Breast Cancer Status Research Articles

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. Retrospective. 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. 3 TECHNICAL EFFICACY: Stage 2.

Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer.

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.

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

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

Deep learning modeling using mammography images for predicting estrogen receptor status in breast cancer.

The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer. To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images. Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model. In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views. The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.

Key Biophysical and Physiological Properties Impacting the Oxygenation Status of Breast Cancers During Thermo-radiotherapy.

Mild hyperthermia at 39-43°C for 30-60 min is applied locoregionally to improve the oxygenation status of recurrent breast cancers, thus enhancing the efficacy of radio-, chemo-, and immunotherapy. In this context, estimated (or even conflicting) data are often used in computational modelling of tumour oxygenation and simulation of O2 transport. In this chapter, we present information that may help to improve adjuvant thermotherapy delivered immediately prior to radiotherapy of recurrent breast cancers. Data are preferentially derived from clinical investigations; in some cases, measurements in experimental breast cancers are included.The biophysical properties presented for healthy, mostly postmenopausal, human breast (composite glandular-adipose-fibrous tissue) measured under normothermic (NT) conditions and in therapeutically heated breast cancers include tissue water content and tissue density. In general, averaged values of parameters reported for NT conditions are higher in breast cancers than in normal breast tissue, i.e., all ratios breast cancer/normal breast are >1. Mean values observed in breast cancers during mild hyperthermia (mHT) are consistently higher than those in NT tumours. Parameters determining convective transports in healthy breast tissue and breast cancer include blood flow rates, blood volume, exchanging water space, arterio-venous shunt flow, interstitial fluid flow rate, interstitial fluid pressure, microvascular permeability, interstitial hydraulic conductivity, and interstitial flow velocity. In general, averaged values of parameters measured under NT conditions are higher in breast cancers than in healthy breast. Except for interstitial fluid pressure, these values increase upon mHT treatment of cancers. Prime factors determining and describing the oxygenation status of the healthy breast, and in NT- versus mHT-treated breast cancers, include: oxygen (O2) delivery rates, O2- extractions, O2- consumption rates, subepidermal microvascular HbO2, tissue oxygen solubility, oxygen diffusion coefficients, mean O2 partial pressures pO2, hypoxic fractions HF<5mmHg, oxygen enhancement ratio, and mitochondrial ROS production. With the exception of the mean pO2, O2 extraction rate and tissue O2 saturation all parameters listed are distinctly higher in breast cancers under NT conditions compared to normal breast. Mild hyperthermia results in therapeutically relevant improvements of the oxygenation status of cancers and enhances mitochondrial ROS production, thus improving radiosensitivity. Note: The oxygenation status of the healthy (postmenopausal) breast is very similar to that of the normal human subcutis.

A Retrospective Cohort Study Assessing Ability of Radiological Investigations to Predict Axillary Nodal Status in Breast Cancer

A Retrospective Cohort Study Assessing Ability of Radiological Investigations to Predict Axillary Nodal Status in Breast Cancer

Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.

To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer. 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA. Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net. The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

Characterisation ofIGF-1 (rs7136446) and IL-6 (rs1800795) polymorphisms among breast cancer patients in western Algeria.

Breast cancer (BC) is themost frequently diagnosed cancer among women worldwide, including Algeria. Certain single nucleotide polymorphisms (SNPs) have been linked to higher risk ofBC. Several studies have been performed on theinsulin- like growth factor 1 (IGF-1) T > C (rs7136446) and theinterleukin 6 (IL-6) 174 G > C (rs1800795) SNPs to explore their role in BC. Theaim ofthis study is to investigate theassociation between the2 SNPs, IL-6 (rs1800795) and IGF-1 (rs7136446) with BC in thepopulation ofwestern Algeria. This study was carried out for thefirst time among theAlgerian population. This case-control study included 109 BC patients and 112 healthy controls. Genomic DNA was extracted from peripheral blood samples. DNA concentration was determined using theQubit 2.0 fluorometer. Thegenotyping ofselected SNPs was performed by real-time polymerase chain reaction followed by statistical analysis to assess genotypic frequencies and genetic association with BC. Theresults showed that IGF-1 rs7136446 was positively associated with BC (p = 0.00001) and that thedistribution ofgenotype frequencies ofrs7136446 showed astatistically difference between human epidermal growth factor receptor 2 (HER-2) positive and HER-2 negative BC (p = 0.04), but this association did not last after thecorrection ofBonferroni. No association was found in genotype distribution oftheIL-6 (rs1800795) among controls and BC patients or with clinicopathological parameters including HER-2 status in BC (p > 0.05). In summary, our findings indicate that IGF-1 rs7136446 is associated with BC in our population ofwestern Algeria.

