Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features

This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.However,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei structures.Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical settings.The existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse datasets.We proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned issues.The study employs BU-NET’s spatial and channel attention methods to enhance segmentation processes.The inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the output.This enhances the robustness of training and convergence processes by reducing the occurrence of vanishing gradients.The primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working representation.The BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)dataset.The framework also achieved 93.92 Area under the ROC Curve(AUC)for TNBC.The results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in segmentation.Furthermore,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical treatments.The research evaluated the efficacy of the proposed method on diverse histopathological imaging datasets,including cancer cells from many organs.The densely connected U-NEt technology offers a promising approach for automating and precisely segmenting cancer cells on histopathology slides,hence assisting pathologists in improving cancer diagnosis and treatment outcomes.

Clinicopathological features,psychological status,and prognosis of 33 patients with occult breast cancer

BACKGROUND Occult breast cancer(OBC)has traditionally been considered to be a carcinoma of unknown primary origin with a favorable prognosis and can be treated as stage II-III breast cancer.Due to the small number of cases and limited clinical experience,treatments vary greatly around the world and no standardized treatment has yet been established.AIM To investigate the clinicopathological features,psychological status and prognostic features of patients with OBC.METHODS The clinicopathological data of 33 OBC patients diagnosed and treated in the Affiliated Hospital of Xuzhou Medical University and Xuzhou Central Hospital from November 2015 to November 2022 were retrospectively analyzed.The psychological status of OBC patients was evaluated by the Self-rating Anxiety Scale and Self-rating Depression Scale.Patients’emotions,stress perception and psychological resilience were evaluated by the Positive and Negative Affect Schedule,the Chinese Perceived Stress Scale,and the Connor-Davidson Resilience Scale(CD-RISC),respectively.Patient survival was calculated using the Kaplan-Meier method,and survival curves were plotted for analysis with the log-rank test.Univariate and multivariate survival analyses were performed using the Cox regression model.RESULTS The 33 OBC patients included 32 females and 1 male.Of the 33 patients,30(91%)had axillary tumors,3(9%)had a neck mass as the primary symptom;18(54.5%)had estrogen receptor-positive tumors,17(51.5%)had progesterone receptor-positive tumors,and 18(54.5%)had Her-2-positive tumors;24(72.7%)received surgical treatment,including 18 patients who underwent modified radical mastectomy,1 patient who underwent breast-conserving surgery plus axillary lymph node dissection(ALND),and 5 patients who underwent ALND alone;12 patients received preoperative neoadjuvant therapy.All 30 patients developed anxiety and depression,with low positive affect scores and high negative affect scores,accompanied by a high stress level and poor psychological resilience.There were no differences in the psychological status of patients according to age,body mass index,or menopausal status.The overall survival and disease-free survival(DFS)of all the patients were 83.3%and 55.7%,respectively.Univariate analysis demonstrated that the initial tumor site(P=0.021)and node stage(P=0.020)were factors that may affect patient prognosis.The 5-year DFS rate of OBC patients who received radiotherapy was greater(P<0.001),while the use of different surgical methods(P=0.687)had no statistically significant effect on patient outcomes.Multivariate analysis revealed that radiotherapy(P=0.031)was an independent prognostic factor.Receiving radiotherapy had a significant effect on the CD-RISC score(P=0.02).CONCLUSION OBC is a rare breast disease whose diagnosis and treatment are currently controversial.There was no significant difference in the efficacy of other less invasive surgical procedures compared to those of modified radical mastectomy.In addition,radiotherapy can significantly improve patient outcomes.We should pay attention to the psychological state of patients while they receive antitumor therapy.

Clinicopathological Features and Long-Term Prognostic Role of Human Epidermal Growth Factor Receptor-2 Low Expression in Chinese Patients with Early Breast Cancer:A Single-Institution Study

