Breast cancer, screening and diagnostic tools: All you need to know

The diagnostic value of a breast cancer diagnosis model based on serum MiRNAs and serum tumor markers

BACKGROUND

Breast cancer (BCa) is the leading cause of cancer-related death among women worldwide. MicroRNAs (miRNAs) are promising tools for diagnosis and prognosis. This study investigated the role of serum miRNAs and tumor markers (TMs) in the diagnosis of BCa.

METHODS

Differentially expressed miRNAs were screened from serum samples of BCa patients and healthy individuals via high-throughput sequencing. The expression of hsa-miR-1911-3p, hsa-miR-4694-5p, hsa-miR-548ao-5p, and hsa-miR-4804-3p in 169 BCa patients and 116 healthy controls was detected via qRT-PCR. Serum tumor-associated antigens were detected by chemiluminescence. Logistic regression was subsequently used to develop the miRNA panel I, TM panel II, and (miRNA + TM) panel III models. Receiver operating characteristic (ROC) curve, precision-recall (PR) curve and decision curve analyses (DCA) were performed to assess the accuracy of the three models for BCa diagnosis. Additionally, the relationships between miRNA expression and the clinical characteristics of patients with BCa were assessed.

RESULTS

Four serum miRNAs (hsa-miR-1911-3p, hsa-miR-548ao-5p, hsa-miR-4694-5p, and hsa-miR-4804-3p) were newly associated with BCa. The miRNA panel I based on hsa-miR-548ao-5p and hsa-miR-4804-3p showed greater diagnostic effectiveness for BCa than TM panel II based on cancer antigen 125 (CA125) and cancer antigen 153 (CA153), with AUC values of 0.816 and 0.777, respectively. (miRNA + TM) panel III had higher diagnostic effectiveness than miRNA panel I, with an AUC value of 0.870. The expression of miR-548ao-5p and miR-4804-3p is closely related to clinical features, such as human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), HER2-enriched subtype, stage III/IV, and lymph node-transplanted breast cancer.

CONCLUSION

MiR-548ao-5p and miR-4804-3 could serve as potential biomarkers for the diagnosis of BCa.

Breast self-examination practice and associated factors among pastoralist women in the West Guji Zone, Oromia, Ethiopia: a community-based cross-sectional study

Background

Breast cancer is the most common cancer among women. It is the leading or second cause of female cancer-related deaths in both developed and developing countries, including Ethiopia. Breast self-examination is an effective and efficient screening method used by women for the early detection of breast cancer. There is limited data about breast self-examination practice among pastoralist women in the study area. Therefore, the aim of this study was to assess the magnitude of breast self-examination practice and associated factors among women of childbearing age in the West Guji Zone, South Ethiopia.

Methods

A community-based cross-sectional study was conducted from 1 March to 30 April 2023 on 424 randomly selected women of childbearing age in the West Guji Zone. A systematic sampling technique was employed to select the study participants. Data was collected using pre-tested and structured questionnaires through face-to-face interviews, entered into EpiData version 4.6 and then exported to SPSS version 25 for cleaning and analysis. Bivariable and multivariable analyses were conducted using binary logistic regression to identify factors associated with breast self-examination practice. Statistical significance was declared at a P-value <0.05.

Result

In this study, 62 (14.6%) of the women had a good practice of breast self-examination. Maternal age (25-34 years) [adjusted odds ratio (AOR) = 1.98, 95% confidence interval (CI): 1.07-3.70], monthly income (AOR = 3.92, 95% CI: 1.34-11.49), residence (AOR = 2.28, 95% CI: 1.09-4.78), and knowledge about breast self-examination (AOR = 2.15, 95% CI: 1.14-4.05) were factors significantly associated with breast self-examination practice.

Conclusion

The study's findings indicated a significantly low level of breast self-examination practice among pastoralist women. Women's education should be promoted, income generated, and the practice of breast self-examination should be advocated.

You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction

Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws related to the presence of false negatives. Adapting screening to each woman's needs could help overcome these challenges. While traditional risk models are valuable tools, we propose an image-based approach. Since artificial intelligence has proven effective in aiding the diagnosis of breast cancer, we aim to translate this technology to risk prediction. A 3-year risk prediction model, with a case-control age-matched approach, was developed based on the analysis of "prior" healthy mammograms. Two classes were defined - "risk" and "control" - based on the assessment done on the most recent examination: if the case was diagnosed with cancer, the prior mammogram was assigned to the "risk" class; otherwise, the prior mammogram was allocated to the "normal" class. In total, we found 3720 available controls and 1471 risk cases. Every mammogram used in this study was taken 3 years before the assessment used for class definition. Risk prediction was aimed through three methodologies: traditional machine learning, deep learning, and a combination of both. The AUCs obtained on the test set were 0.68 for the traditional machine learning, and 0.76 for the other two. No statistically significant differences were found among methods. Our findings suggest that the use of image-based deep learning methods holds promise on the field of Breast Cancer risk prediction, with further validation being needed to confirm their clinical applicability.

Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights

BACKGROUND

Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.

METHODS

Publications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.

RESULTS

A total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. "Frontiers in Oncology" was the journal with the most publications, and "Radiology" had the highest IF. The keywords with the most frequent occurrence were "breast cancer", "deep learning", and "classification". The topic trends in recent years were "explainable AI", "neoadjuvant chemotherapy", and "lymphovascular invasion".

CONCLUSION

The application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.

Three-Dimensional Models: Biomimetic Tools That Recapitulate Breast Tissue Architecture and Microenvironment to Study Ductal Carcinoma In Situ Transition to Invasive Ductal Breast Cancer

Diagnosis of ductal carcinoma in situ (DCIS) presents a challenge as we cannot yet distinguish between those lesions that remain dormant from cases that may progress to invasive ductal breast cancer (IDC) and require therapeutic intervention. Our overall interest is to develop biomimetic three-dimensional (3D) models that more accurately recapitulate the structure and characteristics of pre-invasive breast cancer in order to study the underlying mechanisms driving malignant progression. These models allow us to mimic the microenvironment to investigate many aspects of mammary cell biology, including the role of the extracellular matrix (ECM), the interaction between carcinoma-associated fibroblasts (CAFs) and epithelial cells, and the dynamics of cytoskeletal reorganization. In this review article, we outline the significance of 3D culture models as reliable pre-clinical tools that mimic the in vivo tumor microenvironment and facilitate the study of DCIS lesions as they progress to invasive breast cancer. We also discuss the role of CAFs and other stromal cells in DCIS transition as well as the clinical significance of emerging technologies like tumor-on-chip and co-culture models.

