HER2-negative Breast Cancer Research Articles

Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer

Background:The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).Methods:One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours.Results:In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively).Conclusion:Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed.

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023

BackgroundRecently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.MethodsWe retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.ResultsWe found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.ConclusionsOverall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.

B Cell Lymphocytes as a Potential Source of Breast Carcinoma Marker Candidates.

Despite advances in the genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein-level information. Nowadays breast cancer clinical treatment selection is based on the immunohistochemical (IHC) determination of four protein biomarkers: Estrogen Receptor 1 (ESR1), Progesterone Receptor (PGR), Human Epidermal Growth Factor Receptor 2 (HER2), and proliferation marker Ki-67. The prognostic correlation of tumor-infiltrating T cells has been widely studied in breast cancer, but tumor-infiltrating B cells have not received so much attention. We aimed to find a correlation between immunohistochemical results and a proteomic approach in measuring the expression of proteins isolated from B-cell lymphocytes in peripheral blood samples. Shotgun proteomic analysis was chosen for its key advantage over other proteomic methods, which is its comprehensive and untargeted approach to analyzing proteins. This approach facilitates better characterization of disease-associated changes at the protein level. We identified 18 proteins in B cell lymphocytes with a significant fold change of more than 2, which have promising potential to serve as breast cancer biomarkers in the future.

Effect of internal subtype on the efficacy of CDK4/6 inhibitor therapy in advanced HR+/HER2breast cancer: A review

The classification of breast cancer (BC) by immunohistochemical phenotypes is widely used in routine clinical practice. However, the genetic profile of the tumor does not always correspond to the pathomorphological one, which can significantly affect the prognosis and predict the effectiveness of therapy in BC. This literature review examines the effectiveness of endocrine therapy depending on the internal subtype of BC, and also presents data on the effectiveness of CDK4/6 inhibitors in these subgroups. It has been shown that during metastasis, the tumor acquires a more aggressive subtype (for example, it switches from luminal to HER2-E or basal-like), which can be stopped when using CDK4/6 inhibitors: the change of the internal subtype passes into a more favorable group.

Evolving Management of Breast Cancer in the Era of Predictive Biomarkers and Precision Medicine.

Breast cancer is the most common cancer among women in the world as well as in the United States. Molecular and histological differentiation have helped clinicians optimize treatments with various therapeutics, including hormonal therapy, chemotherapy, immunotherapy, and radiation therapy. Recently, immunotherapy has become the standard of care in locally advanced triple-negative breast cancer and an option across molecular subtypes for tumors with a high tumor mutation burden. Despite the advancements in personalized medicine directing the management of localized and advanced breast cancers, the emergence of resistance to these therapies is the leading cause of death among breast cancer patients. Therefore, there is a critical need to identify and validate predictive biomarkers to direct treatment selection, identify potential responders, and detect emerging resistance to standard therapies. Areas of active scientific and clinical research include novel personalized and predictive biomarkers incorporating tumor microenvironment, tumor immune profiling, molecular characterization, and histopathological differentiation to predict response and the potential emergence of resistance.

Polymorphic variants of the hOGG1, APEX1, XPD, SOD2, and CAT genes involved in DNA repair processes and antioxidant defense and their association with breast cancer risk.

Breast cancer is one of the leading causes of mortality among women. The most frequently encountered tumors are luminal tumors. Associations of polymorphisms in the hOGG1 (rs1052133), APEX1 (rs1130409), XPD (rs13181), SOD2 (rs4880), and CAT (rs1001179) genes were studied in 313 nonsmoking postmenopausal patients with luminal B subtype breast cancer. The control group consisted of 233 healthy nonsmoking postmenopausal women. Statistically significant associations of the XPD and APEX1 gene polymorphisms with the risk of developing luminal B Her2-negative subtype of breast cancer were observed in a log-additive inheritance model, while the CAT gene polymorphism showed an association in a dominant inheritance model (OR = 1.41; CI 95 %: 1.08-1.85; Padj.= 0.011; OR = 1.39; CI 95 %: 1.07-1.81; Padj = 0.013 и OR = 1.70; CI 95 %: 1.19-2.43; Padj = 0.004, respectively). In the group of elderly women (aged 60-74 years), an association of the CAT gene polymorphism with the risk of developing luminal B subtype of breast cancer was found in a log-additive inheritance model (OR = 1.87; 95 % CI: 1.22-2.85; Padj = 0.0024). Using MDR analysis, the most optimal statistically significant 3-locus model of gene-gene interactions in the development of luminal B Her2-negative subtype breast cancer was found. MDR analysis also showed a close interaction and mutual enhancement of effects between the APEX1 and SOD2 loci and the independence of the effects of these loci from the CAT locus in the formation of luminal B subtype breast cancer.

Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification.

Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.

Re-Evaluating the Omission of Radiation Therapy in Low-Risk Patients With Early-Stage Breast Cancer

Traditionally, management of early-stage breast cancer has required adjuvant radiation therapy following breast conserving surgery, due to decreased local recurrence and breast cancer mortality. However, over the past decade, there has been an increasing emphasis on potential overtreatment of patients with early-stage breast cancer. This has given rise to questions of how to optimize deintensification of treatment in this cohort of patients while maintaining clinical outcomes. A multitude of studies have focused on identification of a subset of patients with invasive breast cancer who were at low risk of local recurrence based on clinicopathologic features and therefore suitable for RT omission. These studies have failed to identify a subset that does not from RT with respect to local control. Several ongoing trials are evaluating alternative approaches to deintensification while focusing on tumor biology. With regards to ductal carcinoma in situ (DCIS), the role of RT has been questioned since breast conservation was utilized. Paralleling invasive disease studies, studies have sought to use clinicopathologic features to identify low risk patients suitable for RT omission but have failed to identify a subset that does not from RT with respect to local control. Use of new assays in patients with DCIS may represent the ideal approach for risk stratification and appropriate deintensification. At this time, when considering deintensification, individualizing treatment decisions with a focus on shared decision making is paramount.

Novel feature selection method for accurate breast cancer classification using Correlation coefficient and Modified GWO Algorithm

Breast cancer is perceived as the most common cause of mortality among women globally. Early detection of this disease is critical to reduce significantly the possibility of death. Machine learning techniques have been proved to be efficient and very successful for an accurate breast cancer diagnosis. In this paper, an efficient hybrid Feature Selection (FS) method named a Correlation technique-Modified Grey Wolf Optimizer (CMGWO) was proposed for accurate breast cancer classification based on dimensionality reduction. The suggested technique is based on two stages: the feature selection step and the classification step. Feature selection is the process of picking the most significant characteristics from a dataset. This stage is crucial in machine learning. Firstly, we focus on the filter method by using a Correlation technique for dimensionality reduction. This technique is intended to eliminate and reduce the number of features by selecting one feature from the other correlated features. Secondly, we use the Modified Grey Wolf Optimization algorithm (MGWO) to locate and determine the most significant features from uncorrelated features. After that, we use multiple classifiers to classify breast cancer disease based on the selected features. The Wisconsin Diagnostic Breast Cancer (WDBC) database was used to prove the performance of our proposed work. The experimental results show that the combination of the correlation method and MGWO for feature selection increases the accuracy rate of classification with a minimum number of features. The performances of different machine learning algorithms were evaluated, including Random Forest classifier (RF), Support Vector Machine (SVM) Classifier, and Naïve Bayes (NB) Classifier for the classification step. The suggested technique proves to be the best approach and reliable one among all studied approaches since it increases classification accuracy to 99.12\% obtained by CMGWO using Random Forest classifier and demonstrates its significance in detecting breast cancer.

Morphological and Textural Data Fusion for Breast Cancer Classification Based on Inter and Intra group Variances

Morphological and Textural Data Fusion for Breast Cancer Classification Based on Inter and Intra group Variances

MACHINE LEARNING METHODS FOR BREAST CANCER CLASSIFICATION BY USING DATA SCIENCE TECHNIQUES

This study explores the sensitivity analysis of various machine learning methods applied to the problem of breast cancer classification. By examining the robustness and performance of different algorithms, we aim to identify the most reliable techniques for accurate diagnosis. We assess the impact of key parameters and data variations on model outcomes to provide a comprehensive understanding of each method's strengths and limitations. Our findings offer valuable insights into the selection and optimization of machine learning models for breast cancer detection, ultimately contributing to improved diagnostic accuracy and patient care.

Lactobacillus, Bifidobacterium and Streptococcus, Probiotic Bacteria Supplements, Induce DNA Repair of Local Breast Cancer Cell Line AMJ13

General Background: Breast cancer is the most prevalent cancer affecting women, with increasing incidence worldwide. Specific Background: Recent research has focused on the role of epigenetic changes in DNA damage, repair mechanisms, and the potential therapeutic effects of probiotics. Probiotics have shown promise in promoting tissue regeneration and DNA repair. Knowledge Gap: However, the precise impact of probiotics on DNA repair in cancer cells, specifically breast cancer cells, remains underexplored. Aims: This study aimed to evaluate the effects of probiotics on DNA damage repair in AMJ13 Iraqi breast cancer cells and assess the cytotoxic effects of probiotics on these cells. Results: Using the comet assay, we found significant increases in DNA damage repair in AMJ13 cells treated with Lactobacillus plantarum (T1) and a combination of eight probiotic strains (T2). Exposure to T1 for 48 hours resulted in significant increases in tail DNA (P≤0.001), head DNA (P≤0.001), and tail moment (P&lt;0.001), while T2 showed similar significant increases at 72 hours (P&lt;0.05). Image analysis further supported the DNA repair potential of probiotics, as indicated by a small tail curve for treated cells. Novelty: This study provides novel insights into the therapeutic potential of probiotics in breast cancer treatment by demonstrating their capacity to enhance DNA repair mechanisms in cancer cells. Implications: The findings suggest that probiotic therapy may be a promising adjunct treatment in breast cancer, offering a new avenue for cancer management through the enhancement of DNA repair and reduction of DNA damage. Highlights: Probiotics significantly repaired DNA damage in breast cancer cells. T1 and T2 enhanced DNA repair within 48-72 hours. Probiotics offer potential as breast cancer adjunct therapy. Keywords: Breast cancer, probiotics, DNA repair, AMJ13 cells, cytotoxicity

Quantitative characterization of the effects of fulvestrant alone or in combination with taselisib (PI3Kinase inhibitor) on longitudinal tumor growth in patients with estrogen receptor-positive, HER2-negative, PIK3CA-mutant, advanced or metastatic breast cancer.

Among cases of breast cancer, estrogen receptor-positive (ER +), PIK3CA-mutant, HER2- advanced breast cancer stands as a particularly complex clinical indication where approximately 40% of ER + /HER2- breast carcinomas present mutations in the PIK3CA gene. A significant hurdle in treating ER + breast cancer lies in surmounting the challenges of endocrine resistance. In the clinical setting, a multifaceted approach is essential for this indication, one that not only explores the effectiveness of individual treatments but also delves into the potential gains in therapeutic outcome from combination therapies. In the current study, longitudinal tumor growth inhibition (TGI) models were developed to characterize tumor response over time in postmenopausal women with ER + /HER2- advanced or metastatic breast cancer undergoing treatment with fulvestrant alone or in combination with the PI3K inhibitor, taselisib. Impact of clinically relevant covariates on TGI metrics was assessed to identify patient subsets most likely to benefit from treatment with fulvestrant monotherapy or combination with taselisib. Tumor growth rate constant (Kg) was found to increase with increasing baseline tumor size and in the absence of baseline endocrine sensitivity. Further, Kg decreased in the absence of baseline liver metastases both in fulvestrant monotherapy and combination therapy with taselisib. Overall, additive/potentially synergistic anti-tumor effects were observed in patients treated with the taselisib-fulvestrant combination. These results have important implications for understanding the therapeutic impact of combination treatment approaches and individualized responses to these treatments. Finally, this work, emphasizes the importance of model informed drug development for targeted cancer therapy. NCT02340221 Registered January 16, 2015, NCT01296555 Registered February 14, 2011.

A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports.

Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks (GNNs) to predict MBC patients based on their histopathology reports. This paper introduces a BERT-GNN approach for metastatic breast cancer prediction (BG-MBC) that integrates graph information derived from the BERT model. In this model, nodes are constructed from patient medical records, while BERT embeddings are employed to vectorise representations of the words in histopathology reports, thereby capturing semantic information crucial for classification by employing three distinct approaches (namely univariate selection, extra trees classifier for feature importance, and Shapley values to identify the features that have the most significant impact). Identifying the most crucial 30 features out of 676 generated as embeddings during model training, our model further enhances its predictive capabilities. The BG-MBC model achieves outstanding accuracy, with a detection rate of 0.98 and an area under curve (AUC) of 0.98, in identifying MBC patients. This remarkable performance is credited to the model's utilisation of attention scores generated by the LLM from histopathology reports, effectively capturing pertinent features for classification.

Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved Coati optimization algorithm

Breast cancer is a significant health concern due to its aggressive nature and high mortality rates. Early detection is crucial to improving patient outcomes. Thermography, a non-invasive and cost-effective method, utilizes heat from the breast surface to detect abnormalities, with the Database for Mastology Research (DMR) leveraging infrared images for research purposes. Deep learning (DL), particularly convolutional neural networks (CNNs), shows promise in accurately classifying breast cancer images. However, CNNs face challenges with hyperparameters. To address these challenges, this article proposes a new DL model, the optimized DenseNet model (LFR-COA-DenseNet121-BC), incorporating a boosted metaheuristic algorithm called LFR-COA. This algorithm, a developed version of the Coati Optimization Algorithm (COA), integrates Random opposition-based learning (ROB), Brownian motion, and Lévy Flight (LF) schemes. The proposed model achieved impressive results, accurately classifying 99.97% of the test set. Comparison with established models such as VGG16, VGG19, DenseNet201, InceptionV3, Xception, and MobileNet revealed superior performance of the LFR-COA-DenseNet121-BC model, with 99.97% accuracy, 99.96% sensitivity, and 99.9% specificity. Further comparison with COA-DenseNet121 highlighted the superiority of LFR-COA-DenseNet-BC. In addition, LFR-COA was evaluated at the IEEE Congress on Evolutionary Computation held in 2022 (CEC 2022), and real-world medical scenarios showcased the effectiveness of LFR-COA compared to existing optimization algorithms. LFR-COA consistently outperformed the original COA algorithm and other well-known counterparts in various statistical, convergence, and diversity measures, affirming its efficacy in breast cancer classification.

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

Cancer Patients' Social Relationships During 3 Years After Diagnosis-Generic and Cancer-Specific Social Networks.

Social relationships are important health resources and may be investigated as social networks. We measured cancer patients' social subnetworks divided into generic social networks (people known to the patients) and disease-specific social networks (the persons talked to about the cancer) during 3years after diagnosis. Newly diagnosed patients with localized breast cancer (n = 222), lymphoma (n = 102), and prostate cancer (n = 141) completed a questionnaire on their social subnetworks at 2-5months after diagnosis and 9, 18, and 36months thereafter. Generic and cancer-specific numbers of persons of spouse/partner; other family; close relatives, in detail; and friends were recorded as well as cancer-specific numbers of persons in acquaintances; others with cancer; work community; healthcare professionals; and religious, hobby, and civic participation. The data was analyzed with regression models. At study entry, most patients had a spouse/partner, all had close relatives (the younger, more often parents; and the older, more often adult children with families) and most also friends. The cancer was typically discussed with them, and often with acquaintances and other patients (74-86%). Only minor usually decreasing time trends were seen. However, the numbers of distant relatives and friends were found to strongly increase by the 9-month evaluation (P < 0.001). Cancer patients have multiple social relationships and usually talk to them about their cancer soon after diagnosis. Most temporal changes are due to the natural course of life cycle. The cancer widened the patients' social networks by including other patients and healthcare professionals and by an increased number of relatives and friends.

Molecular Classification of Breast Cancer Using Weakly Supervised Learning

Molecular Classification of Breast Cancer Using Weakly Supervised Learning

CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study.

The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer. The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism. The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% ( P = 0.027); stage II, 72% ( P = 0.014); stage III, 50% ( P = 0.032); and stage IV, 45% ( P = 0.041). The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.