Breast Cancer Screening, Mammography, and Other Modalities

Circ_RPPH1 facilitates progression of breast cancer via miR-1296-5p/TRIM14 axis

Circular RNA Ribonuclease P RNA Component H1 (circ_RPPH1) and microRNA (miRNA) miR-1296-5p play a crucial role in breast cancer (BC), but the molecular mechanism is vague. Evidence showed that miR-1296-5p can activate tripartite motif-containing 14 (TRIM14). Clinical indications of eighty BC patients were collected and the circ_RPPH1 expression was detected using real-time quantitative PCR. MCF-7 and MDA-MB-231 cells were transfected with overexpression or knockdown of circ_RPPH1, miR-1296-5p, or TRIM14. Cell counting kit-8, cell cloning formation, wound healing, Transwell, and flow cytometry assays were performed to investigate the malignant phenotype of BC. The dual-luciferase reporter gene analyses were applied to reveal the interaction between these target genes. Subcutaneous tumorigenic model mice were established with circ_RPPH1 overexpression MDA-MB-231 cells in vivo; the tumor weight and volume, levels of miR-1296-5 and TRIM14 mRNA were measured. Western blot and immunohistochemistry were used to detect TRIM14 in cells and mice. Circ_RPPH1 levels were notably higher in BC patients and have been found to promote cell proliferation, invasion, and migration of BC cells. Circ_RPPH1 altered cell cycle and hindered apoptosis. Circ_RPPH1 knockdown or miR-1296-5p overexpression inhibited the malignant phenotype of BC. Furthermore, miR-1296-5p knockdown reversed circ_RPPH1's promotion effects on BC. Interestingly, TRIM14 overexpression counteracts the inhibitory effects of miR-1296-5p overexpression and circ_RPPH1 silencing on BC. Moreover, in BC tumor-bearing mice, circ_RPPH1 overexpression led to increased TRIM14 expression and facilitated tumor growth. Circ_RPPH1 enhanced BC progression through miR-1296-5p/TRIM14 axis, indicating its potential as a biomarker and therapeutic target in BC.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BACKGROUND

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

OBJECTIVES

This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

METHODS

A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.

RESULTS

The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.

CONCLUSION

By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

Meta-analysis of contrast-enhanced ultrasound in differential diagnosis of breast adenosis and breast cancer

This systematic review and meta-analysis study aimed to determine the total capacity of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of breast lesions and breast cancer. For collecting papers, four groups of keywords were searched in five databases. The required information was extracted from the selected papers. In addition to the descriptive findings, a meta-analysis was also conducted. Thirty-three of thirty-six studies (91.67%) on the differential diagnosis of various degrees and types of breast lesions showed that CEUS has proper performance. The pooled values related to the sensitivity and specificity of CEUS were computed by 88.00 and 76.17.

Clinical Diagnostic Value of Shear-Wave Elastography in Detecting Malignant Nipple Retraction

OBJECTIVES

In recent years, the use of shear-wave elastography (SWE) as a diagnostic tool for detecting malignant breast lesions has shown promising results. This study aims to determine the clinical diagnostic value of SWE in detecting malignant nipple retraction.

METHODS

Both US and SWE (Philips EPIQ7 system) were performed for 41 consecutive patients with nipple retraction (56 nipples). The mean, median, and maximum tissue elasticity values (in kilopascals) were determined for each nipple by using SWE. The sensitivity, specificity, and overall accuracy of each measurement was determined by using the surgical pathology results or clinical diagnosis as the gold standard.

RESULTS

Of the 56 retracted nipples, 32 were due to benign lesions, and 24 were due to malignant lesions. No significant differences in dimensions or echo features were found between the benign and malignant groups. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the color Doppler flow imaging (CDFI) pattern were 63.89% (23/36), 95% (19/20), 95.83 (23/24), 59.38 (19/32), and 75% (42/56), respectively; the corresponding values for median elasticity on SWE were 88.46% (23/26), 96.67% (29/30), 95.83 (23/24), 90.63 (29/32), and 92.85 (52/56), respectively.

CONCLUSIONS

The addition of SWE to conventional US could help differentiate benign from malignant lesions associated with nipple retraction.

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Image quality of opportunistic breast examinations in photon-counting computed tomography: A phantom study

PURPOSE

To compare the breast imaging performance of a clinical whole-body photon-counting CT (PCCT) to that of a dedicated breast CT (BCT) to determine the image quality of opportunistic breast examinations in clinical PCCT.

MATERIALS AND METHODS

To quantify image quality for breast cancer applications, acquisitions of a breast phantom including representations of calcifications, fibers, and masses were performed using a clinical PCCT and a dedicated BCT. When imaging with the PCCT, the phantom was also combined with a thorax phantom to simulate realistic patient positioning, while only the breast phantom was imaged in the BCT. Images in BCT were acquired at 7.0 mGy (CTDI16cm) and using 2.6 mGy-25.0 mGy in the PCCT. Spatial resolution between the BCT and PCCT images was matched and data were reconstructed using the default methods of each system. The dose-normalized contrast-to-noise ratio (CNRD) of masses and the structural visibility of fibers and calcifications were evaluated as figures of merit for all reconstructions.

RESULTS

CNRD between masses and background was 0.56 mGy-½, on average with BCT and varied between 0.39 mGy-½ to 1.46 mGy-½ with PCCT over all dose levels, phantom configurations, and reconstruction algorithms. Calcifications down to a size of 0.29 mm and fibers down to a size of 0.23 mm could be reliably identified in the images of both systems.

