HER2-negative Breast Cancer Research Articles

Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization

ProblemsRaman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model. AimsUsing Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information. MethodsOptimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks. ResultsCompared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions. ConclusionRaman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.

Proton pump inhibitors decrease efficacy of palbociclib in patients with metastatic breast.

The objective of this investigation was to assess the impact of concurrent proton pump inhibitors (PPIs) on progression-free survival (PFS) in patients with hormone receptor-positive and HER2-negative metastatic breast cancer (mBC) who received palbociclib as first-line or successives therapy. A retrospective observational study was conducted, enrolling patients diagnosed with estrogen receptor-positive, human epidermal growth factor receptor 2-negative mBC, and eligible for palbociclib treatment. Patients were categorized as "concurrent PPIs" if they received PPIs for at least two-thirds of the palbociclib therapy duration, and as "no concurrent PPIs" if they did not receive PPIs during the course of palbociclib treatment. A total of 165 patients were included in the study. Among first-line patients treated with palbociclib, those using concurrent PPIs exhibited a PFS of 8.88 months, while patients using palbociclib without concurrent PPIs had a PFS of 67.81 months (p < 0.0001). In second-line or subsequent treatments, patients on palbociclib with concurrent PPIs had a PFS of 7.46 months, whereas those using palbociclib without concurrent PPIs had a PFS of 17.29 months (p = 0.122). This study demonstrates that the concurrent use of PPIs in mBC patients receiving palbociclib negatively affects PFS, particularly in the first-line setting. Nevertheless, further investigation is warranted to explore the impact of PPIs on cycle-dependent kinase 4/6 inhibitors.

Quality of Life Outcomes in Breast Cancer Patients Receiving Chemotherapy With or Without Radiation Therapy

PurposeUnderstanding quality of life (QOL) implications of individual components of breast cancer treatment is important as systemic therapies continue to improve oncologic outcomes. We hypothesized that adjuvant radiation therapy does not significantly impact QOL domains in breast cancer patients undergoing chemotherapy. MethodsData was drawn from three prospective studies in women with localized breast cancer being treated with chemotherapy from March 2014 to December 2019. Patient-reported measures were collected at baseline (pretreatment) and post-treatment using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) measure, which consists of 5 subscales. Changes in mean QOL scores in patients who received radiotherapy were compared to those who did not using a one-sided noninferiority method. Statistical significance was determined below 0.05 to meet noninferiority. ResultsIn a sample of 175 patients, 131 were treated with radiation and 44 had no radiation. The sample consisted mostly of stage I-II breast cancer (78%) with hormone receptor positive (59%) disease, receiving either neoadjuvant (36%) or adjuvant chemotherapy (64%). Mean change in QOL for the group treated with radiation compared to no radiation was noninferior with respect to Physical Well-Being (P = .0027), Social/Family Well-Being (P = .0002), Emotional Well-Being (P = .0203), FACIT-Fatigue Subscale (P = .0072), and the Total FACIT-F score (P = .0005); however, mean change in QOL did not meet noninferiority for Functional Well-Being (P = .0594). ConclusionPatient-reported QOL from baseline to post-treatment, using the Total FACIT-F score, was noninferior in patients treated with versus without radiation therapy. This finding, in addition to individualized QOL subscales, provides important information in the informed decision-making process when discussing the effects of locoregional radiation on QOL in localized breast cancer patients treated with chemotherapy.

Gene panel predicts neoadjuvant chemoimmunotherapy response and benefit from immunotherapy in HER2-negative breast cancer

BackgroundIt is encountering the dilemma of lacking precise biomarkers to predict the response to neoadjuvant chemoimmunotherapy (NACI) and determine whether patients should use immune checkpoint inhibitors (ICIs) in early breast...

A Phase I Trial of Alpelisib Combined With Capecitabine in Patients With HER2-Negative Metastatic Breast Cancer

