Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. OBJECTIVE: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. METHODS: The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. RESULTS: The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. CONCLUSION: The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

Assessment of Hounsfield Units and Factors Associated with Fragmentation of Renal Stones by Extracorporeal Shock Wave Lithotripsy: A Computerized Tomography Study.

The success rate of extracorporeal shock wave lithotripsy (ESWL) is influenced by various factors, including stone density, and is determined through computed tomography scans in terms of Hounsfield units (HU). MATERIALS AND METHODS: This retrospective single-center study was conducted in the King Fahad Hospital. Sixty-seven adult patients with renal and ureteric stones were selected randomly and enrolled in the study. Their ages ranged from 20 to 69 years. The patients were examined with non-contrast enhancement (NCCT) to assess the HU of their stones and were consequently treated with ESWL. RESULTS: Of the 67 patients, 37.3% had stones that were completely fragmented, while 62.7% had stones that were partially fragmented. The HU, location of the stone, multiplicity of the stone, and patient age were found to be significant factors contributing to stone fragility (p-values < 0.05). The HU data were found to have a positive significant linear correlation with serum calcium (r = 0.28, p-value = 0.036), while serum acid had a negative correlation (r = -0.55, p-value < 0.001). Thus, the probability of calcium-containing stone formation increases with increased HU. In contrast, uric acid stone formation likely develops with decreasing HU with serum uric acid. Renal stones in patients with diabetes mellitus and hypertension were not completely fragmented compared to those without clinical history. CONCLUSIONS: Mean HU, location of the stone, laterality, stone status, and the number of ESWL sessions are the most significant factors affecting stone fragility. CT attenuation values can predict the composition of stones from serum calcium and uric acid examinations. Hypertension and diabetes mellitus are risk factors for renal stone fragmentation.

Examining distress among Saudi paramedics in responding to traumatic events: A mixed-methods study.

INTRODUCTION: Paramedics are critical in providing timely medical care to injured patients and responding to trauma events. However, they are often exposed to traumatic events such as death, serious injuries, and violence, which can increase their risk of developing mental health disorders. The nature of their work, including uncontrolled environments and frequent exposure to trauma, as well as the severity of the patient's conditions, all contribute to this risk. This study aims to investigate the distress experienced by Saudi paramedics in response to traumatic events. METHODS: An explanatory sequential mixed-methods approach was used to explore the experiences of distress among a sample of paramedics following traumatic events. RESULTS: The study found that the participants ranked assaults and road traffic collisions as the most concerning traumatic events. Qualitative analysis of the data revealed several themes related to the participants' experiences, including emotional well-being, violence, road traffic collisions, death, personal limitations, culture, coping strategies, and professional support. CONCLUSIONS: The study highlights the high levels of distress experienced by paramedics in response to road traffic collisions, with intrusion and avoidance symptoms being the most commonly reported.

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks.

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Fennel affects porcine ovarian cell functions: The interrelationships with the environmental contaminant benzene.

The objective of this study was to examine the direct effects of the medicinal plant fennel on basic functions of ovarian cells, including proliferation, apoptosis, and release of progesterone and insulin-like growth factor I (IGFI), as well as to prevent the influence of the environmental contaminant benzene on these cells. Porcine ovarian granulosa cells were cultured with or without fennel extract alone or in combination with benzene. The expression of the proliferation marker PCNA and the apoptosis marker bax was analyzed by quantitative immunocytochemistry and enzyme-linked immunosorbent assay (ELISA). Fennel was able to promote proliferation and IGF-I release, but to suppress apoptosis and progesterone release. Benzene promoted the accumulation of both the proliferation and apoptosis markers, as well as IGF-I release, but it inhibited progesterone secretion. The presence of fennel did not prevent the effects of benzene on any of the measured parameters, while benzene prevented the effects of fennel on cell proliferation, apoptosis, and IGF-I but not progesterone output. These observations demonstrate the direct influence of fennel and benzene on basic ovarian cell functions. Furthermore, they show the inability of fennel to prevent the effects of benzene on these cells. On the other hand, the environmental contaminant benzene can block the response of ovarian cells to the medicinal plant fennel.

Impact of Medication Adherence on Bone Mineral Density and Fracture Risk in Patients With Osteoporosis: A Systematic Review.