CONCLUSIONS

Clinical PCCT provides an image quality superior to that obtained with BCT in terms of CNRD and allows for the identification of calcifications and fibers at comparable dose levels.

AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions

Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions. The Globally-aware Multiple Instance Classifier (GMIC) and Global-Local Activation Maps (GLAM) models were fine-tuned for our dataset. We evaluated the sensitivity of both models on missed cancers retrospectively identified by a panel of three radiologists who reviewed prior examinations of 729 cancer cases detected in a screening program with double reading practice. Two of these experts annotated the lesions, and based on their concordance levels, cases were categorized as 'almost perfect,' 'substantial,' 'moderate,' and 'poor.' We employed Similarity or Histogram Intersection (SIM) and Kullback-Leibler Divergence (KLD) metrics to compare saliency maps of malignant cases from the AI model with annotations from radiologists in each category. In total, 24.82% of cancers were labeled as "missed." The performance of GMIC and GLAM on the missed cancer cases was 82.98% and 79.79%, respectively, while for the true screen-detected cancers, the performances were 89.54% and 87.25%, respectively (p-values for the difference in sensitivity < 0.05). As anticipated, SIM and KLD from saliency maps were best in 'almost perfect,' followed by 'substantial,' 'moderate,' and 'poor.' Both GMIC and GLAM (p-values < 0.05) exhibited greater sensitivity at higher concordance. Even in a screening program with independent double reading, adding AI could potentially identify missed cancers. However, the challenging-to-locate lesions for radiologists impose a similar challenge for AI.

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study

BACKGROUND

Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience.

PURPOSE

To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis.

STUDY TYPE

Retrospective.

POPULATION

1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets.

FIELD STRENGTH/SEQUENCE

3.0T/dynamic contrast-enhanced (DCE)-MRI sequence.

ASSESSMENT

DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data.

STATISTICAL TESTS

Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value.

RESULTS

The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation.

DATA CONCLUSION

The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 3.

Knowledge and Attitudes Regarding Breast Cancer Screening and Mammograms Among Women Aged 40 Years and Older in the United Arab Emirates

OBJECTIVES

To assess the knowledge and attitude regarding breast cancer screening and mammograms among 40 years and older females in the United Arab Emirates.

METHODS

 A cross-sectional questionnaire-based study was conducted on women faculty, staff, and female patients attending our hospital. The inclusion criteria were women ≥ 40 years old who agreed to participate. The exclusion criteria were women < 40 and those ≥ 40 years who refused to participate. A signed informed consent was taken. A p-value of < 0.5 was considered significant.

RESULTS

 Among the 460 women enrolled, 420 completed the survey (response rate 91%). The mean age was 48.4 ± 8.2 years. A total of 63.4% of the participants were < 50 years of age. A total of 53.3% were never screened before. About 98% believed that screening is beneficial in early detection. Social media (52.2%) and health professionals (46%) played a vital role in creating awareness. The majority of women were aware of self-breast examinations (73.3%), followed by mammography (68.6%). About 84% and 68.3%, of the participants had incorrect knowledge of the timing and frequency of mammograms, respectively. Only 16.3% of the participants were recommended by their physician, while the rest (83.7%) performed screening based on their awareness. No significant association was found between nutritional status (p=0.252), age at first pregnancy (p=0.409), or having children (p= 0.377) with mammogram uptake. There was a significant association between the perceived benefit of screening and mammogram uptake (p=0.033). There was a positive association between radiation therapy to the chest area and mammogram uptake (p<0.024). A statistically significant association was found between the correct timing of mammograms with family history of cancer (p = 0.037) and previous exposure to radiation therapy to the chest (p = 0.002).

CONCLUSION

There is a need to increase knowledge and awareness regarding breast cancer screening and mammograms among women in UAE. Specifically, breast self-examination should be encouraged and recommended.

Harnessing the potential of long non-coding RNAs in breast cancer: from etiology to treatment resistance and clinical applications

Breast cancer (BC) is the most common malignancy among women and a leading cause of cancer-related deaths of females worldwide. It is a complex and molecularly heterogeneous disease, with various subtypes that require different treatment strategies. Despite advances in high-resolution single-cell and multinomial technologies, distant metastasis and therapeutic resistance remain major challenges for BC treatment. Long non-coding RNAs (lncRNAs) are non-coding RNAs with more than 200 nucleotides in length. They act as competing endogenous RNAs (ceRNAs) to regulate post-transcriptional gene stability and modulate protein-protein, protein-DNA, and protein-RNA interactions to regulate various biological processes. Emerging evidence suggests that lncRNAs play essential roles in human cancers, including BC. In this review, we focus on the roles and mechanisms of lncRNAs in BC progression, metastasis, and treatment resistance, and discuss their potential value as therapeutic targets. Specifically, we summarize how lncRNAs are involved in the initiation and progression of BC, as well as their roles in metastasis and the development of therapeutic resistance. We also recapitulate the potential of lncRNAs as diagnostic biomarkers and discuss their potential use in personalized medicine. Finally, we provide lncRNA-based strategies to promote the prognosis of breast cancer patients in clinical settings, including the development of novel lncRNA-targeted therapies.

Does Laterality in Breast Cancer still have the Importance to be Studied? A Meta-analysis of Patients with Breast Cancer

BACKGROUND

Breast cancer (BC) is one of the most common cancers in the world. Studies show that left-sided BC in pre and post-menopausal women leads to double the risk of worse morbidity and mortality and the reasons are uncertain. Finding the relationship between BC laterality and other possible risk factors can be advantageous for the prognosis of BC.