Objective This study aimed to comprehensively analyze and compare the clinicopathological features and prognosis of Chinese patients with human epidermal growth factor receptor 2(HER2)-low early breast cancer(BC)and HER2-IHC0 BC.Methods Patients diagnosed with HER2-negative BC(N=999)at our institution between January2011 and December 2015 formed our study population.Clinicopathological characteristics,association between estrogen receptor(ER)expression and HER2-low,and evolution of HER2 immunohistochemical(IHC)score were assessed.Kaplan-Meier method and log-rank test were used to compare the long-term survival outcomes(5-year follow-up)between the HER2-IHC0 and HER2-low groups.Results HER2-low BC group tended to demonstrate high expression of ER and more progesterone receptor(PgR)positivity than HER2-IHC0 BC group(P<0.001).The rate of HER2-low status increased with increasing ER expression levels(Mantel-Haenszelχ^(2)test,P<0.001,Pearson’s R=0.159,P<0.001).Survival analysis revealed a significantly longer overall survival(OS)in HER2-low BC group than in HER2-IHC0 group(P=0.007)in the whole cohort and the hormone receptor(HR)-negative group.There were no significant differences between the two groups in terms of disease-free survival(DFS).The discordance rate of HER2 IHC scores between primary and metastatic sites was 36.84%.Conclusion HER2-low BC may not be regarded as a unique BC group in this population-based study due to similar clinicopathological features and prognostic roles.

Correlation and predictive value of pathological complete response and ultrasound characteristic parameters in neoadjuvant chemotherapy for breast

BACKGROUND Breast cancer ranks as one of the most prevalent malignant tumors among women,significantly endangering their health and lives.While radical surgery has been a pivotal method for halting disease progression,it alone is insufficient for enhancing the quality of life for patients.AIM To investigate the correlation between ultrasound characteristic parameters of breast cancer lesions and clinical efficacy in patients undergoing neoadjuvant chemotherapy(NAC).METHODS Employing a case-control study design,this research involved 178 breast cancer patients treated with NAC at our hospital from July 2019 to June 2022.According to the Miller-Payne grading system,the pathological response,i.e.efficacy,of the NAC in the initial breast lesion after NAC was evaluated.Of these,59 patients achieved a pathological complete response(PCR),while 119 did not(non-PCR group).Ultrasound characteristics prior to NAC were compared between these groups,and the association of various factors with NAC efficacy was analyzed using univariate and multivariate approaches.RESULTS In the PCR group,the incidence of posterior echo attenuation,lesion diameter≥2.0 cm,and Alder blood flow grade≥II were significantly lower compared to the non-PCR group(P<0.05).The area under the curve values for predicting NAC efficacy using posterior echo attenuation,lesion diameter,and Alder grade were 0.604,0.603,and 0.583,respectively.Also,rates of pathological stage II,lymph node metastasis,vascular invasion,and positive Ki-67 expression were significantly lower in the PCR group(P<0.05).Logistic regression analysis identified posterior echo attenuation,lesion diameter≥2.0 cm,Alder blood flow grade≥II,pathological stage III,vascular invasion,and positive Ki-67 expression as independent predictors of poor response to NAC in breast cancer patients(P<0.05).CONCLUSION While ultrasound characteristics such as posterior echo attenuation,lesion diameter≥2.0 cm,and Alder blood flow grade≥II exhibit limited predictive value for NAC efficacy,they are significantly associated with poor response to NAC in breast cancer patients.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Analysis of the Clinical Value of Immunohistochemical Testing in the Pathological Diagnosis of Breast Cancer

Objective:To investigate the clinical value of immunohistochemistry(IHC)detection in the pathological diagnosis of breast cancer.Methods:Eighty breast cancer patients admitted to Baoding No.1 Central Hospital from June 2022 to June 2023 were selected as study subjects.The samples were divided into a positive group(40 cases)and a negative group(40 cases)according to ER and PR test results.Immunohistochemistry was performed on all patients to compare the differences between the two groups in C-erbB-2 positive expression and axillary lymph node metastasis.Results:The positive expression rate of C-erbB-2 in the positive group(35.00%)was significantly lower than that in the negative group(80.00%),with a highly significant difference(P<0.001).The axillary lymph node metastasis rate in the positive group(40.00%)was significantly lower than that in the negative group(75.00%),with a significant difference(P<0.05).Conclusion:Immunohistochemical detection in breast cancer pathology enhances diagnostic accuracy,predicts prognosis,and supports personalized treatment by identifying ER,PR,and C-erbB-2.It is worth being widely adopted in clinical practice.

A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.