Liquid biopsy technologies: innovations and future directions in breast cancer biomarker detection

Globally, breast cancer is the most frequent type of cancer, and its early diagnosis and screening can significantly improve the probability of survival and quality of life of those affected. Liquid biopsy-based targets such as circulating tumor cells, circulating tumor DNA, and exosomes have been instrumental in the early discovery of cancer, and have been found to be effective in stage therapy, recurrence monitoring, and drug selection. Biosensors based on these target related biomarkers convert the tested substances into quantifiable signals such as electrical and optical signals through signal transduction, which has the advantages of high sensitivity, simple operation, and low invasiveness. This review provides an overview of the latest progress of liquid biopsy biomarkers in the diagnosis, prognosis and treatment of breast cancer, compares the application and advantages of different biosensors based on these biomarkers in the diagnosis of breast cancer, and analyzes the limitations and solutions of biosensor based methods.

The Genomic and Biologic Landscapes of Breast Cancer and Racial Differences

Breast cancer is a significant health challenge worldwide and is the most frequently diagnosed cancer among women globally. This review provides a comprehensive overview of breast cancer biology, genomics, and microbial dysbiosis, focusing on its various subtypes and racial differences. Breast cancer is primarily classified into carcinomas and sarcomas, with carcinomas constituting most cases. Epidemiology and breast cancer risk factors are important for public health intervention. Staging and grading, based on the TNM and Nottingham grading systems, respectively, are crucial to determining the clinical outcome and treatment decisions. Histopathological subtypes include in situ and invasive carcinomas, such as invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). The review explores molecular subtypes, including Luminal A, Luminal B, Basal-like (Triple Negative), and HER2-enriched, and delves into breast cancer's histological and molecular progression patterns. Recent research findings related to nuclear and mitochondrial genetic alterations, epigenetic reprogramming, and the role of microbiome dysbiosis in breast cancer and racial differences are also reported. The review also provides an update on breast cancer's current diagnostics and treatment modalities.

Knowledge, attitude, and willingness toward breast magnetic resonance imaging screening among women at high risk of breast cancer in Beijing, China

BACKGROUND

Annual breast magnetic resonance imaging (MRI) is highly recommended to assist mammography for women at high risk of breast cancer (BC). This study explored the knowledge, attitude, and willingness toward breast MRI screening among women at high risk of BC.

METHODS

This cross-sectional study enrolled women at high risk of BC between August 2022 and January 2023 in Beijing, China. A structural equation model (SEM) was used to analyze the relationships among knowledge, attitude, and willingness.

RESULTS

A total of 596 questionnaires were collected, and 412 questionnaires (69.13%) were valid. The women's knowledge and attitude scores were 7.75 ± 2.79 (possible range: 0-12) and 48.53 ± 6.31 (possible range: 13-65). Among the women, 297 (72.09%) were willing to undergo regular breast MRI screening. The SEM showed that knowledge had direct effect on attitude [β = 0.77, 95% CI: (0.57, 0.98), P < 0.001], the attitude had direct effect on willingness [β = 0.02, 95% CI: (0.01, 0.02), P < 0.001], knowledge had an indirect effect on willingness through attitude [β = 0.01, 95% CI: (0.01,0.02), P < 0.001], and the direct effect of knowledge on practice was not significant.

CONCLUSIONS

The women at high risk of BC had insufficient knowledge and a relatively positive attitude toward breast MRI screening. Most of them were willing to undergo regular breast MRI screening. Advertising and public health education programs should be designed to improve their knowledge and attitude, therefore improving their willingness and practice.

Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment

Breast cancer liver metastases (BCLM) are hypovascular lesions that resist intravenously administered therapies and have grim prognosis. Immunotherapeutic strategies targeting BCLM critically depend on the tumor microenvironment (TME), including tumor-associated macrophages. However, a priori characterization of the BCLM TME to optimize therapy is challenging because BCLM tissue is rarely collected. In contrast to primary breast tumors for which tissue is usually obtained and histologic analysis performed, biopsies or resections of BCLM are generally discouraged due to potential complications. This study tested the novel hypothesis that BCLM TME characteristics could be inferred from the primary tumor tissue. Matched primary and metastatic human breast cancer samples were analyzed by imaging mass cytometry, identifying 20 shared marker clusters denoting macrophages (CD68, CD163, and CD206), monocytes (CD14), immune response (CD56, CD4, and CD8a), programmed cell death protein 1, PD-L1, tumor tissue (Ki-67 and phosphorylated ERK), cell adhesion (E-cadherin), hypoxia (hypoxia-inducible factor-1α), vascularity (CD31), and extracellular matrix (alpha smooth muscle actin, collagen, and matrix metalloproteinase 9). A machine learning workflow was implemented and trained on primary tumor clusters to classify each metastatic cluster density as being either above or below median values. The proposed approach achieved robust classification of BCLM marker data from matched primary tumor samples (AUROC ≥ 0.75, 95% confidence interval ≥ 0.7, on the validation subsets). Top clusters for prediction included CD68+, E-cad+, CD8a+PD1+, CD206+, and CD163+MMP9+. We conclude that the proposed workflow using primary breast tumor marker data offers the potential to predict BCLM TME characteristics, with the longer term goal to inform personalized immunotherapeutic strategies targeting BCLM.

SIGNIFICANCE

BCLM tissue characterization to optimize immunotherapy is difficult because biopsies or resections are rarely performed. This study shows that a machine learning approach offers the potential to infer BCLM characteristics from the primary tumor tissue.

Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer

BACKGROUND

The aim of this study is to assess the efficacy of a multiparametric ultrasound imaging omics model in predicting the risk of postoperative recurrence and molecular typing of breast cancer.

METHODS

A retrospective analysis was conducted on 534 female patients diagnosed with breast cancer through preoperative ultrasonography and pathology, from January 2018 to June 2023 at the Affiliated Cancer Hospital of Xinjiang Medical University. Univariate analysis and multifactorial logistic regression modeling were used to identify independent risk factors associated with clinical characteristics. The PyRadiomics package was used to delineate the region of interest in selected ultrasound images and extract radiomic features. Subsequently, radiomic scores were established through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM) methods. The predictive performance of the model was assessed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated. Evaluation of diagnostic efficacy and clinical practicability was conducted through calibration curves and decision curves.

RESULTS

In the training set, the AUC values for the postoperative recurrence risk prediction model were 0.9489, and for the validation set, they were 0.8491. Regarding the molecular typing prediction model, the AUC values in the training set and validation set were 0.93 and 0.92 for the HER-2 overexpression phenotype, 0.94 and 0.74 for the TNBC phenotype, 1.00 and 0.97 for the luminal A phenotype, and 1.00 and 0.89 for the luminal B phenotype, respectively. Based on a comprehensive analysis of calibration and decision curves, it was established that the model exhibits strong predictive performance and clinical practicability.

CONCLUSION

The use of multiparametric ultrasound imaging omics proves to be of significant value in predicting both the risk of postoperative recurrence and molecular typing in breast cancer. This non-invasive approach offers crucial guidance for the diagnosis and treatment of the condition.

Comparative efficacy of telehealth interventions on promoting cancer screening: A network meta-analysis of randomized controlled trials

BACKGROUND

Cancer screening is a pivotal method for reducing mortality from disease, but the screening coverage is still lower than expected. Telehealth interventions demonstrated significant benefits in cancer care, yet there is currently no consensus on their impact on facilitating cancer screening or on the most effective remote technology.