OBJECTIVE

This present study aimed to analyze the relationship between BC laterality and possible risk factors.

METHODS

A total of 6089 studies were screened. 23 studies from 1971 to 2021 met the inclusion criteria and were included in the meta-analysis. A pooled relative risk was generated via meta-analysis with a 95% confidence interval.

RESULTS

Left-side BC laterality was significant (p < 0.00001) in the women populations compared to the right side based on the pooled size with possible high-risk factors, including handedness, older women, body mass index, people with black skin, invasive type carcinoma, and estrogen receptor-negative BC. These findings suggest that there may be a complex interplay of genetic, environmental, and lifestyle factors that contribute to left-side BC laterality.

CONCLUSION

Results suggest an increased rate of BC on the left side, with high-risk factors contributing to BC laterality, which may be useful in predicting prognosis. This study provides significant insights into the relationship between high-risk factors and BC laterality. By identifying potential risk factors associated with left-side BC, it may be possible to improve the ability to predict prognosis and develop more targeted treatment strategies. This information could be particularly useful for healthcare providers and patients, as it may guide decisions regarding screening, prevention, and treatment, ultimately improving patient outcomes and reducing the overall burden of BC.

Exploring the Relationship Between CD 166 Expression and Breast Imaging Reporting and Data System (BI-RADS) Scores in Breast Cancer Patients and Healthy Volunteers

BACKGROUND

This research embarked on a crucial endeavor to clarify the connection between levels of CD166 expression and the established Breast Imaging Reporting and Data System (BI-RADS) grading system. Through a comprehensive exploration of this correlation, the objective was to ascertain if CD166 could function as an additional biomarker, enhancing the predictive effectiveness of the BI-RADS classification.

METHOD

This prospective observational study involved 81 women with histopathologically confirmed early breast tumors and 81 radiologically confirmed healthy breast volunteers. The BI-RADS scores of all the participants included in the study were recorded. Before starting treatment, serum, saliva, and urine samples were collected. The CD166 levels were quantified using an enzyme-linked immunosorbent assay.

RESULTS

The study involved the analysis and comparison of the mean and standard deviations of CD166 expression in serum, saliva, and urine across various BI-RADS categories. Notably, statistically significant differentiation was found (p=0.00) across all samples spanning the spectrum of BI-RADS categories.

CONCLUSION

A progressive rise in CD166 concentration coincides with the increasing gradient of the BI-RADS category, implying a possible link between CD166 and breast cancer progression and severity.

Equipment downtime in the radiology departments of three teaching hospitals in Ghana

INTRODUCTION

Radiology equipment requires routine maintenance to prevent equipment breakdown. Equipment breakdown can lead to adverse patient outcomes and lost revenue to hospitals. In Ghana, there is a mismatch between the available radiology equipment and requests which may result in frequent breakdown. Several studies have been conducted to investigate equipment downtime across radiology departments. However, there is none for Ghana. This study therefore investigated the downtimes of radiology equipment across three hospitals in Ghana.

METHOD

The study covered the period January-December 2020. An inventory sheet was used to collect data on equipment specifications, frequency of breakdown, downtimes, average daily patient throughput, the average cost of common examinations, the availability of post-installation training and maintenance contracts/agreements.

RESULTS

The study reviewed 32 items of radiology equipment. Radiology equipment across the hospitals broke down frequently and downtimes were very high. Radiographers/radiologists across the hospitals were provided with poor/inadequate post-installation training, and maintenance contracts/agreements were unavailable. The radiology equipment downtimes resulted in significant lost revenue of GH₵ 16,279,803 (US$ 1,968,537).

CONCLUSION

Radiology equipment across the hospitals broke down frequently and downtimes were lengthy leading to significant lost revenue for the hospitals. Post-installation trainings were poor/inadequate, spanning a few hours. Also, maintenance contracts/agreements were non-existent across the three hospitals. A nationwide study is needed to determine equipment downtimes and lost revenue across all radiology departments in Ghanaian hospitals, to better inform policy-making.

IMPLICATIONS FOR PRACTICE

This study may help hospital managers and other stakeholders involved in policy formulation and strategic planning, put measures in place to minimise radiology equipment breakdown in Ghana. The study may also help optimise radiology services and enable radiology departments to render uninterrupted clinical services to patients in Ghana.

A case-control study on the association between adherence to a Mediterranean-style diet and breast cancer

Background

Previous studies on the association between diet and breast cancer are mostly from Western populations, and data from Middle East countries are scarce, where the prevalence of breast cancer is high; therefore, it ranks first among other cancers. This population-based case-control study aimed to investigate the relationship between a Mediterranean-style diet and breast cancer among Iranian women.

Methods

In the current study, 350 new cases of breast cancer and 700 age- and socioeconomic status-matched controls were enrolled. We evaluated the dietary intakes of participants by using a 106-item Willett-format semi-quantitative dish-based food frequency questionnaire (SQ-FFQ). We calculated the Mediterranean diet score according to the dietary intakes of participants. In addition, using pre-tested questionnaires, we collected information on potential confounding variables.

Results

In this study, we found a significant inverse association between the Mediterranean diet and breast cancer so that after controlling for potential confounders, individuals in the highest tertile of the Mediterranean diet score compared with those in the lowest tertile were 57% less likely to have breast cancer [odds ratio (OR): 0.43, 95% confidence interval (CI): 0.28-0.67]. Such an inverse association was also observed for postmenopausal women. Similarly, after controlling for potential confounding variables, high adherence to the Mediterranean dietary pattern was associated with lower odds of breast cancer (OR: 0.37, 95% CI: 0.23-0.60). However, this relationship was not significant among premenopausal women.