A multicenter study on efficacy of dual-target neoadjuvant therapy for HER2-positive breast cancer and a consistent analysis of efficacy evaluation of neoadjuvant therapy by Miller-Payne and RCB pathological evaluation systems(CSBrS-026)

Objective: The aim of this study was to investigate the factors influencing pathological complete response(pCR)rate in early breast cancer patients receiving neoadjuvant dual-target [trastuzumab(H) + pertuzumab(P)] therapy combined with chemotherapy. Additionally, the consistency of the Miller-Payne and residual cancer burden(RCB)systems in evaluating the efficacy of neoadjuvant therapy for early human epidermal growth factor receptor-2(HER2)+ breast cancer was analyzed.Methods: The clinicopathological data of female patients with early-stage HER2+ breast cancer who received dual-target neoadjuvant therapy at 26 hospitals of the Chinese Society of Breast Surgery(CSBrS) from March 2019 to December 2021 were collected. Patients were allocated to four groups: the HER2 immunohistochemistry(IHC)3+/hormone receptor(HR)-, IHC3+/HR+, IHC2+ in situ hybridization(ISH)+/HR-and IHC2+ ISH+/HR+groups. The overall pCR rate for patients, the pCR rate in each group and the factors affecting the pCR rate were analyzed. The consistency between the Miller-Payne and RCB systems in assessing the efficacy of neoadjuvant therapy was analyzed.Results: From March 1, 2019, to December 31, 2021, 77,376 female patients with early-stage breast cancer were treated at 26 hospitals;18,853(24.4%) of these patients were HER2+. After exclusion of unqualified patients, 2,395 patients who received neoadjuvant dual-target(H+P) therapy combined with chemotherapy were included in this study. The overall pCR rate was 53.0%, and the patients' HR statuses and different HER2+ statuses were significantly correlated with the pCR rate(P<0.05). The consistency of the pathological efficacy assessed by the Miller-Payne and RCB systems was 88.0%(κ=0.717, P<0.001).Conclusions: Different HER2 expression statuses and HR expression statuses are correlated with the pCR rate after dual-target neoadjuvant therapy in HER2+ breast cancer patients. There is a relatively good consistency between Miller-Payne and RCB systems in evaluating the pathologic efficacy of neoadjuvant therapy for HER2+breast cancer.

Analysis of clinicopathological features and prognostic factors of breast cancer brain metastasis

BACKGROUND Breast cancer(BC)has become the most common malignancy in women.The incidence and detection rates of BC brain metastasis(BCBM)have increased with the progress of imaging,multidisciplinary treatment techniques and the extension of survival time of BC patients.BM seriously affects the quality of life and survival prognosis of BC patients.Therefore,clinical research on the clinicopathological features and prognostic factors of BCBM is valuable.By analyzing the clinicopathological parameters of BCBM patients,and assessing the risk factors and prognostic indicators,we can perform hierarchical diagnosis and treatment on the high-risk population of BCBM,and achieve clinical benefits of early diagnosis and treatment.AIM To explore the clinicopathological features and prognostic factors of BCBM,and provide references for diagnosis,treatment and management of BCBM.METHODS The clinicopathological data of 68 BCBM patients admitted to the Air Force Medical Center,Chinese People’s Liberation Army(formerly Air Force General Hospital)from 2000 to 2022 were collected.Another 136 BC patients without BM were matched at a ratio of 1:2 based on the age and site of onset for retrospective analysis.Categorical data were subjected to χ^(2) test or Fisher’s exact probability test,and the variables with P<0.05 in the univariate Cox proportional hazards model were incorporated into the multivariate model to identify high-risk factors and independent prognostic factors of BCBM,with a hazard ratio(HR)>1 suggesting poor prognostic factors.The survival time of patients was estimated by the Kaplan-Meier method,and overall survival was compared between groups by log-rank test.RESULTS Multivariate Cox regression analysis showed that patients with stage Ⅲ/Ⅳ tumor at initial diagnosis[HR:5.58,95% confidence interval(CI):1.99–15.68],lung metastasis(HR:24.18,95%CI:6.40-91.43),human epidermal growth factor receptor 2(HER2)-overexpressing BC and triple-negative BC were more prone to BM.As can be seen from the prognostic data,52 of the 68 BCBM patients had died by the end of follow-up,and the median time from diagnosis of BC to the occurrence of BM and from the occurrence of BM to death or last follow-up was 33.5 and 14 mo,respectively.It was confirmed by multivariate Cox regression analysis that patients with neurological symptoms(HR:1.923,95%CI:1.005-3.680),with bone metastasis(HR:2.011,95%CI:1.056-3.831),and BM of HER2-overexpressing and triple-negative BC had shorter survival time.CONCLUSION HER2-overexpressing,triple-negative BC,late tumor stage and lung metastasis are risk factors of BM.The presence of neurological symptoms,bone metastasis,and molecular type are influencing prognosis factors of BCBM.