BackgroundAlpelisib is an oral α-specific class I PI3K inhibitor approved in combination with fulvestrant for the treatment of PIK3CA-mutated hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) metastatic breast cancer. The tolerability of this drug with the oral chemotherapy capecitabine is unknown. Patients and MethodsThis phase I trial evaluated the dose-limiting toxicities (DLTs) and maximum tolerated dose (MTD) of alpelisib (250 mg or 300 mg daily for 3-weeks) with capecitabine (1000 mg/m2 twice daily for 2-weeks followed by a 1-week rest period) in patients with metastatic HER2-negative breast cancer, regardless of PIK3CA mutation status. ResultsEighteen patients were treated with alpelisib-capecitabine. Half of the patients had HR+ breast cancer, and 16 had prior systemic therapy for metastatic disease. The MTD of alpelisib was 250 mg daily in combination with capecitabine 1000 mg/m2 twice daily. DLTs included hyperglycemia, QTc prolongation, fatigue, and chest pain. The most common grade 3 adverse event (AE) was hyperglycemia (28%). No grade 4 AEs were observed. Three patients discontinued therapy due to an AE. One-third of patients required dose reduction of both alpelisib and capecitabine. Four patients experienced a partial response and 8 patients experienced stable disease. The median progression-free survival was 9.7 months (95% CI 2.8-13.5 months) and median overall survival was 18.2 months (95% CI 7.2-35.2 months). Twelve patients had PIK3CA mutation testing completed, of these 2 had known or likely deleterious PIK3CA mutation. ConclusionThis study provides safety data for an oral combination therapy of alpelisib-capecitabine and defines tolerable doses for further study.

Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

A framework for classifying breast cancer via heterogenetic attention mechanism and optimized feature selection

Breast cancer poses a significant threat to women’s health, emphasizing the crucial role of timely detection. Traditional pathology reports, though widely used, face challenges prompting the development of automated Deep Learning (DL) tools. DL models, gaining traction in radiology, offer precise diagnoses; however, issues with generalization on varying dataset sizes persist. This paper introduces a computationally efficient DL framework, addressing dataset imbalance through a hybrid model design, ensuring both accuracy and speed in breast cancer image classification. Proposed model novel design excels in accuracy and generalization across medical imaging datasets, providing a robust tool for precise diagnostics. The proposed model integrates features from two classifiers, Inception ResNet V2 and Vision Transformers (ViT), to enhance the classification of breast cancer. This synergistic blend enhances adaptability, ensuring consistent performance across diverse dataset scales. A key contribution is the introduction of an Efficient Attention Mechanism within one of the classifiers, optimizing focus on critical features for improved accuracy and computational efficiency. Further, a Resource-Efficient Optimization model through feature selection is proposed, streamlining computational usage without compromising accuracy. Addressing the inherent heterogeneity within classifiers, our framework integrates high dimensional features comprehensively, leading to more accurate tumor class predictions. This consideration of heterogeneity marks a significant leap forward in precision for breast cancer diagnosis. An extensive analysis on datasets, BreakHis and BACH, that are imbalanced in nature is conducted by evaluating complexity, performance, and resource usage. Comprehensive evaluation using the datasets and standard performance metrics accuracy, precision, Recall, F1-score, MCC reveals the model’s high efficacy, achieving a testing accuracy of 0.9936 and 0.994, with precision, recall, F1-score and MCC scores of 0.9919, 0.987, 0.9898, 0.9852 and 0.989, 1.0, 0.993, 0.988 on the BreakHis and BACH datasets, respectively. Our proposed model outperforms state-of-the-art techniques, demonstrating superior accuracy across different datasets, with improvements ranging from 0.25% to 15% on the BACH dataset and from 0.36% to 15.02% on the BreakHis dataset. Our results position the framework as a promising solution for advancing breast cancer prediction in both clinical and research applications. The collective contributions, from framework and hybrid model design to feature selection and classifier heterogeneity consideration, establish a holistic and state-of-the-art approach, significantly improving accuracy and establishing optimization in breast cancer classification from MRI images. Future research for the DL framework in breast cancer image classification includes enhancing interpretability, integrating multi-modal data, and developing personalized treatments.

Real-World Analysis of Breast Cancer Patients Qualifying for Adjuvant CDK4/6 Inhibitors

BackgroundAdjuvant CDK4/6 inhibitors abemaciclib and ribociclib improved disease-free survival (DFS) added to endocrine therapy in hormone receptor (HR)-positive HER2-negative early breast cancer (EBC), in monarchE (NCT03155997) and NATALEE (NCT03701334) trials respectively. We assessed the proportion and outcome of EBC patients qualifying for adjuvant CDK4/6 inhibitors in the real-world. MethodsConsecutive female patients with HR-positive HER2-negative EBC between 1997 and 2017 from the Australian Capital Territory and South-East New South Wales Breast Cancer Treatment Group registry were analyzed. Patients eligible for abemaciclib had ≥4 axillary nodes involved or 1-3 nodes plus primary >5 cm or grade 3. Ribociclib eligibility was defined as node-positive and node-negative with primary >5 cm or >2 cm grade 3. ResultsOf 3840 patients, 671 (17.5%) were abemaciclib-eligible and 1587 (41.3%) ribociclib-eligible . The 5-year DFS was 77% and 94% in abemaciclib-eligible and noneligible registry patients respectively (HR 2.6, 95% CI 2.26-3.05, P < .001). The 5-year DFS was 86% and 97% in ribociclib-eligible and noneligible registry patients respectively (HR 1.92, 95% CI 1.67-2.19, P < .001). Compared with monarchE trial patients, abemaciclib-eligible registry patients were older (median 55 years in registry vs. 51 years in trial), with lower nodal burden (≥4 nodes in 44% in registry vs. 60% in trial). There were more stage III cancers in NATALEE trial patients (60%) than ribociclib-eligible registry patients (24%). ConclusionsMany women with EBC will qualify for adjuvant CDK4/6 inhibitors (17.5% abemaciclib, 41.3% ribociclib) with resource and workforce implications. In the real-world setting, a greater proportion of adjuvant CDK4/6-eligible patients have lower stage disease, therefore the absolute benefit from treatment may be smaller than estimated by the trials.