Intramammary Tumor Location and Ipsilateral Lymphatic Spread in Early Breast Cancer Patients Using One‐Step Nucleic Acid Amplification (OSNA) Assay

Establishing an accurate prognosis for women diagnosed with breast cancer (BC) is extremely challenging. Axillary lymph node (ALN) evaluation is considered of major prognostic value. The one‐step nucleic acid amplification (OSNA) assay is currently used for assessing axillary sentinel lymph node (SLN) status in BC. Additionally, total tumor load (TTL) may help predict further metastatic axillary involvement beyond the SLN. The prognostic value of primary BC location remains controversial due to lack of consensus on the biological differences among tumors at various sites. Evidence suggests that tumors in the internal quadrants (INLs) have worse prognosis compared to those in the external quadrants. Furthermore, ALN involvement is believed to be mainly associated with external quadrant tumors, mainly due to the lymphatic drainage system of the breast. This pilot observational study, despite lacking a control group and having a relatively small sample size, is the first to evaluate the potential relationship between primary BC location and ALN metastasis using the OSNA assay. A sample of consecutive BC patients undergoing axillary staging with the OSNA assay were included. Tumors were categorized into three groups based on primary location: external quadrants and axillary tail (EXL), INLs, and nipple and areola location (NAL). Although not statistically significant, the INL group exhibited a higher mean TTL. Additionally, no significant differences were observed between groups concerning SLN detection techniques, SLN status, number of metastatic SLN, or mean TTL. These findings support the use of the innovative tracer superparamagnetic iron oxide regardless of tumor site. This study underscores the importance of understanding the relationship between BC location and ALN status, which may improve prognostic stratification and targeted therapies based on tumor site. If these observations are confirmed in larger, multicentric studies, the potential conclusions may shift the paradigm of INL tumor treatment, significantly impacting clinical practice and research.

Construction and validation of immune-associated lncRNA model for predicting immune status and therapeutic reactions of triple-negative breast cancer.

The immune status of the tumor microenvironment significantly impacts the clinical prognosis of triple-negative breast cancer (TNBC). The involvement of long noncoding RNAs (lncRNAs) in tumor immune infiltration is widely acknowledged. Therefore, it is crucial to explore the role of significant immune-related lncRNAs in TNBC. We acquired RNA, single-cell sequencing, and clinical information on TNBC from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. To identify immune-related lncRNAs, immune infiltration subgroups were determined and verified using single-sample gene-set enrichment analysis, non-negative matrix factorization, and weighted gene co-expression network analysis. CIBERSORTx, deconvolution, drug sensitivity, and Scissor analyses revealed that differences in cell type and drug efficacy were associated with immune grouping. TNBC samples were classified into immune-desert (cold) and immune-inflamed (hot) subgroups based on a lncRNA model (including LINC01550, LY86-AS1, LINC00494, LINC00877, CHRM3-AS2, HCP5, MIR155HG, and PIK3CD-AS1). Furthermore, using in vitro experiments, we found that LINC01550 promoted malignant phenotypes, including proliferation, survival, and migration of TNBC. The immune-inflamed subgroup exhibited significantly lower half-maximal inhibitory concentration values for common anti-tumor drugs, including palbociclib, ribociclib, mitoxantrone, and sorafenib (T-test, P < 0.001). This may be related to the fact that the immune-inflamed subgroup has more plasmacytoid dendritic cells (pDCs) and B cells than those in immune-desert subgroups (P < 0.001). Differences in specific cell infiltration can lead to increased sensitivity of the immune-inflamed subgroup to anti-tumor drugs. The proposed lncRNA model holds great promise to assess the immune landscapes and therapeutic reactions of TNBC patients.

Integrating machine learning algorithms and multiple immunohistochemistry validation to unveil novel diagnostic markers based on costimulatory molecules for predicting immune microenvironment status in triple-negative breast cancer.