Conclusion

We found that adherence to Mediterranean dietary pattern was associated with reduced odds of breast cancer. Studies with prospective design are needed to further examine this association.

Vaccines in Breast Cancer: Challenges and Breakthroughs

Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.

Circulating tumour cells for early detection of clinically relevant cancer

Given that cancer mortality is usually a result of late diagnosis, efforts in the field of early detection are paramount to reducing cancer-related deaths and improving patient outcomes. Increasing evidence indicates that metastasis is an early event in patients with aggressive cancers, often occurring even before primary lesions are clinically detectable. Metastases are usually formed from cancer cells that spread to distant non-malignant tissues via the blood circulation, termed circulating tumour cells (CTCs). CTCs have been detected in patients with early stage cancers and, owing to their association with metastasis, might indicate the presence of aggressive disease, thus providing a possible means to expedite diagnosis and treatment initiation for such patients while avoiding overdiagnosis and overtreatment of those with slow-growing, indolent tumours. The utility of CTCs as an early diagnostic tool has been investigated, although further improvements in the efficiency of CTC detection are required. In this Perspective, we discuss the clinical significance of early haematogenous dissemination of cancer cells, the potential of CTCs to facilitate early detection of clinically relevant cancers, and the technological advances that might improve CTC capture and, thus, diagnostic performance in this setting.

DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection

Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer's performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management.

Development and validation of a four-dimensional registration technique for DCE breast MRI

BACKGROUND

Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.

RESULTS

After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient K^trans as surrogate parameter for motion artifacts. Mean K^trans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).

CONCLUSIONS

Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis

Early diagnosis of breast cancer (BC), as the most common cancer among women, increases the survival rate and effectiveness of treatment. MicroRNAs (miRNAs) control various cell behaviors, and their dysregulation is widely involved in pathophysiological processes such as BC development and progress. In this study, we aimed to identify potential miRNA biomarkers for early diagnosis of BC. We also proposed a consensus-based strategy to analyze the miRNA expression data to gain a deeper insight into the regulatory roles of miRNAs in BC initiation. Two microarray datasets (GSE106817 and GSE113486) were analyzed to explore the differentially expressed miRNAs (DEMs) in serum of BC patients and healthy controls. Utilizing multiple bioinformatics tools, six serum-based miRNA biomarkers (miR-92a-3p, miR-23b-3p, miR-191-5p, miR-141-3p, miR-590-5p and miR-190a-5p) were identified for BC diagnosis. We applied our consensus and integration approach to construct a comprehensive BC-specific miRNA-TF co-regulatory network. Using different combination of these miRNA biomarkers, two novel diagnostic models, consisting of miR-92a-3p, miR-23b-3p, miR-191-5p (model 1) and miR-92a-3p, miR-23b-3p, miR-141-3p, and miR-590-5p (model 2), were obtained from bioinformatics analysis. Validation analysis was carried out for the considered models on two microarray datasets (GSE73002 and GSE41922). The model based on similar network topology features, comprising miR-92a-3p, miR-23b-3p and miR-191-5p was the most promising model in the diagnosis of BC patients from healthy controls with 0.89 sensitivity, 0.96 specificity and area under the curve (AUC) of 0.98. These findings elucidate the regulatory mechanisms underlying BC and represent novel biomarkers for early BC diagnosis.

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.

Exosomes in Breast Cancer: Involvement in Tumor Dissemination and Prospects for Liquid Biopsy

In women, breast cancer (BC) is the most commonly diagnosed cancer (24.5%) and the leading cause of cancer death (15.5%). Understanding how this heterogeneous disease develops and the confirm mechanisms behind tumor progression is of utmost importance. Exosomes are long-range message vesicles that mediate communication between cells in physiological conditions but also in pathology, such as breast cancer. In recent years, there has been an exponential rise in the scientific studies reporting the change in morphology and cargo of tumor-derived exosomes. Due to the transfer of biologically active molecules, such as RNA (microRNA, long non-coding RNA, mRNA, etc.) and proteins (transcription factors, enzymes, etc.) into recipient cells, these lipid bilayer 30-150 nm vesicles activate numerous signaling pathways that promote tumor development. In this review, we attempt to shed light on exosomes' involvement in breast cancer pathogenesis (including epithelial-to-mesenchymal transition (EMT), tumor cell proliferation and motility, metastatic processes, angiogenesis stimulation, and immune system repression). Moreover, the potential use of exosomes as promising diagnostic biomarkers for liquid biopsy of breast cancer is also discussed.

Mitochondrial mutations and mitoepigenetics: Focus on regulation of oxidative stress-induced responses in breast cancers

