18F]FES PET/CT to biopsy/IHC for determining ER-positive status in mBC and rBC over 5 years. Scenario A, where the determination of ER-positive status is carried out solely through biopsy/IHC, was compared to scenario B, where [18F]FES PET/CT is used in addition to biopsy/IHC. RESULTS: The proportion of true positive and true negative test results increased by 0.2 to 8.0 percent points in scenario B compared to scenario A, while re-biopsies were reduced by 94% to 100%. Scenario B resulted in cost savings up to 142 million dollars. CONCLUSIONS: Adding [18F]FES PET/CT to biopsy/IHC may increase the diagnostic accuracy of the ER status, especially when a tumor sample cannot be obtained, or the risk of a biopsy-related complication is high. Therefore, adding [18F]FES PET/CT to biopsy/IHC would have a positive impact on US clinical and economic outcomes.

Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing.

Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value.

Socio-cultural beliefs and perceptions influencing diagnosis and treatment of breast cancer among women in Ghana: a systematic review.

BACKGROUND: Breast cancer is currently the most commonly diagnosed cancer in Ghana and the leading cause of cancer mortality among women. Few published empirical evidence exist on cultural beliefs and perceptions about breast cancer diagnosis and treatment in Ghana. This systematic review sought to map evidence on the socio-cultural beliefs and perceptions influencing the diagnosis and treatment of breast cancer among Ghanaian women. METHODS: This review was conducted following the methodological guideline of Joanna Briggs Institute and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses. The literature search was conducted in PubMed, CINAHL via EBSCOhost, PsycINFO, Web of Science, and Embase. Studies that were conducted on cultural, religious, and spiritual beliefs were included. The included studies were screened by title, abstract, and full text by three reviewers. Data were charted and results were presented in a narrative synthesis form. RESULTS: After the title, abstract, and full-text screening, 15 studies were included. Three categories were identified after the synthesis of the charted data. The categories included: cultural, religious and spiritual beliefs and misconceptions about breast cancer. The cultural beliefs included ancestral punishment and curses from the gods for wrongdoing leading to breast cancer. Spiritual beliefs about breast cancer were attributed to spiritual or supernatural forces. People had the religious belief that breast cancer is a test from God and they resorted to prayers for healing. Some women perceived that breast cancer is caused by spider bites, heredity, extreme stress, trauma, infections, diet, or lifestyle. CONCLUSION: This study adduces evidence of the socio-cultural beliefs that impact on the diagnosis and treatment of breast cancer among women in Ghana. Taking into consideration the diverse cultural and traditional beliefs about breast cancer diagnosis and treatment, there is a compelling need to intensify nationwide public education on breast cancer to clarify the myths and misconceptions about the disease. We recommend the need to incorporate socio-cultural factors influencing breast cancer diagnosis and treatment into breast cancer awareness programs, education, and interventions in Ghana.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study.

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Implementing Standard Diagnosis and Treatment for Locally Advanced Breast Cancer Through Global Research in Latin America: Results From a Multicountry Pragmatic Trial.

PURPOSE: Breast cancer mortality rates in Latin America (LA) are higher than those in the United States, possibly because of advanced disease presentation, health care disparities, or unfavorable molecular subtypes. The Latin American Cancer Research Network was established to address these challenges and to promote collaborative clinical research. The Molecular Profiling of Breast Cancer Study (MPBCS) aimed to evaluate the clinical characteristics and treatment outcomes of LA participants with locally advanced breast cancer (LABC). PATIENTS AND METHODS: The MPBCS enrolled 1,449 participants from Argentina, Brazil, Chile, Mexico, and Uruguay. Through harmonized procedures and quality assurance measures, this study evaluated clinicopathologic characteristics, neoadjuvant chemotherapy response, and survival outcomes according to residual cancer burden (RCB) and the type of surgery. RESULTS: Overall, 711 and 480 participants in the primary surgery and neoadjuvant arms, respectively, completed the 5-year follow-up period. Overall survival was independently associated with RCB (worse survival for RCBIII-adjusted hazard ratio, 8.19, P < .001, and RCBII [adjusted hazard ratio, 3.69, P < .008] compared with RCB0 [pathologic complete response or pCR]) and type of surgery (worse survival in mastectomy than in breast-conserving surgery [BCS], adjusted hazard ratio, 2.97, P = .001). The hormone receptor-negative-human epidermal growth factor receptor 2-positive group had the highest proportion of pCR (48.9%). The analysis of the ASCO Quality Oncology Practice Initiative breast module revealed high compliance with pathologic standards but lower adherence to treatment administration standards. Notably, compliance with trastuzumab administration varied widely among countries (33.3%-88.7%). CONCLUSION: In LABC, we demonstrated the survival benefit of BCS and the prognostic effect of the response to available neoadjuvant treatments despite an important variability in access to key treatments. The MPBCS represents a significant step forward in understanding the real-world implementation of oncologic procedures in LA.

Strategies for Accessible Breast Screening for People With Intellectual Disability.

INTRODUCTION: People with intellectual disability are less likely to participate in breast screening than people without intellectual disability. They experience a range of barriers to accessing breast screening, however, there is no consensus on strategies to overcome these barriers. Our objective was to reach consensus on the strategies required for accessible breast screening for people with intellectual disability. METHODS: Fourteen experts participated in a modified on-line Delphi that used Levesque's model of health care access as the theoretical framework. At the end of each round descriptive and thematic analyses were completed. Data was then triangulated to determine if consensus was reached. RESULTS: After 3 rounds, 9 strategies were modified, 24 strategies were added and consensus was reached for 52 strategies across the 5 dimensions of access. Key areas of action related to (i) decision making and consent, (ii) accessible information, (iii) engagement of peer mentors, (iv) service navigators, and (v) equipping key stakeholders. CONCLUSIONS: The resulting strategies are the first to articulate how to make breast screening accessible and can be used to inform health policy and quality improvement practices.

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.