Costimulatory molecules are putative novel targets or potential additions to current available immunotherapy, but their expression patterns and clinical value in triple-negative breast cancer (TNBC) are to be clarified. The gene expression profiles datasets of TNBC patients were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Diagnostic biomarkers for stratifying individualized tumor immune microenvironment (TIME) were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Additionally, we explored their associations with response to immunotherapy via the multiplex immunohistochemistry (mIHC). A total of 60 costimulatory molecule genes (CMGs) were obtained, and we determined two different TIME subclasses ("hot" and "cold") through the K-means clustering method. The "hot" tumors presented a higher infiltration of activated immune cells, i.e., CD4 memory-activated T cells, resting NK cells, M1 macrophages, and CD8 T cells, thereby enriched in the B cell and T cell receptor signaling pathways. LASSO and SVM-RFE algorithms identified three CMGs (CD86, TNFRSF17 and TNFRSF1B) as diagnostic biomarkers. Following, a novel diagnostic nomogram was constructed for predicting individualized TIME status and was validated with good predictive accuracy in TCGA, GSE76250 and GSE58812 databases. Further mIHC conformed that TNBC patients with high CD86, TNFRSF17 and TNFRSF1B levels tended to respond to immunotherapy. This study supplemented evidence about the value of CMGs in TNBC. In addition, CD86, TNFRSF17 and TNFRSF1B were found as potential biomarkers, significantly promoting TNBC patient selection for immunotherapeutic guidance.

BRCA mutation status and pathological characterization of breast cancer in Zhoushan Islands, China.

To investigate BRCA1/2 gene mutations and their relationship with clinicopathological features in patients with breast cancer in Zhoushan Islands. High-throughput whole-exome gene sequencing was used to detect BRCA1/2 mutations in 776 breast cancer patients in Zhoushan Islands. The BRCA1/2 mutation rate of breast cancer patients in Zhoushan Islands was 4.38% (34/776). BRCA1 mutations were significantly correlated with age, molecular type, and family history of breast and ovarian cancers. BRCA2 mutations were most commonly found in invasive lobular carcinoma. Moreover, the BRCA2 mutation rate of cancers with molecular type luminal B (receptor protein-tyrosine kinase [HER2]-negative) was also relatively high. The rate of BRCA1/2 mutations in breast cancer patients from Zhoushan Islands is approximately 4.38%, and BRCA1 mutation is related to age, molecular type, and family history of breast and ovarian cancers.

Expression of transferrin receptor 1 and β1-integrins correlates with estrogen receptor status and immune infiltration in breast cancer

To study of transferrin receptor 1 (CD71) expression by primary breast cancer cells in correlation with tumor cell phenotype. Determination of BC phenotype: immunohistochemical staining method (immunofluorescence). Antibodies to ER (estrogen receptors), KL-1 (pancytokeratin), CD71 (transferrin receptor), CD29 (β1-integrins). CD45, CD3, CD4, CD8, CD20 infiltration was also evaluated. ZEISS microscope (AXIOSKOP; Germany), method of G.J. Hammerling et al. Statistical processing: IBM-SPSS Statistics v.21. 63% of BC cases had CD71+ phenotype. CD71-mosaic tumors were observed in 14.4%. β1-integrin expression was monomorphic in 51.6% of cases and mosaic in 38.7%. 85% of ER-positive tumors were CD71-positive with a monomorphic type of reaction; p=0.014. Among ER-negative tumors, CD71-negative reactions were 2-fold more frequent and the monomorphic type was less frequent. ER-positive tumors were CD29-positive in 73%; p=0.031. 45.5% of ER+ tumors were CD29-monomorphic. Among ER-negative tumors, the frequency of CD29-monomorphic tumors was 55%. Significant infiltration by CD3+ cells was predominant in CD71-positive tumors; p=0.016. In the CD29-monomorphic phenotype, CD45+ infiltration was 31.3%, and in the mosaic phenotype, 67.1%. BC aberrantly expresses transferrin receptors, β1-integrins. CD71 expression is associated with ER expression. ER-positive tumors are often monomorphic for CD71. Prominent CD3+ infiltration was present in CD71+ tumors. Expression of β1-integrins correlated with ER+ status and weak immune infiltration.