Epigenetic regulation of mitochondrial DNA (mtDNA) is an emerging and fast-developing field of research. Compared to regulation of nucler DNA, mechanisms of mtDNA epigenetic regulation (mitoepigenetics) remain less investigated. However, mitochondrial signaling directs various vital intracellular processes including aerobic respiration, apoptosis, cell proliferation and survival, nucleic acid synthesis, and oxidative stress. The later process and associated mismanagement of reactive oxygen species (ROS) cascade were associated with cancer progression. It has been demonstrated that cancer cells contain ROS/oxidative stress-mediated defects in mtDNA repair system and mitochondrial nucleoid protection. Furthermore, mtDNA is vulnerable to damage caused by somatic mutations, resulting in the dysfunction of the mitochondrial respiratory chain and energy production, which fosters further generation of ROS and promotes oncogenicity. Mitochondrial proteins are encoded by the collective mitochondrial genome that comprises both nuclear and mitochondrial genomes coupled by crosstalk. Recent reports determined the defects in the collective mitochondrial genome that are conducive to breast cancer initiation and progression. Mutational damage to mtDNA, as well as its overproliferation and deletions, were reported to alter the nuclear epigenetic landscape. Unbalanced mitoepigenetics and adverse regulation of oxidative phosphorylation (OXPHOS) can efficiently facilitate cancer cell survival. Accordingly, several mitochondria-targeting therapeutic agents (biguanides, OXPHOS inhibitors, vitamin-E analogues, and antibiotic bedaquiline) were suggested for future clinical trials in breast cancer patients. However, crosstalk mechanisms between altered mitoepigenetics and cancer-associated mtDNA mutations remain largely unclear. Hence, mtDNA mutations and epigenetic modifications could be considered as potential molecular markers for early diagnosis and targeted therapy of breast cancer. This review discusses the role of mitoepigenetic regulation in cancer cells and potential employment of mtDNA modifications as novel anti-cancer targets.

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.

Advances in breast cancer screening modalities and status of global screening programs

Breast cancer (BC) is the most prevalent malignancy worldwide, and a continued upward trend has been predicted in the coming decades. Screening in selected targeted populations, which is effective in reducing cancer-related mortality, has been widely implemented in many countries. This review summarizes the advances in BC screening techniques, organized or opportunistic BC screening programs across different countries, and screening modalities recommended by different academic authorities. Mammography is the most widely used and effective technique for BC screening. Other complementary techniques include ultrasound, clinical breast examination, and magnetic resonance imaging. Novel screening tests, including digital breast tomosynthesis and liquid biopsies, are still under development. Globally, the implementation status of BC screening programs is uneven, which is reflected by differences in screening modes, techniques, and population coverage. The recommended optimal screening strategies varied according to the authoritative guidelines. The effectiveness of current screening programs is influenced by several factors, including low detection rate, high false-positive rate, and unsatisfactory coverage and uptake rates. Exploration of accurate BC risk prediction models and the development of risk-stratified screening strategies are highly warranted in future research.

Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head

INTRODUCTION AND BACKGROUND

Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality.

METHODS

This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets.

RESULTS

A detailed evaluation of the proposed model's accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset.

CONCLUSIONS

The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.

Artificial intelligence for breast cancer analysis: Trends & directions

Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

Patients with oral preneoplastic lesions and integration of dental pathology referrals

PURPOSE

Oral cancers lack standardized monitoring systems. Our institution has developed an active surveillance system which provides detailed monitoring and follow up of patients with oral preneoplastic lesions (OPL). We examined a historic cohort of patients with OPL seen by regional dental professionals and a current cohort of clinic patients. The major aim was to examine follow up practices for biopsy proven dysplasia to gauge appropriateness of an active monitoring system for oral carcinoma.

MATERIALS AND METHODS

Questionnaires regarding patients with OPL were sent to 285 dentists who had requested oral pathology services from our institution. The follow up practices of 141 dentists were evaluated for patients with OPL. We then examined our current clinic referral patterns for the number of dental referrals after the creation of an oral carcinoma active surveillance clinic.

RESULTS

There were 76.5% (108/141) of patients who received follow up after diagnosis of preneoplastic oral lesions with 14.1% who underwent repeat biopsy. There was a malignant transformation rate of 11.3% including transformation of 42.8% of severe dysplasias into carcinoma within 2 years. After establishment of a dental referral clinic, 21.8% of tumor visits in a six-week period were referred from the regional dental community.

CONCLUSIONS

A high rate of transformation of OPL to cancer in this cohort may support a role for joint dental and otolaryngology surveillance of dysplasia with longitudinal follow up.

Diagnostic accuracy and prognostic value of three-dimensional electrical impedance tomography imaging in patients with breast cancer

Background

Three-dimensional electrical impedance tomography (3D EIT) is a novel, non-invasive, radiation-free imaging technology for breast cancer screening. This study aimed to identify characteristics and classification of 3D EIT breast cancer imaging that could provide diagnostic accuracy and prognostic value for breast cancer patients.

Methods

A total of 645 suspicious breast lesions [Breast Imaging Reporting and Data System (BI-RADS) III, IV, V] identified by mammography or ultrasound were examined with 3D EIT (MEIK, SIM-Technika, Yaroslavl, Russia). Breast tissue conductivity was quantified using MEIK 5.6 software. Diagnostic performance of visually interpreted 3D EIT was assessed using histology (surgical excision or vacuum core biopsy) and clinical follow-up. Kaplan-Meier analysis was used to calculate progression-free survival (PFS) and overall survival (OS) rates. Hazard ratio (HR) with a 95% confidence interval (95% CI) for various clinicopathological variables were determined using univariate and multivariate Cox regression models.

Results

Breast cancer was confirmed in 272 of 645 patients by histopathology and other diagnostic imaging modalities. Among the confirmed cases, 218 patients had positive 3D EIT findings. The sensitivity, specificity, accuracy, positive likelihood, and negative likelihood ratios of 3D EIT were 80.1%, 75.1%, 77.2%, 70.1%, and 83.8%. There were no significant differences in the diagnostic accuracy, sensitivity, or specificity between 3D EIT and mammography, ultrasound, or combined mammography and ultrasound. 3D EIT breast cancer images were classified into 3 different types, including Ia [non-complicated breast cancer (NCBC), 62 cases], Ib [complicated breast cancer (CBC), 131 cases], and Ic [edematous-infiltrative breast cancer (EIBC), 25 cases], which were associated with tumor size (P<0.001), TNM stage (P<0.001), and lymph node metastasis (P=0.012). At 5-year follow-up, multivariate analysis demonstrated that breast cancer 3D EIT imaging classification was an independent predictor for decreased OS (HR: 2.399, 95% CI: 1.035, 5.564, P=0.041) and PFS (HR: 2.836, 95% CI: 1.555, 5.172, P=0.012) in patients with breast cancer.

