Nearby Nodes Research Articles

Bilateral axillary lymph node metastasis after radioactive ablation in recurrent papillary thyroid carcinoma: A case report

Papillary thyroid carcinoma (PTC) is associated with indolent growth and a good prognosis. PTC has a tendency to metastasize into nearby lymph nodes, and 10% of PTC cases may present with metastatic disease. Axillary lymph node metastasis (ALNM) is an ineffably scarce event in thyroid carcinoma, and it may indicate a poor prognosis, with only a few cases reported in the literature. A 44-year-old woman with a previous history of total thyroidectomy, left radical neck dissection, and radioactive iodine (RAI) ablation was clinically presented with a recurrent thyroid mass, cervical lymphadenopathy, extensive skin involvement, and bilateral axillary lymph nodes from metastatic papillary thyroid carcinoma. Imaging scans revealed metastasis to the lungs and cervical bone. She was managed by completion total thyroidectomy, wide excision for the suprasternal nodules, completion left neck dissection, type 3 modified radical right neck dissection, bilateral central compartment clearance, and standard bilateral axillary dissection. ALNM is associated with aggressive histopathology, extensive locoregional disease, and distant metastasis. ALNM in thyroid cancer is uncommon since there is no direct connection between the neck and axilla; thus, ALNM is a culmination of an uncommon pattern. Risk factors contributing to ALNM include skin involvement, bulky cervical lymph nodes, previous surgery, and radiation. Complete surgical resection is the main therapy to achieve the effectiveness of adjuvant therapy.

Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network

Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic conditions of nearby nodes. In order to solve this problem, this paper proposes a model based on Dynamic Diffusion Spatial-Temporal Graph Convolutional Network. It first combines the dynamic generation matrix and the static distance matrix to grasp real-time traffic conditions, and then introduces the diffusion random walk strategy to capture the correlation of spatial nodes. Finally, the convolutional LSTM module is used to mine the spatiotemporal dependence of traffic data to improve the accuracy of traffic prediction. Compared to several baseline models, the experimental results show that the model is 7% better than other models on several metrics and demonstrates the necessity of the module through ablation experiments.

Role of neck dissections in the management of carotid body tumors.

Carotid body tumors (CBTs) are rare neoplasms of the paraganglia at the carotid bifurcation. While typically benign, CBTs occasionally exhibit malignancy, metastasizing to nearby lymph nodes. Histopathologic analysis alone is insufficient to confirm malignancy, requiring metastases to non-neuroendocrine tissue for a definitive diagnosis. The role of selective neck dissections (SNDs) in detecting malignancy and guiding subsequent management remains uncertain. A retrospective case series through electronic chart review was performed on 21 patients undergoing CBT surgeries between 2002 and 2022 at a Canadian institution. SNDs were performed on all 21 patients. Data collection included patient demographics, genetic and laboratory testing results, preoperative imaging, intraoperative and postoperative complications, histologic analysis of neck SND and tumor specimen, and follow-up results. Of the 21 surgical resections, there were three cases (14.3%) of carotid artery injuries and six cases (28.6%) of nerve injuries. One patient (4.8%) experienced three intraoperative strokes. Three patients (14.3%) were found to have lymph node involvement, confirming malignancy, and underwent further treatment with radiotherapy. Interestingly, two patients with carotid injuries had malignant tumors, demonstrating a statistical significance between carotid injury and malignancy (OR 34.00, 95% CI: 1.48, 781.83, p = .041). SNDs are a useful adjunct in detecting malignancy during CBT surgeries. The incidence of malignancy in CBT is low but not negligible, and SND should be considered in patients to prevent inadvertent underdetection of metastatic disease. This study's 14.3% incidence of malignancy suggests that there may be a rationale for considering the universal implementation of SND during CBT resections. 4.