Circular RNAs in breast cancer diagnosis,treatment and prognosis

Breast cancer has surpassed lung cancer to become the most common malignancy worldwide.The incidence rate and mortality rate of breast cancer continue to rise,which leads to a great burden on public health.Circular RNAs(circRNAs),a new class of noncoding RNAs(ncRNAs),have been recognized as important oncogenes or suppressors in regulating cancer initiation and progression.In breast cancer,circRNAs have significant roles in tumorigenesis,recurrence and multidrug resistance that are mediated by various mechanisms.Therefore,circRNAs may serve as promising targets of therapeutic strategies for breast cancer management.This study reviews the most recent studies about the biosynthesis and characteristics of circRNAs in diagnosis,treatment and prognosis evaluation,as well as the value of circRNAs in clinical applications as biomarkers or therapeutic targets in breast cancer.Understanding the mechanisms by which circRNAs function could help transform basic research into clinical applications and facilitate the development of novel circRNA-based therapeutic strategies for breast cancer treatment.

Reversal of tamoxifen resistance by artemisinin in ER+breast cancer:bioinformatics analysis and experimental validation

Breast cancer is the leading cause of cancer-related deaths in women worldwide,with Hormone Receptor(HR)+being the predominant subtype.Tamoxifen(TAM)serves as the primary treatment for HR+breast cancer.However,drug resistance often leads to recurrence,underscoring the need to develop new therapies to enhance patient quality of life and reduce recurrence rates.Artemisinin(ART)has demonstrated efficacy in inhibiting the growth of drug-resistant cells,positioning art as a viable option for counteracting endocrine resistance.This study explored the interaction between artemisinin and tamoxifen through a combined approach of bioinformatics analysis and experimental validation.Five characterized genes(ar,cdkn1a,erbb2,esr1,hsp90aa1)and seven drug-disease crossover genes(cyp2e1,rorc,mapk10,glp1r,egfr,pgr,mgll)were identified using WGCNA crossover analysis.Subsequent functional enrichment analyses were conducted.Our findings confirm a significant correlation between key cluster gene expression and immune cell infiltration in tamoxifen-resistant and-sensitized patients.scRNA-seq analysis revealed high expression of key cluster genes in epithelial cells,suggesting artemisinin’s specific impact on tumor cells in estrogen receptor(ER)-positive BC tissues.Molecular target docking and in vitro experiments with artemisinin on LCC9 cells demonstrated a reversal effect in reducing migratory and drug resistance of drug-resistant cells by modulating relevant drug resistance genes.These results indicate that artemisinin could potentially reverse tamoxifen resistance in ER-positive breast cancer.

Diagnostic challenges and individualized treatment of cervical adenocarcinoma metastases to the breast:A case report

BACKGROUND Cervical cancer is a rare primary tumor resulting in metastases to the breast with few cases reported in literature.Breast metastases are associated with poor prognosis.The following case highlights the diagnostic challenges associated with metastatic cervical cancer to the breast along with individualized treatment.CASE SUMMARY A 44-year-old G7P5025 with no significant past medical or surgical history presented with heavy vaginal to an outside emergency department where an exam and a pelvic magnetic resonance imaging showed a 4.5 cm heterogenous lobulated cervical mass involving upper two thirds of vagina,parametria and lymph node metastases.Cervical biopsies confirmed high grade adenocarcinoma with mucinous features.A positron emission tomography/computed tomography(PET/CT)did not show evidence of metastatic disease.She received concurrent cisplatin with external beam radiation therapy.Follow up PET/CT scan three months later showed no suspicious fluorodeoxyglucose uptake in the cervix and no evidence of metastatic disease.Patient was lost to follow up for six months.She was re-imaged on re-presentation and found to have widely metastatic disease including breast disease.Breast biopsy confirmed programmed death-ligand 1 positive metastatic cervical cancer.The patient received six cycles of carboplatin and paclitaxel with pembrolizumab.Restaging imaging demonstrated response.Patient continued on pembrolizumab with disease control.CONCLUSION Metastatic cervical cancer to the breast is uncommon with nonspecific clinical findings that can make diagnosis challenging.Clinical history and immunohistochemical evaluation of breast lesion,and comparison to primary tumor can support diagnosis of metastatic cervical cancer to the breast.Overall,the prognosis is poor,but immunotherapy can be considered in select patients and may result in good disease response.