UGT1A1*28 polymorphism and the risk of toxicity and disease progression in patients with breast cancer receiving sacituzumab govitecan.

Sacituzumab govitecan (sacituzumab) emerged as an important agent in metastatic and locally recurrent HER2-negative breast cancer treatment. UGT1A1 polymorphisms have also been shown to predict sacituzumab toxicity. In this retrospective study, we sought to evaluate the associations between UGT1A1 status, toxicity, and therapeutic outcomes in sacituzumab recipients with advanced breast cancer who underwent genotype testing for UGT1A1 alleles (N = 68). We found 17 (25%) of our patients to be homozygous for UGT1A1*28 and 24 (35.3%) were heterozygous. Of seven African American patients with triple-negative breast cancer, five were homozygous for UGT1A1*28 and two were heterozygous. Patients with a homozygous UGT1A1*28 genotype were significantly more likely to have treatment terminated because of adverse effects. However, the polymorphism was not associated with treatment discontinuation because of disease progression. This retrospective, real-world analysis suggests potential clinical utility in UGT1A1 testing for patients receiving sacituzumab, but future trials are needed to confirm the association between genotypes and treatment outcomes.

Optimizing breast cancer diagnosis: combining hybrid architectures through Apache Spark

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.

Immune and Gene-expression Profiling in Estrogen Receptor Low and Negative Early Breast Cancer.

The cut-off of < 1% positive cells to define estrogen receptor (ER) negativity by immunohistochemistry (IHC) in breast cancer (BC) is debated. We explored the tumor immune microenvironment and gene-expression profile of patients with early-stage HER2-negative ER-low (ER 1-9%) BC, comparing them to ER-negative (ER < 1%) and ER-intermediate (ER 10-50%) tumors. Among 921 patients with early-stage I-III, ER ≤ 50%, HER2-negative BCs, tumors were classified as ER-negative (n = 712), ER-low (n = 128), or ER-intermediate (n = 81). Tumor-infiltrating lymphocytes (TILs) were evaluated. CD8+, FOXP3+ cells, and PD-L1 status were assessed by IHC and quantified by digital pathology. We analyzed 776 BC-related genes in 116 samples. All tests were 2-sided at < 0.05 significance level. ER-low and ER-negative tumors exhibited similar median TILs, significantly higher than ER-intermediate tumors. CD8/FOXP3 ratio and PD-L1 positivity rates were comparable between ER-low and ER-negative groups. These groups showed similar enrichment in Basal-like intrinsic subtypes and comparable expression of immune-related genes. ER-low and ER-intermediate tumors showed significant transcriptomic differences. High TILs (≥30%) were associated with improved relapse-free survival (RFS) in ER-low (5-year RFS 78.6% vs 66.2%, log-rank p = .033, hazard ratio (HR) 0.37 [95% CI 0.15-0.96]) and ER-negative patients (5-year RFS 85.2% vs 69.8%, log-rank p < .001, HR 0.41 [95% CI 0.27-0.60]). ER-low and ER-negative tumors are similar biological and molecular entities, supporting their comparable clinical outcomes and treatment responses, including to immunotherapy. Our findings contribute to the growing evidence calling for a reevaluation of ER-positive BC classification and management, aligning ER-low and ER-negative tumors more closely.