DESIGN

A network meta-analysis was conducted to detect the impact of telehealth interventions on cancer screening and to identify the most effective teletechnologies.

METHODS

Six English databases were searched from inception until July 2023 to yield relevant randomized controlled trials (RCTs). Two individual authors completed the literature selection, data extraction, and methodological evaluations using the Cochrane Risk of Bias tool. Traditional pairwise analysis and network meta-analysis were performed to identify the overall effects and compare different teletechnologies.

RESULTS

Thirty-four eligible RCTs involving 131,644 participants were enrolled. Overall, telehealth interventions showed statistically significant effects on the improvement of cancer screening. Subgroup analyses revealed that telehealth interventions were most effective for breast and cervical cancer screening, and rural populations also experienced benefits, but there was no improvement in screening for older adults. The network meta-analysis indicated that mobile applications, video plus telephone, and text message plus telephone were associated with more obvious improvements in screening than other teletechnologies.

CONCLUSION

Our study identified that telehealth interventions were effective for the completion of cancer screening and clarified the exact impact of telehealth on different cancer types, ages, and rural populations. Mobile applications, video plus telephone, and text message plus telephone are the three forms of teletechnologies most likely to improve cancer screening. More well-designed RCTs involving direct comparisons of different teletechnologies are needed in the future.

CLINICAL RELEVANCE

Telehealth interventions should be encouraged to facilitate cancer screening, and the selection of the optimal teletechnology based on the characteristics of the population is also necessary.

Near-infrared AIEgens for sulfatase imaging in breast cancer in vivo

Aggregation-induced emission luminogens (AIEgens) enable highly sensitive and in situ visualization of sulfatase to benefit the early diagnosis of breast cancer (BC), but current sulfatase AIEgens always emit visible light (<650 nm). Herein, a near-infrared (NIR) AIEgen QMT-SFA is developed for sulfatase imaging in vivo. Hydrophilic QMT-SFA is cleaved by sulfatase to yield hydrophobic QMT-OH, which subsequently aggregates into nanoparticles to turn the AIE fluorescence "on", enabling sensitive sulfatase imaging in 4T1 cells and mouse models.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

BACKGROUND

Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens.

METHODS

A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.

RESULTS

Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79).

CONCLUSIONS

DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

The impact of tumor microenvironment: unraveling the role of physical cues in breast cancer progression

Metastasis accounts for the vast majority of breast cancer-related fatalities. Although the contribution of genetic and epigenetic modifications to breast cancer progression has been widely acknowledged, emerging evidence underscores the pivotal role of physical stimuli in driving breast cancer metastasis. In this review, we summarize the changes in the mechanics of the breast cancer microenvironment and describe the various forces that impact migrating and circulating tumor cells throughout the metastatic process. We also discuss the mechanosensing and mechanotransducing molecules responsible for promoting the malignant phenotype in breast cancer cells. Gaining a comprehensive understanding of the mechanobiology of breast cancer carries substantial potential to propel progress in prognosis, diagnosis, and patient treatment.

A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment

OBJECTIVE

To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments.

METHODS

This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance.

RESULTS

Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83-0.91), 0.91 (95% CI, 0.87 - 0.96), and 0.92 (95% CI, 0.86-0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from - 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram.

CONCLUSION

When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses.

CLINICAL RELEVANCE STATEMENT

The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice.

KEY POINTS

• Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions.

The impact of (simulated) resolution on breast cancer diagnosis based on high-resolution 3D micro-CT microcalcification images

BACKGROUND

Breast microcalcifications (MCs) are considered to be a robust marker of breast cancer. A machine learning model can provide breast cancer diagnosis based on properties of individual MCs - if their characteristics are captured at high resolution and in 3D.

PURPOSE

The main purpose of the study was to explore the impact of image resolution (8 µm, 16 µm, 32 µm, 64 µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro-CT MC images.

METHODS

Breast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8 µm (original resolution) and 16 µm, 32 µm, 64 µm (simulated image resolutions). Radiomic features were extracted at each image resolution in an attempt, to find a compact feature signature allowing to distinguish benign and malignant MCs. Machine learning algorithms were used for classifying individual MCs and samples (i.e., patients). For sample diagnosis, a custom-based thresholding approach was used to combine individual MC results into sample results. We conducted classification experiments when using (a) the same MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution; (b) the same MCs visible in 8 µm, 16 µm, and 32 µm resolution; (c) the same MCs visible in 8 µm and 16 µm resolution; (d) all MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for each experiment.

RESULTS

The individual MC results yielded an accuracy of 77.27%, AUC of 83.83%, F1 score of 77.25%, sensitivity of 80.86%, and specificity of 72.2% at 8 µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16 µm instead of 8 µm, a 4.01% drop when using 32 µm instead of 8 µm, a 10.69% drop when using 64 µm instead of 8 µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8 µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16 µm instead of 8 µm, a 4.91% drop when using 32 µm instead of 8 µm, and a 6.3% drop when using 64 µm instead of 8 µm.

CONCLUSIONS

The highest classification results are obtained at the highest resolution (8 µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70 µm), breast cancer diagnosis will be improved.

An Overview of Advances in Rare Cancer Diagnosis and Treatment

Cancer stands as the leading global cause of mortality, with rare cancer comprising 230 distinct subtypes characterized by infrequent incidence. Despite the inherent challenges in addressing the diagnosis and treatment of rare cancers due to their low occurrence rates, several biomedical breakthroughs have led to significant advancement in both areas. This review provides a comprehensive overview of state-of-the-art diagnostic techniques that encompass new-generation sequencing and multi-omics, coupled with the integration of artificial intelligence and machine learning, that have revolutionized rare cancer diagnosis. In addition, this review highlights the latest innovations in rare cancer therapeutic options, comprising immunotherapy, targeted therapy, transplantation, and drug combination therapy, that have undergone clinical trials and significantly contribute to the tumor remission and overall survival of rare cancer patients. In this review, we summarize recent breakthroughs and insights in the understanding of rare cancer pathophysiology, diagnosis, and therapeutic modalities, as well as the challenges faced in the development of rare cancer diagnosis data interpretation and drug development.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.

Biomarkers and biosensors for early cancer diagnosis, monitoring and prognosis

Cancers continue to be of major concern due to their serious global socioeconomic impact, apart from the continued increase in the incidence of various cancer types. A major challenge that this disease poses is due to the low "early detection" rates which limit the therapeutic outcomes for the affected individuals. Current research has highlighted the discovering biomarkers that help in early cancer detection and the development of technologies for the detection and quantification of such biomarkers. Biomarkers range from proteins to nucleic acids, and can be specific to a particular cancer type. Detection and quantification of such biomarkers at low levels from biological samples is being made possible by the advent of developing biosensors and by using biomedical engineering technologies such as tumor-on-a-chip models. Here, we present biomarkers that can be helpful for the early detection of breast, colorectal, esophageal, lung, liver, ovarian, and prostate cancer. In addition, we discuss the potential of circulating tumor cell DNA (ctDNA) as an early diagnostic marker. Finally, biosensors available for the detection of cancer biomarkers, which is a recent advancement in this area of research, are discussed.