Prognostic impact of tumor-associated neutrophils in breast cancer.

Neutrophils are the most common type of leukocyte in mammals and play an essential role in the innate immune system and anti-cancer responses. However, recent studies identified the presence of tumor-associated neutrophils (TANs) as a poor prognostic factor. The present study investigated whether relationships exist between TANs and the clinicopathological factors and genetic status of breast cancer. A total of 196 breast cancer patients with sufficient biopsy, breast-conserving surgery, or mastectomy specimens between 2014 and 2021 in Hokuto Hospital were included. TANs were individually counted in the tumor stroma (TS) and tumor nest (TN). A higher density of TANs in both TS and TN correlated with tumor size (TS P = 0.010; TN P = 0.001), a high histological grade (TS P < 0.001; TN P < 0.001), the histological type (TS P = 0.009; TN P = 0.034), a high ratio of lymph node metastasis (TS P < 0.001; TN P < 0.001), an advanced stage of cancer (TS P < 0.001; TN P = 0.002), intrinsic subtypes (TS P < 0.001; TN P < 0.001), ERBB2 (TS P < 0.001; TN P < 0.001), MAP3K1 (TS P = 0.002; TN P = 0.023), and TP53 (TS P < 0.001; TN P < 0.001). A higher density of TANs in TS and TN also correlated with shorter disease-free survival and overall survival (P < 0.001). The present results suggest that a higher density of TANs correlates with unfavorable prognostic factors in breast cancer. Further research on clinicopathological and genetic factors associated with TANs in breast cancer is needed.

ERCC3 Gene Associated with Breast Cancer: A Genetic and Bioinformatic Study

Female breast cancer is the most common and the fifth deadliest cancer worldwide. It is influenced by a combination of genetic, hormonal, and environmental factors. The excision repair cross‐complementation group 3 gene (ERCC3) has recently been identified as a breast cancer susceptibility gene in various cohorts of different geographical and ethnic origin. To explore the role of ERCC3 mutations in breast cancer development and pathological diagnosis, genetic analysis was conducted in 291 patients and 291 controls from mainland China. Bioinformatic analysis and immunohistochemistry (IHC) were performed. A novel ERCC3 mutation p.Y116X was identified in a breast cancer family, while no frequency bias for the genotype and allele of rs754010782 and rs371627165 was observed (all P &gt; 0.05). Bioinformatic analysis revealed that ERCC3 expression was negatively associated with estrogen receptor (ER), progesterone receptor (PR), nontriple‐negative status, and nodal status of breast cancers. ERCC3 amplifications and deep deletions primarily occurred in breast invasive cancer not otherwise specified (NOS) and metaplastic breast cancer, respectively. The decreased ERCC3 expression in tumor tissues of patient with p.Y116X mutation was found by IHC. The ERCC3 mutation p.Y116X may increase breast cancer risk in the Han‐Chinese population. ERCC3 exhibits potential as a biomarker for the pathological diagnosis of breast cancer.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer

This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients. A total of 178 patients were randomly split into a training dataset (N=124) and a validation dataset (N=54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI). The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT_reported_LNM). Incorporating Radscore_Combine(utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT_reported_LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore_Combine could be a valuable biomarker for effectively predicting the status of LVI. The nomogram demonstrated a reliable ability to predict LVI in IBC patients.

Prognostic Role of p53 Immunohistochemical Status in Invasive Breast Cancer. A Retrospective Review of 1387 Cases With Luminal-Like/Her2 Negative Breast Tumors.

The aim of this study was to evaluate the prognostic role of p53 immunohistochemical (IHC) expression in a large cohort of patients with hormone receptors (HR)-positive/Her2-negative primary invasive breast cancer. Retrospective review of consecutive cases treated at our Breast Unit between 2003 and 2013. Patients were divided into 3 subgroups based on p53 IHC expression: null (0%), low (0.1%-49%), and high (≥50%) p53 expression. A total of 1387 patients were included in the study with a median follow-up of 86 months. After adjusting for age, size, node status, lymphovascular invasion, progesterone, and Ki67 expression, only null p53 immunophenotype was associated with worse disease-free survival (DFS) (OR 1.74, 95% IC, 1.11-2.71, P = .015) and distant recurrence-free survival (DRFS) (OR 1.73, 95% IC, 1.04-2.90, P = .036). Null p53 impacted significantly DFS and DRFS also in patients with early breast cancer. p53 IHC expression affects survival and, thus can be a valuable tool in the management of patients with HR-positive/Her2-negative breast cancer.

A Retrospective Study of the Epidemiology, Histomorphological Features, Hormonal Status, and Radiological Features of Breast Cancer Diagnosed on Biopsies in Women in Bahrain Up to the Age of 40: A Single-Center Study.

Breast cancer is the most commonly diagnosed cancer in women worldwide. There are many risk factors that contribute to breast cancer which involvemodifiable and non-modifiable factors. Most of the patients diagnosed with breast cancer are over 50 years of age, with breast cancer in women less than 40 years of age being relatively rare and typically more aggressive variants. Moreover, radiological examination is essential for diagnosis and triaging patients for further diagnostic procedures including tissue biopsy sampling. Despite the rarity of malignancy amongthe younger age group, all of their breast lesions are usually biopsied. Hence, this paper outlines the percentage of benign and malignant breast lesions detected on biopsies obtained from female patients who presented to our hospital before or at the age of 40. We conducted a single institution retrospective study on 267 breast biopsies done for female patientsbefore or at the age of 40 in the period from January 2020 to January 2023. The data was obtained from the National Health Information System at Salmaniya Medical Complex. The data collected included clinical history, histological, and radiological findings. Data on prognostic markers (estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status) were also included. The distribution of samples was established according to age ranges, pathological diagnostic categories (B1-5), and prognostic marker interpretation. Further subdivision was performed on cases with malignancy according to tumor grade. The frequency distribution was obtained for ER, PR, and HER status jointly. The association between age and grade, as well as age and categories, was also determined. All the analyses were performed using SPSS Statistics version 26.0 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp), and the statistical significance was tested at a 5% level. Out of 267 samples, the majority (62.9%) were in patients with a range of age between 30 and 40. There were 68.5% of samples with the B2 (benign) category, followed by 20.2% with the B5b (malignant-invasive carcinoma) category. Out of 61 malignant samples, there were 17 (6.4%) samples positive on ER and PR but negative on HER2, 16 (6.0%) samples negative on ER, PR, and HER2, eight (3.0%) samples negative on ER and PR negative but positive on HER2, and five (8.2%) samples positive on ER, PR, and HER2. The majority of malignant cases were of grade II which accounted for 29 (10.9%) samples, followed by 23 (8.6%) with grade III. The association between age and grade was statistically not significant (p=0.113). However, the association between age and B categories was statistically significant with a p-value of 0.0002. A significantly higher proportion of cases with B5a (malignant-in situ carcinoma) or B5b (malignant-invasive carcinoma) categories were in the age range of 31-35 years and 36-40 years. Breast cancer is rare amongyounger women. It mostly occurs in women over the age of 40 years. In women under the age of 40, it usually presents as a self-detected palpable mass and can show various radiological findings in accordance with the histological grade. Ultrasonography is the main method for the diagnosis of breast cancer especially in younger women, whereas mammography and MRI can contribute to both diagnosis and assessment of the extent of the disease.

Prognostic value of glucose transporter proteins-1 (GLUT1) in breast carcinoma

ABSTRACT Several studies have reported increased glucose transporters (GLUT) expression in different cancer types, including breast cancer. The primary purpose of this study is to examine GLUT1 immunoexpression in breast cancer patients in Saudi Arabia and to determine its significance. The study examined the association between GLUT1 immunophenotype and the clinicopathological characteristics in breast cancer. GLUT1 expression was analyzed in retrospectively collected tissue samples (n = 578) from breast cancer patients using immunohistochemistry. A total of 311 (54%) of the cases expressed GLUT1 cytoplasmic immunohistochemical staining. In univariate analysis, we found a significant association between GLUT1 expression and high-grade tumors (p < 0.0001). Positive estrogen and progesterone receptor results predicted lower GLUT1 immunoexpression (p < 0.0001 for both). Vascular invasion showed a significant association with GLUT1 immunoexpression (p = 0.045). Our findings support that GLUT1 immunohistochemistry can be used as a marker to determine the grade and hormonal receptor status in breast cancer.