Conclusions

3D EIT breast cancer images were classified into 3 types based on different image characteristics. 3D EIT appeared to be useful in clinical diagnostic performance and prognostic evaluation in patients with breast cancer.

Synergistic Theranostics of Magnetic Resonance Imaging and Photothermal Therapy of Breast Cancer Based on the Janus Nanostructures Fe3O4-Aushell-PEG

Background

Satisfactory prognosis of breast cancer (BC) is limited by difficulty in early diagnosis and insufficient treatment. The combination of molecular imaging and photothermal therapy (PTT) may provide a solution.

Methods

Fe₃O₄-Au_shell nanoparticles (NPs) were prepared by thermal decomposition, seeded growth and galvanic replacement and were comprehensively characterized. After conjugated to PEG, NPs were used as MRI and PTT agents in vitro and in vivo.

Results

Fe₃O₄-Au_shell NPs which had uniform Janus-like morphology were successfully synthesized. The Fe₃O₄ had a size of 18 ± 2.2 nm, and the Au_shell had an outer diameter of 25 ± 3.3 nm and an inner diameter of 20 ± 2.9 nm. The NPs showed superparamagnetism, a saturation magnetization of 36 emu/g, and an optical absorption plateau from 700 to 808 nm. The Fe₃O₄-Au_shell NPs were determined to possess good biocompatibility. After PEG coating, the zeta potential of NPs was changed from −23.75 ± 1.37 mV to −13.93 ± 0.55 mV, and the FTIR showed the characteristic C–O stretching vibration at 1113 cm⁻¹. The NPs’ MR imaging implied that the T₂ can be shortened by Fe₃O₄-Au_shell NPs in a concentration-dependent manner, and the Fe₃O₄-Au_shell NPs coated with PEG at the molar ratio of 160 (PEG: NPs) showed the highest transverse relaxivity (r₂) of 216 mM⁻¹s⁻¹. After irradiation at 0.65 W/cm² for 5 min, all cells incubated with the Fe₃O₄-Au_shell-PEG160 NPs (Fe: 30 ppm, Au: 70 ppm) died. After administrated intratumorally, Fe₃O₄-Au_shell-PEG160 notably decreased the signal intensity of tumor in T₂WI images. Under the same irradiation, the temperature of tumors injected with Fe₃O₄-Au_shell-PEG160 quickly rose to 54.6°C, and the tumors shrank rapidly and were ablated in 6 days.

Conclusion

Fe₃O₄-Au_shell-PEG NPs show good r₂ and PTT performance and are promising synergistic theranostic agents of MRI and PTT for BC.

Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

The Usefulness of Spectral Mammography in Surgical Planning of Breast Cancer Treatment-Analysis of 999 Patients with Primary Operable Breast Cancer

Contrast-enhanced spectral mammography (CESM) is a promising, digital breast imaging method for planning surgeries. The study aimed at comparing digital mammography (MG) with CESM as predictive factors in visualizing multifocal-multicentric cancers (MFMCC) before determining the surgery extent. We analyzed 999 patients after breast cancer surgery to compare MG and CESM in terms of detecting MFMCC. Moreover, these procedures were assessed for their conformity with postoperative histopathology (HP), calculating their sensitivity and specificity. The question was which histopathological types of breast cancer were more frequently characterized by multifocality–multicentrality in comparable techniques as regards the general number of HP-identified cancers. The analysis involved the frequency of post-CESM changes in the extent of planned surgeries. In the present study, MG revealed 48 (4.80%) while CESM 170 (17.02%) MFMCC lesions, subsequently confirmed in HP. MG had MFMCC detecting sensitivity of 38.51%, specificity 99.01%, PPV (positive predictive value) 85.71%, and NPV (negative predictive value) 84.52%. The respective values for CESM were 87.63%, 94.90%, 80.57% and 96.95%. Moreover, no statistically significant differences were found between lobular and NST cancers (27.78% vs. 21.24%) regarding MFMCC. A treatment change was required by 20.00% of the patients from breast-conserving to mastectomy, upon visualizing MFMCC in CESM. In conclusion, mammography offers insufficient diagnostic sensitivity for detecting additional cancer foci. The high diagnostic sensitivity of CESM effectively assesses breast cancer multifocality/multicentrality and significantly changes the extent of planned surgeries. The multifocality/multicentrality concerned carcinoma, lobular and invasive carcinoma of no special type (NST) cancers with similar incidence rates, which requires further confirmation.

Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

Apoptotic Effects of Melittin on 4T1 Breast Cancer Cell Line is associated with Up Regulation of Mfn1 and Drp1 mRNA Expression

BACKGROUND AND PURPOSE

Melittin, as the main ingredient of honeybee venom, that has shown anticancer properties. The present study aimed at investigating the cytotoxic impacts of melittin on 4T1 breast cancer cells.