Estimating disparities in breast cancer screening programs towards mortality, case fatality, and DALYs across BRICS-plus

BACKGROUND

Numerous studies over the past four decades have revealed that breast cancer screening (BCS) significantly reduces breast cancer (BC) mortality. However, in BRICS-plus countries, the association between BCS and BC case fatality and disability are unknown. This study examines the association of different BCS approaches with age-standardized mortality, case-fatality, and disability-adjusted life years (DALYs) rates, as well as with other biological and sociodemographic risk variables, across BRICS-plus from a national and economic perspective.

METHODS

In this ecological study applying mixed-effect multilevel regression models, a country-specific dataset was analyzed by combining data from the Global Burden of Disease study 2019 on female age-standardized BC mortality, incidence, and DALYs rates with information on national/regional BCS availability (against no such program or only a pilot program) and BCS type (only self-breast examination (SBE) and/or clinical breast examination (CBE) [SBE/CBE] versus SBE/CBE with mammographic screening availability [MM and/or SBE/CBE] versus SBE/CBE/mammographic with digital mammography and/or ultrasound (US) [DMM/US and/or previous tests] in BRICS-plus countries.

RESULTS

Compared to self/clinical breast examinations (SBE/CBE) across BRICS-plus, more complex BCS program availability was the most significant predictor of decreased mortality [MM and/or SBE/CBE: - 2.64, p < 0.001; DMM/US and/or previous tests: - 1.40, p < 0.001]. In the BRICS-plus, CVD presence, high BMI, second-hand smoke, and active smoking all contributed to an increase in BC mortality and DALY rate. High-income and middle-income regions in BRICS-plus had significantly lower age-standardized BC mortality, case-fatality, and DALYs rates than low-income regions when nationwide BC screening programs were implemented.

CONCLUSIONS

The availability of mammography (digital or traditional) and BCS is associated with breast cancer burden in BRICS-plus countries, with regional variations. In light of high-quality evidence from previous causal studies, these findings further support the preventive role of mammography screening for BCS at the national level. Intervening on BCS related risk factors may further reduce the disease burden associated with BC.

Identification of PIMREG as a novel prognostic signature in breast cancer via integrated bioinformatics analysis and experimental validation

Background

Phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator (PIMREG) expression is upregulated in a variety of cancers. However, its potential role in breast cancer (BC) remains uncertain.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to gather relevant information. The expression of PIMREG and its clinical implication in BC were assessed by using Wilcoxon rank-sum test. The prognostic value of PIMREG in BC was evaluated through the Cox regression model and nomogram, and visualized by Kaplan-Meier survival curves. Genes/proteins that interact with PIMREG in BC were also identified through GeneMANIA and MaxLink. Gene set enrichment analysis (GSEA) was then performed. The correlations of the immune cell infiltration and immune checkpoints with the expression of PIMREG in BC were explored via TIMER, TISIDB, and GEPIA. Potential drugs that interact with PIMREG in BC were explored via Q-omic. The siRNA transfection, CCK-8, and transwell migration assay were conducted to explore the function of PIMREG in cell proliferation and migration.

Results

PIMREG expression was significantly higher in infiltrating ductal carcinoma, estrogen receptor negative BC, and progestin receptor negative BC. High expression of PIMREG was associated with poor overall survival, disease-specific survival, and progression-free interval. A nomogram based on PIMREG was developed with a satisfactory prognostic value. PIMREG also had a high diagnostic ability, with an area under the curve of 0.940. Its correlations with several immunomodulators were also observed. Immune checkpoint CTLA-4 was significantly positively associated with PIMREG. HDAC2 was found as a potentially critical link between PIMREG and BRCA1/2. In addition, PIMREG knockdown could inhibit cell proliferation and migration in BC.

Conclusions

The high expression of PIMREG is associated with poor prognosis and immune checkpoints in BC. HDAC2 may be a critical link between PIMREG and BRCA1/2, potentially a therapeutic target.

Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment

As the most frequent and vulnerable malignancy among women, breast cancer universally manifests a formidable healthcare challenge. From a biological and molecular perspective, it is a heterogenous disease and is stratified based on the etiological factors driving breast carcinogenesis. Notably, genetic predispositions and epigenetic impacts often constitute the heterogeneity of this disease. Typically, breast cancer is classified intrinsically into histological subtypes in clinical landscapes. These stratifications empower physicians to tailor precise treatments among the spectrum of breast cancer therapeutics. In this pursuit, numerous prognostic algorithms are extensively characterized, drastically changing how breast cancer is portrayed. Therefore, it is a basic requisite to comprehend the multidisciplinary rationales of breast cancer to assist the evolution of novel therapeutic strategies. This review aims at highlighting the molecular and genetic grounds of cancer additionally with therapeutic and phytotherapeutic context. Substantially, it also renders researchers with an insight into the breast cancer cell lines as a model paradigm for breast cancer research interventions.

A Multicenter Study of Genotype Variation/Demographic Patterns in 2475 Individuals Including 1444 Cases With Breast Cancer in Turkey

Objective

Breast cancer (BC) is the most common cancer type in women and may be inherited, mostly in an autosomal dominant pattern. The clinical diagnosis of BC relies on the published diagnostic criteria, and analysis of two genes, BRCA1 and BRCA2, which are strongly associated with BC, are included in these criteria. The aim of this study was to compare BC index cases with non-BC individuals in terms of genotype and diagnostic features to investigate the genotype/demographic information association.

Materials and Methods

Mutational analyses for the BRCA1/BRCA2 genes was performed in 2475 individuals between 2013-2022 from collaborative centers across Turkey, of whom 1444 with BC were designated as index cases.

Results

Overall, mutations were identified in 17% (421/2475), while the percentage of mutation carriers in cases of BC was similar, 16.6% (239/1444). BRCA1/BRCA2 gene mutations were detected in 17.8% (131/737) of familial cases and 12% (78/549) of sporadic cases. Mutations in BRCA1 were found in 4.9%, whereas 12% were in BRCA2 (p<0.05). Meta-analyses were performed to compare these results with other studies of Mediterranean-region populations.

Conclusion

Patients with BRCA2 mutations were significantly more common than those with BRCA1 mutations. In sporadic cases, there was a lower proportion with BRCA1/BRCA2 variants, as expected, and these results were consistent with the data of Mediterranean-region populations. However, the present study, because of the large sample size, revealed more robust findings than previous studies. These findings may be helpful in facilitating the clinical management of BC for both familial and non-familial cases.