Spatial Correlations Based Fault Tolerant Source Localization Using Wireless Sensor Networks

The source localization is a process to determine the location of a signal source based on measurements from sensor nodes or receivers that are spatially distributed in the area around the source. Localizing intruders and mobile users are some of the utilizations of wireless sensor networks (WSNs) based source localization problem. However, sensor faults and channel noise are the important issues that degrade the source localization performances of the systems. In this paper, we significantly study and improve our recent results; the representative fault tolerant hitting set and feature selection approaches for localizing a source in a decentralized WSN using the faulty sensor nodes and channel noise. First, we propose a technique to place some active sensor nodes in the sleep state such that the source localization performances of the hitting set and feature selection approaches do not significantly degrade. The proposed technique takes advantage of spatial correlations among sensor nodes, where nearby sensor nodes provide same decisions to the fusion center with a high probability. We observe that the performance gap for the results with and without sleeping sensor nodes decreases by increasing total sensor nodes. In addition, we improve our recent results by proposing a technique based on spatial correlations among sensor nodes to minimize the impact of channels’ noises, especially for a higher fault probability of sensor nodes. Finally, extensive simulations on synthetic and real-world data sets validate our theoretical results.

Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) face significant challenges, such as limited energy resources, high propagation delays, and harsh underwater environments. Efficient clustering can help address these challenges by grouping nearby nodes to minimize network fragmentation and balance energy consumption. However, placing gateways near the sink node can result in increased communication overhead and higher energy consumption in regions with concentrated data flow. To address these issues, we propose an energy-efficient artificial fish swarm-based clustering cognitive intelligence protocol (EAFSCCIP). EAFSCCIP leverages the collective behavior of artificial fish within a Bees algorithm framework, using a combination of heuristic and metaheuristic approaches for optimal cluster-head (CH) selection in each round. The protocol focuses on reducing energy consumption and extending network lifetime by considering real-time energy levels and the proximity of nodes for CH selection. Simulations have been executed in NS3 to validate and compare the performance of the proposed algorithm with the existing clustering protocols. The results indicate that EAFSCCIP significantly enhances the packet delivery ratio (PDR) by an average of 5.33% over existing methods and improves network lifetime by 6.54% compared to traditional protocols. It also reduces energy consumption by 25.6% and decreases packet loss by 50.5%, while achieving 20.4% higher throughput at the initial stage. These improvements make EAFSCCIP a promising solution for applications like acoustic monitoring in UWSNs, providing a balance between energy efficiency and reliable data transmission.

Adaptive reverse task offloading in edge computing for AI processes

Nowadays, we witness the proliferation of edge IoT devices, ranging from smart cameras to autonomous vehicles, with increasing computing capabilities, used to implement AI-based services in users’ proximity, right at the edge. As these services are often computationally demanding, the popular paradigm of offloading their tasks to nearby cloud servers has gained much traction and been studied extensively. In this work, we propose a new paradigm that departs from the above typical edge computing offloading idea. Namely, we argue that it is possible to leverage these end nodes to assist larger nodes (e.g., cloudlets) in executing AI tasks. Indeed, as more and more end nodes are deployed, they create an abundance of idle computing capacity, which, when aggregated and exploited in a systematic fashion, can be proved beneficial. We introduce the idea of reverse offloading and study a scenario where a powerful node splits an AI task into a group of subtasks and assigns them to a set of nearby edge IoT nodes. The goal of each node is to minimize the overall execution time, which is constrained by the slowest subtask, while adhering to predetermined energy consumption and AI performance constraints. This is a challenging MINLP (Mixed Integer Non-Linear Problem) optimization problem that we tackle with a novel approach through our newly introduced EAI-ARO (Edge AI-Adaptive Reverse Offloading) algorithm. Furthermore, a demonstration of the efficacy of our reverse offloading proposal using an edge computing testbed and a representative AI service is performed. The findings suggest that our method optimizes the system’s performance significantly when compared with a greedy and a baseline task offloading algorithm.

Convergence properties of the radial basis function-finite difference method on specific stencils with applications in solving partial differential equations

We consider the problem of approximating a linear differential operator on several specific stencils using the radial basis function method in the finite difference scheme. We prove a linear convergence order on a non-equispaced five-point stencil. Then, we discuss how the convergence rate can be boosted up to the second-order on an equispaced stencil. Moreover, we show that including additional nearby nodes (six to twelve) in the stencil does not improve the convergence rate, thus increasing the computational load without enhancing convergence. To overcome this limitation, we propose a stencil that accelerates the convergence up to four using a nine-point stencil, unlike existing approaches which are based on thirteen-point equispaced stencils to achieve such an order of convergence. To support our findings, we conduct numerical experiments by solving Poisson equations and a parabolic problem.

Application Value of Endoscopic Ultrasonography in Diagnosis and Treatment of Inflammatory Bowel Disease.