Tumor deposits in axillary adipose tissue in patients with breast cancer:Do they matter?

Tumor deposits(TDs)are defined as discrete,irregular clusters of tumor cells lying in the soft tissue adjacent to but separate from the primary tumor,and are usually found in the lymphatic drainage area of the primary tumor.By definition,no residual lymph node structure should be identified in these tumor masses.At present,TDs are mainly reported in colorectal cancer,with a few reports in gastric cancer.There are very few reports on breast cancer(BC).For TDs,current dominant theories suggest that these are the result of lymph node metastasis of the tumor with complete destruction of the lymph nodes by the tumor tissue.Even some pathologists classify a TD as two lymph node metastases for calculation.Some pathologists also believe that TDs belong to the category of disseminated metastasis.Therefore,regardless of the origin,TDs are an indicator of poor prognosis.Moreover,for BC,sentinel lymph node biopsy is generally used at present.Whether radical axillary lymph node dissection should be adopted for BC with TDs in axillary lymph nodes is still inconclusive.The present commentary of this clinical issue has certain guiding significance.It is aimed to increase the awareness of the scientific community towards this under-recognized problem in BC pathology.

RARRES2 regulates lipid metabolic reprogramming to mediate the development of brain metastasis in triple negative breast cancer

Background Triple negative breast cancer(TNBC),the most aggressive subtype of breast cancer,is characterized by a high incidence of brain metastasis(BrM)and a poor prognosis.As the most lethal form of breast cancer,BrM remains a major clinical challenge due to its rising incidence and lack of effective treatment strategies.Recent evidence suggested a potential role of lipid metabolic reprogramming in breast cancer brain metastasis(BCBrM),but the underlying mechanisms are far from being fully elucidated.Methods Through analysis of BCBrM transcriptome data from mice and patients,and immunohistochemical validation on patient tissues,we identified and verified the specific down-regulation of retinoic acid receptor responder 2(RARRES2),a multifunctional adipokine and chemokine,in BrM of TNBC.We investigated the effect of aberrant RARRES2 expression of BrM in both in vitro and in vivo studies.Key signaling pathway components were evaluated using multi-omics approaches.Lipidomics were performed to elucidate the regulation of lipid metabolic reprogramming of RARRES2.Results We found that downregulation of RARRES2 is specifically associated with BCBrM,and that RARRES2 deficiency promoted BCBrM through lipid metabolic reprogramming.Mechanistically,reduced expression of RARRES2 in brain metastatic potential TNBC cells resulted in increased levels of glycerophospholipid and decreased levels of triacylglycerols by regulating phosphatase and tensin homologue(PTEN)-mammalian target of rapamycin(mTOR)-sterol regulatory element-binding protein 1(SREBP1)signaling pathway to facilitate the survival of breast cancer cells in the unique brain microenvironment.Conclusions Our work uncovers an essential role of RARRES2 in linking lipid metabolic reprogramming and the development of BrM.RARRES2-dependent metabolic functions may serve as potential biomarkers or therapeutic targets for BCBrM.

Histopathological and Ultrasound Correlation in Women Presenting with Breast Lumps in Yaoundé, Cameroon

Context: Breast cancer is very deadly among women with higher rates in the developing world. Imaging tools such as ultrasound, can be used to differentiate between the types of breast lumps. This study aimed to determine the value of ultrasound as a first-line examination in the diagnosis of breast masses. Methods: This was a retrospective cross-sectional study of women presenting with breast lumps from November 2022 to June 2023 at the Yaounde General Hospital, which lasted seven months, from November 2022 to June 2023. The sampling was exhaustive and consecutive. Association between variables was studied using the x2 test and concordance between ultrasound and histopathological findings was assessed using the Kappa correlation coefficient. Results: 234 women were included in the study. Their mean age was 46.3 ± 11.4 years. Overall, 15 (6.4%) lumps were benign while 219 (93.6%) were malignant. Triple negative (5.6%) was the most recurrent genomic classification. The correlation between the ultrasound and histopathological findings was significant, with an observed concordance rate at 85.1%, kappa = 0.322 and a p-value Conclusion: The performance of ultrasound in differentiating benign and malignant lesion was high. However, the discordant cases highlight the need for a diagnosis system which blends histopathological and radiological findings for an improved management of patients with breast lumps.