Effects and Mechanisms of Kaempferol in the Management of Cancers through Modulation of Inflammation and Signal Transduction Pathways

Cancer is the principal cause of death and its incidence is increasing continuously worldwide. Various treatment approaches are in practice to treat cancer, but these treatment strategies may be associated with severe side effects and also produce drug resistance. However, natural compounds have established their role in cancer management with minimal side effects. In this vista, kaempferol, a natural polyphenol, mainly found in vegetables and fruits, has been revealed to have many health-promoting effects. Besides its health-promoting potential, its anti-cancer potential has also been described in in vivo as well as in in vitro studies. The anti-cancer potential of kaempferol has been proven through modulation of cell signaling pathways in addition to the induction of apoptosis and cell cycle arrest in cancer cells. It leads to the activation of tumor suppressor genes, inhibition of angiogenesis, PI3K/AKT pathways, STAT3, transcription factor AP-1, Nrf2 and other cell signaling molecules. Poor bioavailability of this compound is one of the major limitations for its proper and effective disease management actions. Recently, some novel nanoparticle-based formulations have been used to overcome these limitations. The aim of this review is to provide a clear picture regarding the mechanism of action of kaempferol in different cancers through the modulation of cell signaling molecules. Besides this, strategies to improve the efficacy and synergistic effects of this compound have also been described. However, more studies are needed based on clinical trials to fully explore the therapeutic role of this compound, especially in cancer treatment.

Mild photothermal/radiation therapy potentiates ferroptosis effect for ablation of breast cancer via MRI/PA imaging guided all-in-one strategy

BACKGROUND

Nanotheranostics advances anticancer management by providing therapeutic and diagnostic functions, that combine programmed cell death (PCD) initiation and imaging-guided treatment, thus increasing the efficacy of tumor ablation and efficiently fighting against cancer. However, mild photothermal/radiation therapy with imaging-guided precise mediating PCD in solid tumors, involving processes related to apoptosis and ferroptosis, enhanced the effect of breast cancer inhibition is not fully understood.

RESULTS

Herein, targeted peptide conjugated gold nano cages, iRGD-PEG/AuNCs@FePt NPs ternary metallic nanoparticles (Au@FePt NPs) were designed to achieve photoacoustic imaging (PAI)/Magnetic resonance imaging (MRI) guided synergistic therapy. Tumor-targeting Au@FePt forms reactive oxygen species (ROS), initiated by X-ray-induced dynamic therapy (XDT) in collaboration with photothermal therapy (PTT), inducing ferroptosis-augmented apoptosis to realize effective antitumor therapeutics. The relatively high photothermal conversion ability of Au@FePt increases the temperature in the tumor region and hastens Fenton-like processes to achieve enhanced synergistic therapy. Especially, RNA sequencing found Au@FePt inducting the apoptosis pathway in the transcriptome profile.

CONCLUSION

Au@FePt combined XDT/PTT therapy activate apoptosis and ferroptosis related proteins in tumors to achieve breast cancer ablation in vitro and in vivo. PAI/MRI images demonstrated Au@FePt has real-time guidance for monitoring synergistic anti-cancer therapy effect. Therefore, we have provided a multifunctional nanotheranostics modality for tumor inhibition and cancer management with high efficacy and limited side effects.

Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population?

Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580-0.628). Calibration was better for longer-term prediction horizons (E/O_{long-term ranges}: 0.86-1.71; E/O_{short-term ranges}:1.24-3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.

Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study

Background

The serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available.

Methods

Women participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected (n=453 case-control pairs). Using a high-resolution mass spectrometry approach 284 compounds were quantitatively analyzed, including 30 amino acids and biogenic amines, hexoses, and 253 lipids (acylcarnitines, glycerides, phosphatidylcholines, sphingolipids, and cholesteryl esters).

Results

Age was a major confounding factor responsible for a large heterogeneity in the dataset, hence age-defined subgroups were analyzed separately. The largest number of metabolites whose serum levels differentiated BC cases and controls (82 compounds) were observed in the subgroup of younger women (<45 years old). Noteworthy, increased levels of glycerides, phosphatidylcholines, and sphingolipids were associated with reduced risk of cancer in younger and middle-aged women (≤64 years old). On the other hand, increased levels of serum lipids were associated with an enhanced risk of breast cancer in older women (>64 years old). Moreover, several metabolites could be detected whose serum levels were different between BC cases diagnosed earlier (<5 years) and later (>10 years) after sample collecting, yet these compounds were also correlated with the age of participants. Current results were coherent with the results of the NMR-based metabolomics study performed in the cohort of HUNT2 participants, where increased serum levels of VLDL subfractions were associated with reduced risk of breast cancer in premenopausal women.

Conclusions

Changes in metabolite levels detected in pre-diagnostic serum samples, which reflected an impaired lipid and amino acid metabolism, were associated with long-term risk of breast cancer in an age-dependent manner.

Decision impact studies, evidence of clinical utility for genomic assays in cancer: A scoping review

BACKGROUND

Decision impact studies have become increasingly prevalent in cancer prognostic research in recent years. These studies aim to evaluate the impact of a genomic test on decision-making and appear to be a new form of evidence of clinical utility. The objectives of this review were to identify and characterize decision impact studies in genomic medicine in cancer care and categorize the types of clinical utility outcomes reported.

METHODS

We conducted a search of four databases, Medline, Embase, Scopus and Web of Science, from inception to June 2022. Empirical studies that reported a "decision impact" assessment of a genomic assay on treatment decisions or recommendations for cancer patients were included. We followed scoping review methodology and adapted the Fryback and Thornbury Model to collect and analyze data on clinical utility. The database searches identified 1803 unique articles for title/abstract screening; 269 articles moved to full-text review.

RESULTS

87 studies met inclusion criteria. All studies were published in the last 12 years with the majority for breast cancer (72%); followed by other cancers (28%) (lung, prostate, colon). Studies reported on the impact of 19 different proprietary (18) and generic (1) assays. Across all four levels of clinical utility, outcomes were reported for 22 discrete measures, including the impact on provider/team decision-making (100%), provider confidence (31%); change in treatment received (46%); patient psychological impacts (17%); and costing or savings impacts (21%). Based on the data synthesis, we created a comprehensive table of outcomes reported for clinical utility.

CONCLUSIONS

This scoping review is a first step in understanding the evolution and uses of decision impact studies and their influence on the integration of emerging genomic technologies in cancer care. The results imply that DIS are positioned to provide evidence of clinical utility and impact clinical practice and reimbursement decision-making in cancer care. Systematic review registration: Open Science Framework osf.io/hm3jr.

An enzyme-powered microRNA discriminator for the subtype-specific diagnosis of breast cancer

Breast cancer, a disease with highly heterogeneous features, is the most common malignancy diagnosed in people worldwide. Early diagnosis of breast cancer is crucial for improving its cure rate, and accurate classification of the subtype-specific features is essential to precisely treat the disease. An enzyme-powered microRNA (miRNA, RNA = ribonucleic acid) discriminator was developed to selectively distinguish breast cancer cells from normal cells and further identify subtype-specific features. Specifically, miR-21 was used as a universal biomarker to discriminate between breast cancer cells and normal cells, and miR-210 was used to identify triple-negative subtype features. The experimental results demonstrated that the enzyme-powered miRNA discriminator displayed low limits of detection at fM levels for both miR-21 and miR-210. Moreover, the miRNA discriminator enabled the discrimination and quantitative determination of breast cancer cells derived from different subtypes based on their miR-21 levels, and the further identification of the triple-negative subtype in combination with the miR-210 levels. Therefore, it is hoped that this study will provide insight into subtype-specific miRNA profiling, which may have potential use in the clinical management of breast tumours based on their subtype characteristics.

Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks

Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance. To overcome this, digital breast tomosynthesis takes images (slices) at different angles that are later reconstructed into a 3D image. Having in mind that the slices are planar images where tissue overlapping does not occur, the goal of the work done here was to develop a deep learning model that could, based on the said slices, classify lesions as benign or malignant. The developed model was based on the work done by Muduli et. al, with a slight change in the fully connected layers and in the regularization done. In total, 77 DBT volumes-39 benign and 38 malignant-were available. From each volume, nine slices were taken, one where the lesion was most visible and four above/below. To increase the quantity and the variability of the data, common data augmentation techniques (rotation, translation, mirroring) were applied to the original images three times. Therefore, 2772 images were used for training. Data augmentation techniques were then applied two more times-one set used for validation and one set used for testing. Our model achieved, on the testing set, an accuracy of 93.2% while the values of sensitivity, specificity, precision, F1-score, and Cohen's kappa were 92%, 94%, 94%, 94%, and 0.86, respectively. Given these results, the work done here suggests that the use of single-slice DBT can compare to state-of-the-art studies and gives a hint that with more data, better augmentation techniques and the use of transfer learning might overcome the use of mammograms in this type of studies.

The various role of microRNAs in breast cancer angiogenesis, with a special focus on novel miRNA-based delivery strategies

After skin malignancy, breast cancer is the most widely recognized cancer detected in women in the United States. Breast cancer (BCa) can happen in all kinds of people, but it's much more common in women. One in four cases of cancer and one in six deaths due to cancer are related to breast cancer. Angiogenesis is an essential factor in the growth of tumors and metastases in various malignancies. An expanded level of angiogenesis is related to diminished endurance in BCa patients. This function assumes a fundamental part inside the human body, from the beginning phases of life to dangerous malignancy. Various factors, referred to as angiogenic factors, work to make a new capillary. Expanding proof demonstrates that angiogenesis is managed by microRNAs (miRNAs), which are small non-coding RNA with 19-25 nucleotides. MiRNA is a post-transcriptional regulator of gene expression that controls many critical biological processes. Endothelial miRNAs, referred to as angiomiRs, are probably concerned with tumor improvement and angiogenesis via regulation of pro-and anti-angiogenic factors. In this article, we reviewed therapeutic functions of miRNAs in BCa angiogenesis, several novel delivery carriers for miRNA-based therapeutics, as well as CRISPR/Cas9 as a targeted therapy in breast cancer.

A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice.

Numerical Estimation of SAR and Temperature Distributions inside Differently Shaped Female Breast Tumors during Radio-Frequency Ablation

Radio-frequency (RF) ablation is a reliable technique for the treatment of deep-seated malignant tumors, including breast carcinoma, using high ablative temperatures. The paper aims at a comparative analysis of the specific absorption rate and temperature distribution during RF ablation with regard to different female breast tumors. In the study, four tumor models equivalent to an irregular tumor were considered, i.e., an equivalent sphere and ellipsoid with the same surfaces and volumes as the irregular tumor and an equivalent sphere and ellipsoid inscribed in the irregular tumor. An RF applicator with a specific voltage, operating at 100 kHz inserted into the anatomically correct female breast, was applied as a source of electromagnetically induced heat. A conjugated Laplace equation with the modified Pennes equation was used to obtain the appropriate temperature gradient in the treated area. The levels of power dissipation in terms of the specific absorption rate (SAR) inside the naturalistically shaped tumor, together with the temperature profiles of the four simplified tumor models equivalent to the irregular one, were determined. It was suggested that the equivalent tumor models might successfully replace a real, irregularly shaped tumor, and the presented numeric methodology may play an important role in the complex therapeutic RF ablation process of irregularly shaped female breast tumors.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Diagnostic Value of Superb Microvascular Imaging in Differentiating Benign and Malignant Breast Tumors: A Systematic Review and Meta-Analysis

PURPOSE

We performed a systematic review and meta-analysis of studies that investigated the diagnostic performance of Superb Microvascular Imaging (SMI) in differentiating between benign and malignant breast tumors.

METHODS

Studies published between January 2010 and March 2022 were retrieved by online literature search conducted in PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Disc, China National Knowledge Infrastructure, Wanfang, and Vip databases. Pooled sensitivity, specificity, and diagnostic odd ratios were calculated using Stata software 15.0. Heterogeneity among the included studies was assessed using I2 statistic and Q test. Meta-regression and subgroup analyses were conducted to investigate potential sources of heterogeneity. Influence analysis was conducted to determine the robustness of the pooled conclusions. Deeks' funnel plot asymmetry test was performed to assess publication bias. A summary receiver operating characteristic curve (SROC) was constructed.

RESULTS

Twenty-three studies involving 2749 breast lesions were included in our meta-analysis. The pooled sensitivity and specificity were 0.80 (95% confidence interval [CI], 0.77-0.84, inconsistency index [I2] = 28.32%) and 0.84 (95% CI, 0.79-0.88, I2 = 89.36%), respectively. The pooled diagnostic odds ratio was 19.95 (95% CI, 14.84-26.82). The area under the SROC (AUC) was 0.85 (95% CI, 0.81-0.87).

CONCLUSION

SMI has a relatively high sensitivity, specificity, and accuracy for differentiating between benign and malignant breast lesions. It represents a promising supplementary technique for the diagnosis of breast neoplasms.

Cancer screening and breast cancer family history in Spanish-speaking Hispanic/Latina women in California

Background

Breast cancer is the most common cancer among women in the U.S. and the leading cause of cancer death among Hispanics/Latinas (H/L). H/L are less likely than Non-H/L White (NHW) women to be diagnosed in the early stages of this disease. Approximately 5-10% of breast cancer can be attributed to inherited genetic mutations in high penetrance genes such as BRCA1/2. Women with pathogenic variants in these genes have a 40-80% lifetime risk of breast cancer. Past studies have shown that genetic counseling can help women and their families make informed decisions about genetic testing and early cancer detection or risk-reduction strategies. However, H/L are 3.9-4.8 times less likely to undergo genetic testing than NHW women. We developed a program to outreach and educate the H/L community about hereditary breast cancer, targeting monolingual Spanish-speaking individuals in California. Through this program, we have assessed cancer screening behavior and identified women who might benefit from genetic counseling in a population that is usually excluded from cancer research and care.

Materials and Methods

The "Tu Historia Cuenta" program is a promotores-based virtual outreach and education program including the cities of San Francisco, Sacramento, and Los Angeles. Participants responded to three surveys: a demographic survey, a breast cancer family history survey, and a feedback survey. Survey responses were described for participants and compared by area where the program took place using chi-square, Fisher exact tests, and t tests. Multinomial logistic regression models were used for multivariate analyses.