METHODS

Hemolytic activity of different concentrations (0.125, 0.25, 0.5, 1, 2, 4, 8μg/ml) of melittin was assayed and then cytotoxicity of selected concentrations of melittin (2, 4, 8, 16, 32, and 64μg/ml), 2 and 4μg/ml of cisplatin and 0.513, 0.295 and 0.123μg/ml of doxorubicin was evaluated on 4T1 cells using MTT assay. We used Morphological evaluation and flow cytometric analysis was used. Real time PCR was also used to determine mRNA expression of Mfn1 and Drp1 genes.

RESULTS

All compounds showed anti-proliferative effects on the tumor cell line with different potencies. Melittin had higher cytotoxicity against 4T1 breast cancer cells (IC50= 32μg/ml-72h) and higher hemolytic activity (HD50= 1μg/ml), as compared to cisplatin and doxorubicin. Mellitin at 16 and 32μg/ml showed apoptotic effects on 4T1 cells according to the flow cytometric analysis. The Real time PCR analysis of Drp1 and Mfn1 expression in cells treated with 16μg/ml of melittin revealed an up-regulation in Drp1 and Mfn1 genes mRNA expression in comparison with control group. Treatment with 32μg/ml of melittin was also associated with a rise in mRNA expression of Drp1 and Mfn1 as compared to the control group.

CONCLUSION

The results of this study showed that melittin has anticancer effects on 4T1 cell lines in a dose and time dependent manner and can be a good candidate for further research on breast cancer treatment.

Prediction model that combines with multidisciplinary analysis for clinical evaluation of malignancy risk of solid breast nodules

Objective

Few studies have systematically developed predictive models for clinical evaluation of the malignancy risk of solid breast nodules. We performed a retrospective review of female patients who underwent breast surgery or puncture, aiming to establish a predictive model for evaluating the clinical malignancy risk of solid breast nodules.

Method

Multivariable logistic regression was used to identify independent variables and establish a predictive model based on a model group (207 nodules). The regression model was further validated using a validation group (112 nodules).

Results

We identified six independent risk factors (X₃, boundary; X₄, margin; X₆, resistive index; X₇, S/L ratio; X₉, increase of maximum sectional area; and X₁₄, microcalcification) using multivariate analysis. The combined predictive formula for our model was: Z=−5.937 + 1.435X₃ + 1.820X₄ + 1.760X₆ + 2.312X₇ + 3.018X₉ + 2.494X₁₄. The accuracy, sensitivity, specificity, missed diagnosis rate, misdiagnosis rate, negative likelihood ratio, and positive likelihood ratio of the model were 88.39%, 90.00%, 87.80%, 10.00%, 12.20%, 7.38, and 0.11, respectively.

Conclusion

This predictive model is simple, practical, and effective for evaluation of the malignancy risk of solid breast nodules in clinical settings.

Contrast-enhanced ultrasound of breast fibromatosis: a case report

We herein present a rare case of breast fibromatosis, the contrast-enhanced ultrasonography (CEUS) findings of which we believe have never been described. The high similarity between the clinical and imaging manifestations of breast cancer makes its differential diagnosis difficult. In this report, we describe the CEUS findings of a less common type of fibromatosis, discuss the potential value of CEUS to differentiate it from malignant breast lesions, and briefly review the literature.

New Insight into Triple-Negative Breast Cancer Therapy: The Potential Roles of Endoplasmic Reticulum Stress and Autophagy Mechanisms

BACKGROUND

Breast cancer is accounted as the fifth leading cause of mortality among the other cancers. Notwithstanding, Triple Negative Breast Cancer (TNBC) is responsible for 15-20% of breast cancer mortality. Despite many investigations, it remains incurable in part due to insufficient understanding of its exact mechanisms.

METHODS

A literature search was performed in PubMed, SCOPUS and Web of Science databases using the keywords autophagy, Endoplasmic Reticulum (ER) stress, apoptosis, TNBC and the combinations of these keywords.

RESULTS

It was found that autophagy plays a dual role in cancer, so that it may decrease the viability of tumor cells or act as a cytoprotective mechanism. It then appears that using compounds having modulatory effects on autophagy is of importance in terms of induction of autophagic cell death and diminishing the proliferation and metastasis of tumor cells. Also, ER stress can be modulated in order to stimulate apoptotic and autophagic cell death in tumor cells.

CONCLUSION

Perturbation in the signaling pathways related to cell survival leads to the initiation and progression of cancer. Regarding the advancement in the cancer pathology, it seems that modulation of autophagy and ER stress are promising.

Exploring the diagnostic value of photoacoustic imaging for breast cancer: the identification of regional photoacoustic signal differences of breast tumors

We examined 14 benign and 26 malignant breast nodules by a handheld dual-modal PA/US imaging system and analyzed the data using the quantitative and semi-quantitative method. The PA signal spatial density and PA scores of different regions of the benign and malignant nodules were compared, and the diagnostic performances of two diagnostic methods based on PA parameters were evaluated. For both quantitative and semi-quantitative results, significant differences in the distributions of PA signals in different regions of benign and malignant breast lesions were identified. The PA parameters showed good performance in diagnosing breast cancer, indicating the potential of PAI in clinical utilization.

Investigating the Image Quality and Utility of Synthetic MRI in the Breast

Purpose

Synthetic MRI reconstructs multiple sequences in a single acquisition. In the present study, we aimed to compare the image quality and utility of synthetic MRI with that of conventional MRI in the breast.