Enhancing Breast Cancer Detection: Leveraging Convolutional Neural Networks

Abstract: Breast cancer continues to be a critical health concern, necessitating early detection and accurate classification for effective treatment. This study presents a comparative analysis between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We compiled a comprehensive dataset of breast cancer images and applied appropriate preprocessing techniques to optimize the input for the models. The dataset was divided into training, validation, and testing sets to evaluate the models' performance. The CNN model comprises multiple convolutional and pooling layers followed by fully connected layers for classification, while the DenseNet121 model is fine-tuned specifically for breast cancer detection. The models were evaluated based on metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. The DenseNet121 model outperformed the custom CNN model, achieving higher accuracy and reliability. For wider accessibility, we integrated the superior DenseNet121 model into a user-friendly web-based interface using Python Flask, enabling real-time breast cancer predictions. Ethical considerations were paramount, ensuring data privacy, security, and transparency in all model predictions. This study highlights the effectiveness of the DenseNet121 model and contributes to improved breast cancer diagnosis and patient care.

The treatment pattern of advanced HR-positive and HER2-negative breast cancer in central southern China: a hospital-based cross-sectional study

AimsThis investigation aims to elucidate the treatment status of advanced HR+/HER2- breast cancer patients in Hunan Province of Central Southern China from November 2021 to December 2022.MethodsData from 301 patients with advanced HR+/HER2- breast cancer were collected from the breast cancer investigation project in Hunan under the guidance of the Chinese Society of Clinical Oncolfogy (CSCO). The data included the clinical characteristics of patients and the status of first-line and second-line rescue treatment.ResultsFirst-line chemotherapy and endocrine therapy for mBC accounted for 40% (121/301) and 60% (180/301) of treatments, respectively. AI (21%), AI plus CDK4/6 inhibitor (28%), and fulvestrant (24%) or fulvestrant plus CDK4/6 inhibitor (18%) were the most common first-line endocrine therapies. Taxane-based chemotherapy was the most common first-line chemotherapy (59%). Second-line chemotherapy and endocrine therapy for mBC accounted for 43% (72/166) and 57% (94/166) of treatments, respectively. Fulvestrant (23%) or fulvestrant plus CDK4/6 inhibitor (29%) were the most common second-line endocrine therapies. The prevalences of AI and AI plus CDK4/6 inhibitor decreased to 19% and 11%, respectively. T (taxane)-based chemotherapy was still the most common chemotherapy regimen (46%). Third-line chemotherapy was more prevalent than endocrine therapy (57% vs. 41%). T (taxane)-based chemotherapy was still the most common chemotherapy regimen (46%). Fulvestrant plus CDK4/6 inhibitor was the most common endocrine therapy (33%). AI, AI plus CDK4/6 inhibitor, and fulvestrant accounted for 21%, 12% and 18% of third-line endocrine therapies, respectively.ConclusionsCompared to chemotherapy, endocrine therapy was a more favorable choice for first-line and second-line treatment for HR+/HER2- advanced breast cancer patients in Hunan Province.

The NEOLETRIB trial: neoadjuvant treatment with Letrozole and Ribociclib in ER-positive, HER2-negative breast cancer.

Chemotherapy is used as neoadjuvant therapy for all subgroups of breast cancer, including ER-positive, and HER2-negative cases. However, studies have suggested that using aromatase inhibitors combined with CDK4/6-inhibitors might be an appropriate alternative in selected patients. Thus, the NEOLETRIB trial evaluates the response of ER-positive, HER2-negative luminal A/B breast cancer to the combination of letrozole and ribociclib in the neoadjuvant setting. Comprehensive molecular biology procedures, including sequential single-cell RNA-sequencing of tumor biopsies, are performed during 6 months of treatment with extensive biobanking of blood samples, tumor biopsies and gut microbiome specimens. Our findings will hopefully contribute to an improved selection of patients who may benefit from this drug combination and give new insights into the intra-tumoral changes during this treatment.Trial registration number: NCT05163106 (ClinicalTrials.gov).

Breast Cancer Classification based on Ultrasound Images using the Support Vector Machine (SVM) Algorithm

Breast Cancer Classification based on Ultrasound Images using the Support Vector Machine (SVM) Algorithm

The impact of human epidermal growth factor receptor-2 (low) status on the efficacy of first line cyclin-dependent kinase 4/6 inhibitors in advanced breast cancer.