Results and Conclusion

We enrolled 1042 women, 892 completed the cancer family history survey and 62 (7%) provided responses compatible with referral to genetic counseling. We identified 272 women (42.8% ages 40 to 74 years) who were due for mammograms, 250 women (24.7% ages 25 to 65 years) due for Papanicolaou test, and 189 women (71.6% ages 50+) due for colorectal cancer screening. These results highlight the need of additional support for programs that spread awareness about cancer risk and facilitate access to resources, specifically within the H/L community.

Ultrasonographic Evaluation of Skin Toxicity Following Radiotherapy of Breast Cancer: A Systematic Review

The present review aimed to systematically review skin toxicity changes following breast cancer radiotherapy (RT) using ultrasound (US). PubMed and Scopus databases were searched according to PRISMA guidelines. The characteristics of the selected studies, measured parameters, US skin findings, and their association with clinical assessments were extracted. Seventeen studies were included with a median sample size of 29 (range 11-166). There were significant US skin changes in the irradiated skin compared to the nonirradiated skin or baseline measurements. The most observed change is skin thickening secondary to radiation-induced oedema, except one study found skin thinning after pure postmastectomy RT. However, eight studies reported skin thickening predated RT attributed to axillary surgery. Four studies used US radiofrequency (RF) signals and found a decrease in the hypodermis's Pearson correlation coefficient (PCC). Three studies reported decreased dermal echogenicity and poor visibility of the dermis-subcutaneous fat boundary (statistically analysed by one report). The present review revealed significant ultrasonographic skin toxicity changes in the irradiated skin most commonly skin thickening. However, further studies with large cohorts, appropriate US protocol, and baseline evaluation are needed. Measuring other US skin parameters and statistically evaluating the degree of the association with clinical assessments are also encouraged.

Nano-Based Theranostic Platforms for Breast Cancer: A Review of Latest Advancements

Breast cancer (BC) is a highly metastatic multifactorial disease with various histological and molecular subtypes. Due to recent advancements, the mortality rate in BC has improved over the past five decades. Detection and treatment of many cancers are now possible due to the application of nanomedicine in clinical practice. Nanomedicine products such as Doxil^® and Abraxane^® have already been extensively used for BC adjuvant therapy with favorable clinical outcomes. However, these products were designed initially for generic anticancer purposes and not specifically for BC treatment. With a better understanding of the molecular biology of BC, several novel and promising nanotherapeutic strategies and devices have been developed in recent years. In this context, multi-functionalized nanostructures are becoming potential carriers for enhanced chemotherapy in BC patients. To design these nanostructures, a wide range of materials, such as proteins, lipids, polymers, and hybrid materials, can be used and tailored for specific purposes against BC. Selective targeting of BC cells results in the activation of programmed cell death in BC cells and can be considered a promising strategy for managing triple-negative BC. Currently, conventional BC screening methods such as mammography, digital breast tomosynthesis (DBT), ultrasonography, and magnetic resonance imaging (MRI) are either costly or expose the user to hazardous radiation that could harm them. Therefore, there is a need for such analytical techniques for detecting BC that are highly selective and sensitive, have a very low detection limit, are durable, biocompatible, and reproducible. In detecting BC biomarkers, nanostructures are used alone or in conjunction with numerous molecules. This review intends to highlight the recent advances in nanomedicine in BC treatment and diagnosis, emphasizing the targeting of BC cells that overexpress receptors of epidermal growth factors. Researchers may gain insight from these strategies to design and develop more tailored nanomedicine for BC to achieve further improvements in cancer specificity, antitumorigenic effects, anti-metastasis effects, and drug resistance reversal effects.

Molecular Characterization of Exosomes for Subtype-Based Diagnosis of Breast Cancer

Breast cancer is very heterogeneous and the most frequently diagnosed cancer worldwide, and precise therapy targeting specific subtypes may improve the survival rates of breast cancer patients. In this study, we designed a biomimetic vesicle by camouflaging catalytic DNA machinery with a breast cancer cell membrane, which enabled the molecular classification of circulating exosomes for subtype-based diagnosis through homotypic recognition. In addition, the vesicles specifically targeted and fused with breast cancer exosomes with phenotypic homology and manipulated the DNA machinery to amplify electrochemical signaling using exosomal RNA as an endogenous trigger. The biomimetic vesicles prepared with MCF-7 cancer cell-derived membranes were shown to recognize estrogen receptor-positive breast cancer exosomes and exhibited a low detection limit of 557 particles mL^-1 with microRNA-375 used as the endogenous biomarker. Furthermore, the biomimetic vesicles prepared with MDA-MB-231 cancer cell-derived membranes displayed satisfactory performance in a homotypic analysis of triple-negative breast cancer exosomes with a potential therapeutic target, PD-L1 mRNA, used as the endogenous biomarker. Most importantly, cross-validation experiments confirmed the high accuracy and selectivity of this homotypic recognition-driven analysis for molecular subtyping of breast cancer. When applied to clinical samples of breast cancer patients, the vesicles demonstrated feasibility and reliability for evaluating the molecular features of cancer cell-derived exosomes and enabled stage-specific monitoring of breast cancer patients because the electrochemical signals showed a positive correlation with disease progression. Therefore, this work may provide new ideas for the precise diagnosis and personalized treatment of breast cancer patients throughout the whole disease process.

Salivary biomarkers in breast cancer diagnosis: A systematic review and diagnostic meta-analysis

BACKGROUND

Salivary diagnostics and their utility as a nonaggressive approach for breast cancer diagnosis have been extensively studied in recent years. This meta-analysis assesses the diagnostic value of salivary biomarkers in differentiating between patients with breast cancer and controls.

METHODS

We conducted a meta-analysis and systematic review of studies related to salivary diagnostics published in PubMed, EMBASE, Scopus, Ovid, Science Direct, Web of Science (WOS), and Google Scholar. The articles were chosen utilizing inclusion and exclusion criteria, as well as assessing their quality. Specificity and sensitivity, along with negative and positive likelihood ratios (NLR and PLR) and diagnostic odds ratio (DOR), were calculated based on random- or fixed-effects model. Area under the curve (AUC) and summary receiver-operating characteristic (SROC) were plotted and evaluated, and Fagan's Nomogram was evaluated for clinical utility.

RESULTS

Our systematic review and meta-analysis included 14 papers containing 121 study units with 8639 adult subjects (4149 breast cancer patients and 4490 controls without cancer). The pooled specificity and sensitivity were 0.727 (95% CI: 0.713-0.740) and 0.717 (95% CI: 0.703-0.730), respectively. The pooled NLR and PLR were 0.396 (95% CI: 0.364-0.432) and 2.597 (95% CI: 2.389-2.824), respectively. The pooled DOR was 7.837 (95% CI: 6.624-9.277), with the AUC equal to 0.801. The Fagan's nomogram showed post-test probabilities of 28% and 72% for negative and positive outcomes, respectively. We also conducted subgroup analyses to determine specificity, sensitivity, DOR, PLR, and NLR based on the mean age of patients (≤52 or >52 years old), saliva type (stimulated and unstimulated saliva), biomarker measurement method (mass spectrometry [MS] and non-MS measurement methods), sample size (≤55 or >55), biomarker type (proteomics, metabolomics, transcriptomics and proteomics, and reagent-free biophotonic), and nations.