Inflammatory bowel disease refers to a group of non-specific inflammatory illnesses affecting the gastrointestinal tract. According to pathogenic characteristics, it is divided into Ulcerative colitis and Crohn's disease. The exact cause and pathogenic mechanism of these disorders are not yet fully understood. Inaddition, there is currently no definitive diagnostic method for inflammatory bowel disease, which mainly depends on clinical symptoms, blood testing, imaging investigations, and endoscopic examination, which includes histology. Endoscopic Ultrasonography is a digestive tract examination technique that combines endoscopy and ultrasound. Compared to conventional endoscopy, it can visualize surface and deep lesions of the gastrointestinal wall, as well as provide information on the characteristics of the surrounding layers and nearby lymph nodes. Due to these advantages, Endoscopic Ultrasonography has played a significant role in the evaluation of inflammatory bowel disease in recent years. Through this work, we aim to identify the applications of this method in the case of patients with inflammatory bowel disease.

Literature Review on Stages of Colorectal Cancer

Colorectal cancer, primarily originating from polyps in the colon or rectum, represents a significant health challenge due to its potential progression to malignancy. Polyps, abnormal tissue growths in the inner lining of the colon or rectum, often start as benign adenomas. Over time, these adenomas may undergo cellular mutations, transforming into cancerous tumors. Early detection and removal of polyps during screening significantly reduce the likelihood of cancer development. If left untreated, colorectal cancer can invade deeper layers of the colon wall, metastasize to nearby lymph nodes, and spread to distant organs, commonly the liver, peritoneum, or lungs. Colon cancer typically originates in the mucosa, the innermost lining of the colon, and progresses through the tissue and muscle layers over a span of approximately 10 years. The anatomy of the colon and rectum, consisting of the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum, plays a critical role in understanding the disease's progression. Advances in screening methods and treatments have significantly improved outcomes, emphasizing the importance of early intervention in reducing colorectal cancer mortality.

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering–attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64–0.71 and root mean square error in the range of 2.06–2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

De-Differentiated Metastatic Melanoma: A Diagnostic Challenge

Abstract Introduction/Objective Melanoma is one of the common malignant neoplasms with several histological appearances. Its diagnosis is usually based on histomorphologic findings and immunohistochemistry (IHC). De-differentiated melanoma (DM) is a special form of malignant melanoma with different histo-morphologic and immunohistochemical characteristics than conventional malignant melanomas. It has a worse prognosis and can impose a diagnostic challenge. Methods/Case Report A 62-year-old male previously had melanoma on his chest, which was surgically removed with negative margins and no involvement of nearby sentinel lymph nodes. The patient presented two months later with a rapidly enlarging mass in the right axillary region. Subsequent lymph node dissection revealed several nodes, the largest measuring 14 cm with extensive necrosis. Microscopic examination of the largest mass revealed clusters of irregularly shaped cells displaying marked nuclear abnormalities, prominent nucleoli, and heightened mitotic activity. Immunohistochemical analysis of this mass showed negative results for SOX-10, HMB45, Melan-A, S100 protein, CAM5.2, BRAF, and myogenin. Conversely, the remaining lymph nodes exhibited typical signs of melanoma metastasis, expressing relevant melanoma markers. Given the patient’s history of melanoma and the presence of metastatic melanoma in nearby lymph nodes, the histological and immunohistochemical findings support the diagnosis of dedifferentiated cutaneous metastatic melanoma. Results (if a Case Study enter NA) N/A Conclusion The distinctive appearance and lack of typical markers in this type of melanoma pose considerable difficulties in distinguishing it from other malignancies like poorly differentiated carcinomas and high-grade sarcomas. Furthermore, dedifferentiated melanoma tends to be highly aggressive, often resisting standard and immunological treatments. Therefore, comprehensive clinical and histomorphological assessments are crucial for accurately diagnosing these complex and challenging cases.

Identification of WSN Compromised Nodes and Performance Degradation Prevention using Cooperative Cache

Wireless Sensor Networks (WSNs) are critical in a variety of applications such as environmental monitoring, surveillance, and healthcare. WSNs, on the other hand, are vulnerable to assaults due to their dispersed and resource-constrained nature, resulting in compromised nodes and performance deterioration. Using network characteristics analysis and cooperative cache management with data and process affinity, this study provides a unique way to identifying compromised WSN nodes and mitigating performance deterioration. The initial portion of our suggested technique focuses on finding compromised nodes using in-depth network characteristics research. This suggested system can precisely locate probable hacked nodes by monitoring metrics such as traffic patterns, node activity, and communication abnormalities. Machine learning algorithms improve the identification process by learning and evolving adaptively to identify new assault patterns. Once affected nodes have been discovered, the second component of this suggested solution leverages cooperative cache management to prevent performance deterioration. Data and process affinity is presented, in which nearby nodes share caches to effectively store and retrieve data relevant to their processing activities. This affinity-based cache collaboration increases data availability, decreases communication costs, and improves overall network performance.