Metochalcone induces senescence-associated secretory phenotype via JAK2/STAT3 pathway in breast cancer

Breast and lung cancers are the leading causes of mortality and most frequently diagnosed cancers in women and men,respectively,worldwide.Although the antitumor activity of chalcones has been extensively studied,the molecular mechanisms of isoliquiritigenin analog 2',4',4-trihydroxychalcone(metochalcone;TEC)against carcinomas remain less well understood.In this study,we found that TEC inhibited cell proliferation of breast cancer BT549 cells and lung cancer A549 cells in a concentration-dependent manner.TEC induced cell cycle arrest in the S-phase,cell migration inhibition in vitro,and reduced tumor growth in vivo.Moreover,transcriptomic analysis revealed that TEC modulated the activity of the JAK2/STAT3 and P53 pathways.TEC triggered the senescence-associated secretory phenotype(SASP)by repressing the JAK2/STAT3 axis.The mechanism of metochalcone against breast cancer depended on the induction of SASP via deactivation of the JAK2/STAT3 pathway,highlighting the potential of chalcone in senescence-inducing therapy against carcinomas.

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

Assessing pathological features of breast cancer via the multimodal information of multiphoton and Raman imaging

For unveiling the pathological evolution of breast cancer, nonlinear multiphoton microscopic(MPM) and confocal Raman microspectral imaging(CRMI) techniques were both utilized to address the structural and constitutional characteristics of healthy(H), ductal carcinoma in situ(DCIS), and invasive ductal carcinoma(IDC) tissues. MPM-based techniques,including two-photon excited fluorescence(TPEF) and second harmonic generation(SHG), visualized label-free and the fine structure of breast tissue. Meanwhile, CRMI not only presented the chemical images of investigated samples with the K-mean cluster analysis method(KCA), but also pictured the distribution of components in the scanned area through univariate imaging. MPM images illustrated that the cancer cells first arranged around the basement membrane of the duct,then proliferated to fill the lumens of the duct, and finally broke through the basement membrane to infiltrate into the stroma.Although the Raman imaging failed to visualize the cell structure with high resolution, it explained spectroscopically the gradual increase of nucleic acid and protein components inside the ducts as cancer cells proliferated, and displayed the distribution pattern of each biological component during the evolution of breast cancer. Thus, the combination of MPM and CRMI provided new insights into the on-site pathological diagnosis of malignant breast cancer, also ensured technical support for the development of multimodal optical imaging techniques for precise histopathological analysis.

Prognostic factors of breast cancer brain metastasis

In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology.Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality.Brain metastases may involve the brain parenchyma and/or leptomeninges.Symptomatic brain metastases develop in 10%-16%of newly recognized cases each year,and this rate increases to 30%in autopsy series.Depending on the size of the metastatic foci,it may be accompanied by extensive vasogenic edema or may occur as small tumor foci.Since brain metastases are a significant cause of morbidity and mortality,early diagnosis can have significant effects on survival and quality of life.The risk of developing brain metastases emerges progressively due to various patient and tumor characteristics.Patient variability may be particularly important in the susceptibility and distribution of brain metastases because malignant blood must cross the brain barrier and move within the brain parenchyma.Some characteristics of the tumor,such as gene expression,may increase the risk of brain metastasis.Clinical growth,tumor stage,tumor grade,growth receptor positivity,HER2 positivity,molecular subtype(such as triple negative status,luminal/nonluminal feature)increase the risk of developing breast cancer metastasis.Factors related to survival due to breast cancer brain metastasis include both tumor/patient characteristics and treatment characteristics,such as patient age,lung metastasis,surgery for brain metastasis,and HER2 positivity.If cases with a high risk of developing brain metastasis can be identified with the help of clinical procedures and artificial intelligence,survival and quality of life can be increased with early diagnosis and treatment.At the same time,it is important to predict the formation of this group in order to develop new treatment methods in cases with low survival expectancy with brain metastases.

Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.