Osteoporosis is a chronic, prevalent disease marked by decreased bone mass and changes in bone anatomy associated with significant morbidity. The management of osteoporosis necessitates long-term therapy for which patient adherence is of vital importance. In the present review, we aim to collect all potential evidence from relevant studies that reported the impact of medication adherence on bone mineral density and fracture risk in patients with osteoporosis. We have conducted both electronic and manual search strategies within the potential databases and included articles and reviews to find relevant studies. We have assessed the effects of osteoporotic medication adherence on fracture rates and bone mineral density. The study participants were divided into two groups, adherent and non-adherent. Studies from the year 2010-2023 were included. Final inclusion consisted of 14 studies that showed variation in adherence rates with only three studies reporting optimal adherence followed by two studies with nearly half adherent population while the rest of the studies reported low medication adherence. The highest adherence rate reported was 82% while the lowest was 8%. Among the included studies the fracture rates varied significantly. Decreased rates of fracture were observed in the adherent population however two of the included studies were contrary to these findings. Additionally, only three studies discussed the effect of adherence on bone mineral density. Lack of medication adherence is linked to an increased risk of fracture, and low bone mineral density, further associated with more severe complications as per the evidence from the literature. However, variation in the fracture rates as observed in our findings advocates the need for further research for the generalizability of results.

Establishing local diagnostic reference levels for computed tomography examinations using size-specific dose estimates.

OBJECTIVES: To establish local DRL (LDRL) for computed tomography (CT) examinations based on size-specific dose estimates (SSDEs), which consider patient size. The concept of diagnostic reference level (DRL) was introduced to limit patient exposure to unnecessary radiation. However, traditional DRL values do not consider patient size. METHODS: Following institutional committee approval, data were collected from CT examinations of adult patients at Madinah General Hospital, Al Madinah Al Munawwarah, Saudi Arabia from January to March 2023. The SSDE was calculated for each patient using the effective diameter (Deff). RESULTS: The LDRLs of the brain, cervical spine, chest, thoracic spine and kidneys, ureters, and bladder (KUB) examinations were 118 mGy, 12 mGy, 8 mGy, 17 mGy, and 7 mGy, respectively. A strong correlation was observed between SSDEs and the volume computed tomography dose index (CTDIvol) for all examinations except chest scans (p<0.05). Size-specific dose estimates were higher than the CTDIvol, with a greater difference for patients with smaller Deff (p<0.05). CONCLUSION: The established LDRL was within the international DRL. The use of SSDE has the potential to provide more accurate and relevant data for radiation safety practices; however, widespread adoption of SSDE in new CT scanners is necessary for promoting consistency and standardization methodologies.

Impact of COVID-19 Pandemic on Respiratory Syncytial Virus (RSV) Prophylaxis Program: A Tertiary-Care Center Experience.

OBJECTIVES: The purpose of this investigation was to evaluate the effects of the COVID-19 pandemic on the respiratory syncytial virus (RSV) prevention program at our institution across three time frames: 2019-2020, 2020-2021, and 2021-2022. METHODS: We carried out a descriptive, single-site observational study spanning four years, from June 2019 to June 2022. Our study included patients in our institution's RSV program who met our enrollment criteria. We collected information about the number of children receiving immunoprophylaxis, immunoprophylaxis doses, and RSV risk factors. RESULTS: The number of patients receiving immunoprophylaxis dropped across the three periods, from 315 patients in the first period (2019-2020) to 176 in the second period (2020-2021), and further decreased to 128 in the third period (2021-2022). Following the COVID-19 pandemic, there was a 50% reduction in the number of patients receiving immunoprophylaxis. The proportion of RSV-infected patients remained relatively similar in the first and second periods (2.86% and 2.27%, respectively) but increased in the third period (5.47%). In the first period, most patients (60.32%) received seven doses, 11.75% got four to six doses, and 27.95% received three doses or fewer. The second period saw 59.66% of patients receiving four to six doses and 40.34% receiving three doses or fewer. In the third period, a mere 9.38% received four to five doses, while 90.63% got three doses or fewer. CONCLUSIONS: While preventative measures associated with COVID-19 may have helped reduce the number of RSV cases, the pandemic seems to have caused a significant decrease in the number of children receiving immunoprophylaxis and the doses of immunoprophylaxis. More extensive, multicenter research is needed to understand the impact of the COVID-19 pandemic on RSV immunoprophylaxis, its activity, and seasonal patterns fully.

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

Successful surgical removal of a cutaneous lesion compatible with gigantic vascular eccrine spiradenoma: a rare case report.

Giant vascular eccrine spiradenoma (GVES) is an uncommon type of eccrine spiradenoma (ES). Compared to an ES, this is characterized by a greater degree of vascularity and a bigger size overall. In clinical practice, it is frequently mistaken for a vascular or malignant tumor. To achieve an accurate diagnosis of GVES via biopsy and successfully perform the surgical removal of a cutaneous lesion in the left upper abdomen compatible with GVES. We present a 61-year-old female with the lesion accompanied by on-and-off pain, bloody discharge and skin changes surrounding the mass treated surgically. However, there was no fever, weight loss, trauma or family history of malignancy or cancer managed by surgical excision. The patient recovered well postoperatively and was discharged on the same day with a 2-week follow-up. The wound got healed, the clips were removed on Day 7 postoperatively, and there was no requirement for further follow-up.