Methods

We retrospectively collected the imaging data of 37 women (mean age: 55.1 years; range: 20–78 years) who had undergone both synthetic and conventional MRI of T2-weighted, T1-weighted, and fat-suppressed (FS)-T2-weighted images. Two independent breast radiologists evaluated the overall image quality, anatomical sharpness, contrast between tissues, image homogeneity, and presence of artifacts of synthetic and conventional MRI on a 5-point scale (5 = very good to 1 = very poor). The interobserver agreement between the radiologists was evaluated using weighted kappa.

Results

For synthetic MRI, the acquisition time was 3 min 28 s. On the 5-point scale evaluation of overall image quality, although the scores of synthetic FS-T2-weighted images (4.01 ± 0.56) were lower than that of conventional images (4.95 ± 0.23; P < 0.001), the scores of synthetic T1- and T2-weighted images (4.95 ± 0.23 and 4.97 ± 0.16) were comparable with those of conventional images (4.92 ± 0.27 and 4.97 ± 0.16; P = 0.484 and 1.000, respectively). The kappa coefficient of conventional MRI was fair (0.53; P < 0.001), and that of conventional MRI was fair (0.46; P < 0.001).

Conclusion

The image quality of synthetic T1- and T2-weighted images was similar to that of conventional images and diagnostically acceptable, whereas the quality of synthetic T2-weighted FS images was inferior to conventional images. Although synthetic MRI images of the breast have the potential to provide efficient image diagnosis, further validation and improvement are required for clinical application.

The expression of Galectin-3 in breast cancer and its association with metastatic disease: a systematic review of the literature

Breast cancer is the most common form of cancer and the second highest cause of cancer mortality in female patients. The significance of the expression of Galectin-3 has been correlated with various malignancy types and in data from several research papers, the expression of Galectin-3 has been associated with the progression and metastasis of breast cancer. In the present study, the authors' goal is to identify whether the expression of Galectin-3 in breast cancer can be associated with the presence and/or recurrence of a metastatic disease. Both Scopus and PubMed databases were utilized, by inputting the following combination of keywords: (((Breast) AND Metastasis)) AND ((Galectin 3) OR Galectin-3). The time of publication and text availability were not considered when searching the databases and all relevant articles in English were initially accepted. We included one case-control study, three retrospective case studies and one retrospective cohort study. In two of the included studies, the levels in concentration of Galectin-3 were not correlated with a significant difference in prognosis. In two studies, the lacking in expression of Galectin-3 was associated with a worse prognosis and in one of the studies selected, the elevated levels of Galectin-3 were correlated with recurrence of disease in triple negative breast cancer cases. For most of the studies selected for this review, the results were contradictory in regard to the role of Galectin-3 for prognosis and metastatic potential in female breast cancer patients. It is still unclear, whether Galectin-3 can be used as a prognostic marker for advanced breast cancer disease.

Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification

Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.

Overview in Breast Cancer Screening in Lebanon

Breast cancer (BC) is the most common cancer in women and men combined, and it is the second cause of cancer deaths in women after lung cancer. In Lebanon, the same epidemiological profile applies where BC is the leading cancer among Lebanese females, representing 38.2% of all cancer cases. As per the Center for Disease Control, there was a decline in BC mortality rate from 2003 to 2012 reflecting the adoption of national mammographic screening as the gold standard for BC detection by Western countries. The aim of this review study is to summarize current recommendations for BC screening and the available modalities for detecting BC in different countries, particularly in Lebanon. It also aims at exploring the impact of screening campaigns on BC early stage diagnosis in Lebanon. Despite the considerable debates whether screening mammograms provides more harm than benefits, screening awareness should be stressed since its benefits far outweigh its risks. In fact, the majority of BC mortality cases in Western countries are non-preventable by the use of screening mammograms alone. As such, Lebanon adopted a public focus on education and awareness campaigns encouraging early BC screening. Several studies showed the impact of early detection that is reflected by an increase in early stage disease and a decrease in more aggressive stages. Further studies should shed the light on the effect of awareness campaigns on early breast cancer diagnosis and clinical down staging at a national scope; therefore, having readily available data on pre- and post-adoption of screening campaigns is crucial for analyzing trends in mortality of breast cancer origin and reduction in advanced stages diseases. There is still room for future studies evaluating post-campaigns knowledge, attitudes, and practices of women having participated, emphasizing on the barriers refraining Lebanese women to contribute in BC screening campaigns.

Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Backgrounds

Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules.

Methods

Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.

Results

Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.

Conclusions

Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.

Breast artery calcification as a predictor of coronary artery calcification: a cross-sectional study

INTRODUCTION

Coronary artery disease is the main cause of burden of disease in the world. Coronary calcification is seen as an aetiopathological event in the pathogenesis of cardiovascular diseases. Studies have shown that breast artery calcification, which is routinely found in mammography of elderly women, could be predictive of coronary artery calcification.

MATERIAL AND METHODS

In this cross-sectional study, 60 women over 40 years of age were included. All of these patients had undergone mammography after having an indication to undergo a computed tomography-angiography. Breast arterial calcification and calcium scores were determined for each patient, and the paired-t test was used to analyse the data.

RESULTS

The mean age of patients was 49.52 ± 8.83 years. Of these 60 women, 50% were postmenopausal and 50% were not. In 37 (61.7%) cases, mild to severe coronary calcification was observed, and 50 (83.3%) had mild to severe breast arterial calcification. There was a significant correlation between coronary calcification and breast artery calcification (p = 0.001), and there was also a significant relationship between coronary calcification and postmenopausal calcification (p < 0.001).

CONCLUSIONS

Breast artery calcification can be a suitable predictor for coronary artery calcification and is a valid method for predicting cardiovascular disease probability in the future.