Reducing Transmission Signal Collisions on Optimized Link State Routing Protocol Using Dynamic Power Transmission

Many devices connected to a network inevitably result in clashes between communication signals. These collisions are an important factor that causes a decrease in network performance, especially affecting Quality of Service (QoS) like throughput, Packet Delivery Ratio (PDR), and end-to-end de- lay, which has a direct impact on the success of data transmission by potentially causing data loss or damage. The aim of this research is to integrate the Dynamic Power Transmission (DPT) algorithm into the Optimized Link State Routing (OLSR) routing protocol to regulate the communication sig- nal strength range. The DPT algorithm dynamically adapts the signal coverage distance based on the density of neighboring nodes to reduce signal collisions. In our protocol, the basic mechanism of a DPT algorithm includes four steps. The Hello message structure of OLSR has been modified to incorporate the ”x-y position” coordinate field data. Nodes calculate distances to neighbors using these coordinates, which is crucial for route discovery, where all nearby nodes can process route re-quests. The results of this research are that DPT-OLSR improves network efficiency in busy areas. In particular, the DPT-OLSR routing protocol achieves an average throughput enhancement of 0.93%, a 94.79% rise in PDR, and reduces end-to-end delay by 45.69% across various variations in node density. The implication of this research result is that the algorithm proposed automatically adapts the transmission power of individual nodes to control the number of neighboring nodes within a de-fined range. This effectively avoids unwanted interference, unnecessary overhearing, and excessive processing by other nodes, ultimately boosting the network’s overall throughput.

Hybrid quantum key distribution: A novel approach for secure end-to-end communication in network infrastructure

The Quantum Key Distribution (QKD) network approach has emerged as a promising solution and has garnered significant interest across various domains. QKD networks can be broadly categorized into two main types: those based on measurement-device-dependent (MDD) protocols and those based on measurement-device-independent (MDI) protocols. A novel network scheme is proposed combining these two protocols to guarantee end-to-end secure communication. It comprises two interconnected networks — the first uses a measurement-device-dependent protocol to connect routers with nearby nodes, while the second employs a measurement-device-independent protocol to interconnect the routers. In the first network, the process begins with Alice (Router) generating two types of weak quantum signals — the first has intensity, frequency and polarization or phase randomly varied, while the second with different delay times. Alice randomly transmits either signal states carrying information or decoy states with varying properties to the receivers (Bobs) over the quantum channels. She also sends a synchronization signal along with the quantum signals. At the receiver end, the synchronization pulse timestamps the incoming quantum signals. Each Bob then randomly shifts the frequencies of the received signals and measures their phase/polarization of the first signal and measures the delay times of the second signal. The secret key is derived by combining the frequency, the delay time and the phase/polarization information of the quantum signals, resulting in longer key lengths. Encoding information across multiple degrees of freedom enables extended key generation for secure communication. However, between the routers, MDI-QKD protocols are used to securely share the keys. This hybrid approach aims to ensure secure end-to-end communication across the network.

Trailblazer: A proactive caching, blockchain, minimum content retrieval latency, and energy cost-aware resource and job orchestration scheme for publisher-subscriber based 6G ICN services

With the rise of information-centric networks (ICN), the user can access the caching content from nearby caching nodes rather than the remote content server through in-network caching facilities. The existing articles did not look into minimum caching content retrieval latency, user energy consumption, user financial cost, and maximum service provider profit-aware proper resource orchestration and job scheduling policies all at once, taking into account multiple publishers and subscriber-based caching jobs, blockchain, user-owned and service provider cache, pre-caching, neighbor collaboration, and available resources. To suppress these challenges, this article offers a proactive pre-caching, collaboration, blockchain, minimum latency-aware resource, and job scheduling policy for 6G ICN services that takes into account publishers’ and subscribers’ caching job requests, user-owned and service provider cache, available resources, and deadline. The experimental results visualized that at least 16% content retrieval delay, 66% energy, and 22% service provider profit gain are attained in the proposed scheme over the existing techniques.

Correlation Between Female Junior High School Students Knowledge And BSE Behaviour

Introduction: Breast cancer often presents as a painless lump and can develop, invade surrounding breast tissue, and spread to nearby lymph nodes or other organs in the body. The high mortality rate of breast cancer is due to a lack of knowledge about the disease and the importance of early detection, leading to many patients seeking medical help at a late stage. According to Globocan data from 2020, teenage cancer patients in Indonesia rank 3rd in Asia. The government recommends Breast Self-Examination (BSE) as the most effective screening method. BSE is very easy to perform, yet many teenagers are unaware of this method. Objective: The review is to summarize the correlation between students knowledge and BSE Behaviour. Method: The study used a descriptive quantitative design with a cross-sectional approach and a simple random sampling technique. The sample size was 141 students, and the chi-square statistical test was used for analysis. Result: The majority, 91.5%, of Ananda Junior High School students had never performed BSE and had poor knowledge about the pathophysiology of breast cancer (93.6%) and breast cancer prevention (83.7%). A p-value of 0.000 (p < 0.05) was obtained, indicating a significant correlation between knowledge about the pathophysiology and prevention of breast cancer and BSE behavior. Conclussion: The proportion of Ananda Bekasi Junior High School students who engage in BSE behavior is very low (8.5%). Most respondents had poor knowledge about the pathophysiology of breast cancer (93.6%) and breast cancer prevention (83.7%).

Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

SummaryVehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis.

The objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P-SCD) and stable subjective cognitive decline (S-SCD), as well as to identify potential indicators that can effectively distinguish between P-SCD and S-SCD. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow-up period of over 3 years. This study included 39 individuals with S-SCD, 15 individuals with P-SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties. For global metric, the S-SCD group exhibited stronger small-worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S-SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P-SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S-SCD group. There are differences in brain functional networks at baseline between P-SCD and S-SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P-SCD and S-SCD.

Distinguishing Bronchial Carcinoid from Benign Bronchocele using 68Ga-DOTATOC PET/CT Imaging.

Bronchial carcinoids are low-grade neuroendocrine tumors with slow growth rates and the potential to spread to nearby lymph nodes. Here we present a challenging case of bronchial carcinoid visualized alongside an adjacent benign bronchocele. Chest computed tomography (CT) identified the endobronchial mass with unclear morphological and diagnostic insights. A differential diagnosis of several benign and malignant etiologies was made. Subsequently, an endobronchial biopsy confirmed lung carcinoid. For better evaluation, a 68Ga-labeled 1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid-d-Phe1-Tyr3-octreotide (68Ga-DOTATOC) positron emission tomography/CT scan was performed. The scan revealed a locally confined endobronchial mass with intense 68Ga-DOTATOC expression. Adjacent benign bronchocele was visualized with insignificant 68Ga-DOTATOC expression. Histopathological examination of the resected upper lobe confirmed these findings. This case highlights the importance of somatostatin receptor imaging in accurately identifying the extent of carcinoid tumors in the primary, nodal, and metastatic domains.

Evaluating the level of trace elements in Iraqi women with early-stage breast cancer

Breast cancer is one of a group of diseases in which cells in the breast tissue change and divide uncontrollably, causing these cells to clump. Invasive cancers can spread to nearby lymph nodes or other organs (metastasize). Elements are found in different forms in nature, and these elements are very necessary for the performance of the body›s various functions; Elements such as iron and zinc are essential components of enzymes where they attract or subtract molecules and facilitate their conversion to specific end products. Few elements donate or accept electrons during redox reactions. This study aimed to follow the change in the level of trace elements (TEs) and the extent to which these changes are related to the risk of breast cancer before taking chemotherapy or removing the tumor, by drawing 5 ml of venous blood from 120 women (60 patients and 60 control), the serum was separated then used to measure Zn, Cu, Fe, Mg, Cr, and whole blood (2ml) to measure Pb, Cd. In addition to studying the receiver operating characteristic curve (ROC) of these elements. Evaluation of trace elements revealed a significant increase in the levels of Fe, Cu, Pb, and Cd, in contrast to a significant decrease in the levels of Zn and Mg in breast cancer patients compared to a healthy control group.