Mechanical and Biocompatibility Properties of 3D-Printed Dental Resin Reinforced with Glass Silica and Zirconia Nanoparticles: In Vitro Study.

This study aimed to assess the mechanical and biocompatibility properties of dental resin reinforced with different nanoparticle additives. Temporary crown specimens were 3D-printed and grouped based on nanoparticle type and amount, including zirconia and glass silica. Flexural strength testing evaluated the material's ability to withstand mechanical stress using a three-point bending test. Biocompatibility was tested using MTT and dead/live cell assays to assess effects on cell viability and tissue integration. Fractured specimens were analysed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) for fracture surface examination and elemental composition determination. Results show that adding 5% glass fillers and 10-20% zirconia nanoparticles significantly improves the flexural strength and biocompatibility of the resin material. Specifically, the addition of 10%, 20% zirconia, and 5% glass silica by weight significantly increases the flexural strength of the 3D-printed resins. Biocompatibility testing reveals cell viabilities greater than 80% in all tested groups. Reinforced 3D-printed resin holds clinical potential for restorative dentistry, as zirconia and glass fillers have been shown to enhance mechanical and biocompatibility properties of dental resin, making it a promising option for dental restorations. The findings of this study may contribute to the development of more effective and durable dental materials.

Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

The Clinical Potential of 3D-Printed Crowns Reinforced with Zirconia and Glass Silica Microfillers.

The development of 3D-printed crown resin materials with improved mechanical and physical properties is an area of growing interest in dentistry. This study aimed to develop a 3D-printed crown resin material modified with zirconia glass (ZG) and glass silica (GS) microfillers to enhance overall mechanical and physical properties. A total of 125 specimens were created and divided into five groups: control unmodified resin, 5% either ZG or GS reinforced 3D-printed resin, and 10% either ZG or GS reinforced 3D-printed resin. The fracture resistance, surface roughness, and translucency parameter were measured, and fractured crowns were studied under a scanning electron microscope. The results showed that 3D-printed parts that were strengthened with ZG and GS microfillers demonstrated comparable mechanical performance to unmodified crown resin but resulted in greater surface roughness, and only the group that contained 5% ZG showed an increase in translucency. However, it should be noted that increased surface roughness may impact the aesthetics of the crowns, and further optimisation of microfillers concentrations may be necessary. These findings suggest that the newly developed dental-based resins that incorporate microfillers could be suitable for clinical applications, but further studies are necessary to optimise the nanoparticle concentrations and investigate their long-term clinical outcomes.

Isolated Tracheabronchomalacia Misdiagnosed for Years as Bronchial Asthma.

Tracheomalacia (TM) is an abnormal collapse of the tracheal lumen, which often occurs when the cartilaginous part of the trachea has not developed. It is a rare condition but is seen often in infancy and childhood period. The incidence of primary airway malacia in children was estimated to be at least one in 2,100. It has a wide range of etiologies, and it is often localized but rarely generalized as in our case. It could be severe enough to indicate frequent admission and might expose the patient to multiple unnecessary medications. We are reporting a case with unusual primary tracheobronchomalacia (TBM) that was missed for several years with a huge burden on both families and healthcare providers. A five-year-old Saudi girl had multiple admissions to the intensive care unit with similar presentation each time, and she was misdiagnosed as having asthma exacerbation with an occasional chest infection. Bronchoscopy revealed the underlying condition, and the patient was kept on the minimal intervention of nasal continuous positive airway pressure (CPAP) and aggressive airway hydration therapy, all with the goal of improving the patient's outcome and reducing hospital admissions. We emphasize the importance of alerting physicians about malacia as an important cause of recurrent wheezy chest, which is one of the common asthma mimickers; in such cases, flexible bronchoscopy remains the gold standard diagnostic test, while the treatment remained supportive.

Ephedra alata Subsp. Alenda as a Novel Source of Bioactive Phytochemicals: Characterization Based on the Mass Spectrometry and Profiling of Antioxidant and Anti-Inflammatory Properties.

The aim of the present study was to examine, for the first time, the phytochemical content of Ephedra alata pulp extract (EAP) and explore its antioxidant and anti-inflammatory capacities. High-performance liquid chromatography-electrospray ionization-quadrupole-time-of-flight mass spectrometry (HPLC-ESI-QTOF/MS) was used for phytochemical analysis and three in vitro antioxidant assays together with three in vitro anti-inflammatory tests were used for the assessment of biological activity. The HPLC-ESI-QTOF/MS analysis revealed the presence of 42 metabolites, including flavonoids, sphingolipides, fatty acids, ephedrine derivatives, and amino acid derivatives. In vitro findings revealed that EAP has interesting 2,2-diphenyl-1-picrylhydrazyl (DPPH), superoxide, and ferrous ion chelating capacities (IC50 values were 0.57 mg/mL, 0.55 mg/mL, and 0.51 mg/mL for DPPH, superoxide radical, and ferrous ion, respectively). Furthermore, EAP showed a noticeable anti-inflammatory ability by inhibiting the two cyclooxygenase isoforms, COX-1 and COX-2 (IC50 of 59.1 and 58.8 µg/mL for COX-1 and COX-2, respectively), preventing protein denaturation (IC50 = 0.51 mg/mL), and protecting membrane stabilization (IC50 = 0.53 mg/mL). The results highlighted the use of Ephedra alata pulp as a potential source of natural compounds with therapeutic effects for the management of inflammatory disorders.

Evaluating Sonographers' Awareness of Artifacts in Renal Ultrasound Scans.

BACKGROUND: A routine ultrasound scan is used to assess a variety of renal pathological cases. Sonographers face a variety of challenges that may affect their interpretation. Understanding normal organ shapes, human anatomy, physical concepts, and artifacts is required for accurate diagnosis. Sonographers must understand how artifacts appear in ultrasound images in order to reduce errors and improve diagnosis. The purpose of this study is to assess sonographers' awareness and knowledge of artifacts in renal ultrasound scans. METHODS: Participants in this cross-sectional study were asked to complete a survey containing different types of common artifacts in renal system ultrasound scans. An online questionnaire survey was used to collect the data. The questionnaire targeted radiologists, radiologic technologists, and intern students in Madinah hospitals in the ultrasound department. RESULTS: The total number of participants was 99, with 9.1% being radiologists, 31.3% being radiology technologists, 6.1% being senior specialists, and 53.5% being intern students. There was a significant difference in the participants' knowledge of ultrasound artifacts in the renal system with the total selection of the right artifact in senior specialists at 73% and intern students 45%. There was a direct relationship between age and years of experience in distinguishing artifacts in the renal system scan. A category of participants with the highest age and experience got 92% of the correct selection of artifacts. CONCLUSION: The study concluded that intern students and radiology technologists have limited knowledge of ultrasound scan artifacts, whereas senior specialists and radiologists have a high level of awareness of the artifacts.

Malignant Pleural Effusion in Pediatrics: A Rare Presentation.

Pleural effusion is the most common presentation of pleural diseases. It is relatively common in children with two predominant types: exudative and transudative effusions. In children, exudative types are the most common with bacterial infection being the most prevalent cause. In some cases, effusion could be difficult to confirm. We describe two patients with a similar age group who presented with respiratory distress in the form of fever, cough, and shortness of breath. They were managed clinically and radiologically as cases of parapneumonic effusion. Both were started on antibiotics with no improvement. After reviewing the cases, it was discovered that some crucial aspects of the history and physical examination that were essential to reach the correct diagnosis had not been taken into consideration. Parapneumonic effusion should be taken with caution, meticulous history and examination are warranted, and lymphocytic-predominant effusion is very alarming for potential malignancy in the absence of tuberculosis infection. If the antibiotic medication yields no significant improvement, earlier referral should be considered.

Factors Predicting Positive Sentinel Lymph Node Biopsy in Clinically Node-Negative Breast Cancer.

Purpose: Sentinel lymph node (SLN) biopsy (SLNB) is the standard tool to stage the axilla of breast cancer patients. This study aimed to identify the predictors of positive SLNB in patients with clinically node-negative breast cancer. Patients and Methods: A retrospective, single-institution cohort of patients with early-stage breast cancer without clinically identifiable axillary lymphadenopathy was chosen from January 2010 to December 2018. Logistic regression was used to identify possible predictors of positive SLNB. Results: Four hundred and seventy patients were identified; their mean age was 50±11 years. Most patients had the following characteristics: invasive ductal carcinoma (n=382, 81.3%), unilateral tumor (n=461, 98.1%), unifocal disease (n=351, 74.7%), intermediate grade (n=276, 59.0%), and estrogen and progesterone receptor positivity with human epidermal growth factor receptor 2 negativity (n=305, 64.9%). The mean size of the breast mass was 2.3±1.5 cm. SLNB was positive in 128 (27.2%) cases. The mean number of SLNs was 2±1.2. Axillary lymph node dissection was performed in 109 patients. The mean number of lymph nodes removed was 15±6. In 66 (60.6%) of the 109 patients with metastatic axillary nodes, only the SLNs were found to be positive. The number of SLNs, tumor size, tumor grade, receptor status, prominent axillary lymph nodes, and lymphovascular invasion predicted positive SLNB (P = 0.01, 0.03, 0.03, and 0.04 and <0.001 and <0.001, respectively). Conclusion: Our results suggest that a number of histopathological and radiological characteristics of breast cancer can predict SLNB positivity in clinically node-negative breast cancer patients.