CONCLUSION

Saliva, as a noninvasive biomarker, has the potential to accurately differentiate breast cancer patients from healthy controls.

A Single-Center Audit of Symptomatic Breast Cancer Patient Referrals During COVID-19 Pandemic Restrictions

Background

Recommendations to balance cancer care with patient and hospital staff safety have been issued to hospitals during the coronavirus disease (COVID-19) pandemic. Concerns have been raised that service restrictions could jeopardize effective cancer management. Thus, this study aimed to conduct an audit to verify this proposition.

Methods

We conducted an audit comparing two-week wait (2ww) breast cancer referrals in our center between May and July 2019 and 2020. The primary endpoints were changes in the overall referral rates, differences in the waiting time, and breast cancer diagnosis rates between the two cohorts. Group differences were evaluated using the chi-square test (χ²). A p-value of <0.05 at 95% CI was considered significant.

Results

The 2ww referrals decreased by 442 (28.3%) in 2020 (2019 N=1564 vs. 2020, N=1122). Referrals in 2020 were associated with a higher rate of two-week specialist consultation than referrals in 2019 (p<0.05). The 2020 patient cohort was associated with a higher rate of breast cancer diagnosis than the 2019 cohort (6.9% vs. 4.9%, p<0.05). Of the 521 patients who had telephone consultations, 29.2% were discharged, and 367 (70.4%) had post-telephone one-stop clinic visits, of which 9.0% had breast cancer.

Conclusions

The audit provided evidence of effective breast cancer services during the COVID-19 pandemic restrictions. The study results could inform patients and the general public at large that the waiting time and breast cancer diagnosis are not compromised during COVID-19 pandemic management. The high rates of post-telephone one-stop clinic visits and cancer diagnosis may indicate weakness in triage and difficulties in diagnosing nonspecific presentation of cancer over the telephone.

Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

MIR4435-2HG: A newly proposed lncRNA in human cancer

Long non-coding RNAs (lncRNAs) play important roles in the occurrence and progression of tumors. Extensive research has contributed to the current understanding of the critical roles played by lncRNAs in various cancers. LncRNA MIR4435-2HG has been found to be crucial in many cancers, such as breast, cervical, colorectal, and gastric cancer. Expression of MIR4435-2HG is generally upregulated in cancers and MIR4435-2HG participates in many biological functions through molecular mechanism of competitive endogenous RNA networks. This review profiles recent research findings on the expression, functions, mechanism, and clinical value of MIR4435-2HG in cancer, and serves as a reference for further MIR4435-2HG-related research and clinical trials.

Insights into the value of statistical models, solvent, and relativistic effects for investigating Re complexes of 2-(4'-aminophenyl)benzothiazole: a potential spectroscopic probe

Cancer affects a major part of the worldwide population, and, to minimize deaths, the diagnosis in the early stages of the disease is fundamental. Thus, to improve diagnosis and treatment new potential spectroscopic probes are crucial. Benzothiazole derivates present antitumor properties and are highly selective and interact strongly with the enzyme phosphoinositide 3-kinase (PI3K), which was associated with cell proliferation and breast cancer cells. In this paper, the rhenium shielding tensors (¹⁸⁷Re(σ)) and hydrogen and carbon chemical shifts (¹H(δ) and ¹³C(δ)) of the Re(CO)₃(NNO) complex conjugated with 2-(4'-aminophenyl)benzothiazole (ReABT) were evaluated. A statistical HCA model was used to analyze the best DFT protocol to compute σ and δ values and to evaluate the relativistic effects, both in the basis set and Hamiltonian as well as the functionals M06L or PBE0. The best protocol was applied to obtain ¹⁸⁷Re(σ) of the ReABT complex in different environments (gas phase, solution, and in the active site of the PI3K enzyme). The results point out that ¹⁸⁷Re(σ) values of the ReABT complex change significantly when the complex is docked in the PI3K enzyme.

Dicoma anomala Enhances Phthalocyanine Mediated Photodynamic Therapy in MCF-7 Breast Cancer Cells

Breast cancer is one of the most common types of cancer in women, and it is regarded as the second leading cause of cancer-related deaths worldwide. The present study investigated phytochemical profiling, in vitro anticancer effects of Dicoma anomala methanol root extract and its enhancing effects in phthalocyanine mediated PDT on MCF-7 (ATCC^® HTB-22™) breast cancer cells. Ultra-high performance liquid chromatography coupled to electrospray ionization quadrupole-time of flight mass spectrometry (UHPLC-qTOF-MS²) was used to identify the secondary metabolites in the crude extract. The 50% inhibitory concentration (IC₅₀) of the two experimental models was established from dose response studies 24 h post-treatment with D. anomala methanol root extract (25, 50, and 100 μg/ml) and ZnPcS₄ (5, 10, 20, 40, and 60 μM) mediated PDT. The inverted microscope was used to analyze morphological changes, trypan blue exclusion assay for viability, and Annexin V-fluorescein isothiocyanate (FITC)-propidium iodide (PI) for cell death mechanisms. Immunofluorescence analysis was used to investigate the qualitative expression of the Bax, p53, and caspase 3 apoptotic proteins. Experiments were performed 4 times (n = 4) and SPSS version 27 software was used to analyze statistical significances. D. anomala methanol root extract induced cell death in MCF-7 cells by decreasing cell viability. The combination of D. anomala methanol root extract and ZnPcS₄ mediated PDT led to a significant increase in apoptotic activities, expression of Bax, and p53 with significant decrease in cell viability. These findings pinpoint the possibility of D. anomala methanol root extract of being employed as a natural antiproliferative agent in the treatment of various cancers.

Highly Efficient Separation of Ultratrace Radioactive Copper Using a Flow Electrolysis Cell

Preparing compounds containing the radioisotope ⁶⁴Cu for use in positron emission tomography cancer diagnostics is an ongoing area of research. In this study, a highly efficient separation method to recover ⁶⁴Cu generated by irradiating the target ⁶⁴Ni with a proton beam was developed by employing a flow electrolysis cell (FE). This system consists of (1) applying a reduction potential for the selective adsorption of ⁶⁴Cu from the target solution when dissolved in HCl and (2) recovering the ⁶⁴Cu deposited onto the carbon working electrode by desorbing it from the FE during elution with 10 mmol/L HNO₃, which applies an oxidation potential. The ⁶⁴Cu was selectively eluted at approximately 30 min under a flow rate of 0.5 mL/min from the injection to recovery. The newly developed flow electrolysis system can separate the femtomolar level of ultratrace radioisotopes from the larger amount of target metals as an alternative to conventional column chromatography.