The fact that the human epidermal growth factor receptor 2 (HER2)-low group, historically classified as HER2 negative in breast cancer histology, benefited from HER2-targeted treatments similarly to the HER2-positive group indicates that this group has a distinct histology from the HER2-0 group. The effectiveness of cyclin-dependent kinase 4/6 inhibitors, which are the standard first-line treatment for hormone receptor-positive, HER2-negative advanced breast cancer, in this newly defined histological subgroup remains a topic of debate. In our study, we examined the impact of HER2 status on the efficacy of CDK4/6 inhibitors. Our study is a retrospective, multicenter, real-world data analysis. One hundred sixty patients were included in the study. The relationship between HER2 status and other clinical-pathological features, as well as progression-free survival, was examined. Median follow-up was 20.33 ± 0.98 months. The mPFS could not be reached. All patients exhibited positive estrogen receptor expression. Among the patients, 111 (69.4%) were categorized as HER2-0, and 49 (30.6%) as HER2-low. The 24-month progression-free survival rates were similar between HER2-0 and HER2-low patients (60.6% vs 65.3%, hormone receptor: 1.18, CI: 0.67-2.20, P = .554). We established that the mPFS achieved with cyclin-dependent kinase 4/6 inhibitors as a first-line therapy for patients with advanced breast cancer is unaffected by HER2 status.

Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis.

Omission of Completion Axillary Lymph Node Dissection for Patients with Breast Cancer Treated by Upfront Mastectomy and Sentinel Node Isolated Tumor Cells or Micrometastases.

Omission of completion axillary lymph node dissection (cALND) in patients undergoing mastectomy with sentinel node (SN) isolated tumor cells (ITC) or micrometastases is debated due to potential under-treatment, with non-sentinel node (NSN) involvement detected in 7% to 18% of patients. This study evaluated the survival impact of cALND omission in a cohort of breast cancer (BC) patients treated by mastectomy with SN ITC or micrometastases. Among 554 early BC patients (391 pN1mi, 163 ITC), the NSN involvement rate was 13.2% (49/371). With a median follow-up of 66.46 months, multivariate analysis revealed significant associations between cALND omission and overall survival (OS, HR: 2.583, p = 0.043), disease-free survival (DFS, HR: 2.538, p = 0.008), and metastasis-free survival (MFS, HR: 2.756, p = 0.014). For Her2-positive or triple-negative patients, DFS was significantly affected by cALND omission (HR: 38.451, p = 0.030). In ER-positive Her2-negative BC, DFS, OS, recurrence-free survival (RFS), and MFS were significantly associated with cALND omission (DFS HR: 2.358, p = 0.043; OS HR: 3.317; RFS HR: 2.538; MFS HR: 2.756). For 161 patients aged ≤50 years with ER-positive/Her2-negative cancer, OS and breast cancer-specific survival (BCSS) were notably impacted by cALND omission (OS HR: 103.47, p = 0.004; BCSS HR: 50.874, p = 0.035). These findings suggest a potential negative prognostic impact of cALND omission in patients with SN micrometastases or ITC. Further randomized trials are needed.

Comparative cost-effectiveness analysis of CDK4/6 inhibitors in the first-line treatment of HR-positive and HER2-negative advanced breast cancer: a Markov's model-based evaluation.

CDK4/6 inhibitors are the first-line treatment for HR+/HER2- advanced breast cancer. Despite their clinical benefit, they can increase healthcare expenditure. To date, there is no thorough comparison among the three approved CDK4/6 inhibitors in terms of their cost-effectiveness. To investigate and compare the cost-effectiveness of CDK4/6 inhibitors in combination with letrozole as a first-line treatment for advanced breast cancer with hormonal-receptor-positivity and HER-2-negativity versus one another and versus letrozole monotherapy. A 10-year within-cycle-corrected Markov's model was employed from the healthcare payer perspective. Costs were obtained from the National Center for Cancer Care and Research (NCCCR) in Qatar. Utilities and transition probabilities were calculated from published landmark trials of PALOMA-2, MONALEESA-2, MONARCH-3, PO25, and other relevant literature. Costs, measured in Qatari Riyal (QAR), and effectiveness, measured in quality-adjusted-life-years (QALYs), were incremented and the incremental cost-effectiveness ratio (ICER) was compared to a willingness-to-pay threshold (WTP) of 1.5 Qatari GDP (448,758 QAR). A deterministic sensitivity analysis was implemented to account for uncertainties. Ribociclib was the most effective option, generating 4.420 QALYs, followed by palbociclib (4.406 QALYs), abemaciclib (4.220 QALYs), then letrozole monotherapy (2.093 QALYs). As for cost-effectiveness, ribociclib dominated palbociclib. However, it was not cost-effective compared to abemaciclib (ICER=1,588,545 QAR/QALY). Ribociclib remained dominant over palbociclib with all uncertainties. The base-case conclusion of ribociclib versus abemaciclib remained robust over all uncertainties. From the healthcare payer perspective in Qatar, ribociclib is the most effective CDK4/6 inhibitor. It was dominant over palbociclib in terms of cost-effectiveness; however, it was not cost-effective compared to abemaciclib at current prices.

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities.