Detection Of Breast Lesions Research Articles

Eco-logistical comparison of non-radioactive seeds and the wire-guided localization for intraoperative detection of breast lesions

BackgroundThe current standard for the preoperative marking of non-palpable breast lesions is wire guided localization (WGL) which is associated with logistical efforts and patient discomfort. Non-radioactive seeds (NRS) recently challenged the use of WGL; but do they provide a better alternative from a logistical and environmental perspective? MethodsWGL standard was compared with NRS available in Germany: Magseed®, Pintuition®, SAVI SCOUT ® and LOCalizer™ on a logistical and carbon-footprinting basis. In the logistical analysis the number of patient contacts with the healthcare system for lesion localization/removal and the number of breast punctures were evaluated in two different clinical scenarios (primary surgery and secondary surgery after neoadjuvant treatment). The carbon footprints of WGL and NRS (with exception of LOCalizer) were assessed based on their material compositions and operating energy in a streamlined approach. ResultsApplication of NRS reduces the number of contacts by 33.3 % (2 vs. 3) in primary, by 50 % (2 vs. 4) in secondary surgery, and the number of breast punctures by 33.3 % (2 vs.3). Annual Germany-wide material- and energy-based carbon footprints of NRS (1.6 to 3.2 tons CO2eq) are significantly lower in comparison to WGL (10.3 tons CO2eq). The implementation of NRS would lead to a CO2eq reduction by around 79 % compared to WGL. ConclusionsThe use of NRS for the localization of non palpable breast lesions is more favorable from the environmental and logistical perspective, when compared to WGL with possible benefits for patients, healthcare providers and the environment.

Does Contrast-Enhanced Mammography Outperform Digital Breast Tomosynthesis for Detection and Characterization of Breast Lesions or Vice Versa?

Does Contrast-Enhanced Mammography Outperform Digital Breast Tomosynthesis for Detection and Characterization of Breast Lesions or Vice Versa?

Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists.

Ultrasound is essential for detecting breast lesions. The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification system is widely used, but its subjectivity can lead to inconsistency in diagnostic outcomes. Artificial intelligence (AI) models, such as ChatGPT-3.5, may potentially enhance diagnostic accuracy and efficiency in medical settings. This study aimed to assess the utility of the ChatGPT-3.5 model in generating BI-RADS classifications for breast ultrasound reports and its ability to replicate the "chain of thought" (CoT) in clinical decision-making to improve model interpretability. Breast ultrasound reports were collected, and ChatGPT-3.5 was used to generate diagnoses and treatment plans. We evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing test and a consistency analysis. To enhance the interpretability of the model, we applied the CoT method to deconstruct the decision-making chain of the GPT model. A total of 131 patients were evaluated, with 57 doctors participating in the experiment. ChatGPT-3.5 showed promising performance in structure and organization (S&O), professional terminology and expression (PTE), treatment recommendations (TR), and clarity and comprehensibility (C&C). However, improvements are needed in BI-RADS classification, malignancy diagnosis (MD), likelihood of being written by a physician (LWBP), and ultrasound doctor artificial intelligence acceptance (UDAIA). Turing test results indicated that AI-generated reports convincingly resembled human-authored reports. Reproducibility experiments displayed consistent performance. Erroneous report analysis revealed issues related to incorrect diagnosis, inconsistencies, and overdiagnosis. The CoT investigation supports the potential of ChatGPT to replicate the clinical decision-making process and offers insights into AI interpretability. The ChatGPT-3.5 model holds potential as a valuable tool for assisting in the efficient determination of BI-RADS classifications and enhancing diagnostic performance.

Assessment Of Ultrasound Versus Mammography In Detecting Breast Lesions In Women Aged 40‐60: A Cross‐Sectional Study

Assessment Of Ultrasound Versus Mammography In Detecting Breast Lesions In Women Aged 40‐60: A Cross‐Sectional Study

Graph Neural Network Enhanced Dual-Branch Network for lesion segmentation in ultrasound images

Ultrasound images are widely used in the detection of breast lesions, thyroid nodules, renal tumors and other medical conditions due to their cost-effectiveness and convenience. Automatic segmentation of lesions in ultrasound images is crucial for clinical diagnosis. U-shaped networks with encoder–decoder architectures and skip connections have achieved significant success in medical image segmentation. However, challenges arise in segmentation due to the inherent blurriness of ultrasound image boundaries and the irregular shapes and volumes of lesions. Despite the capability of transformers to capture global information, we posit that graph neural network (GNN) offers a more flexible approach to capturing irregular features. GNN constructs connections for each part, leveraging global information in the process. In this study, we propose GNN-Enhanced Dual-Branch Network (GED-Net), a robust framework for lesion segmentation in ultrasound images. The Network consists of an encoder with two branches: one for extracting local features using convolutional neural network (CNN) and the other for extracting global features using GNN, along with a fusion module and a decoder. We evaluated the model on four publicly available medical ultrasound datasets including BUSI, Dataset B, DDTI and OASBUD, and demonstrated superior performance compared to most classical U-Net networks and state-of-the-art U-shaped networks. The source code is available at https://github.com/Yakiw/GED-Net.

Magnetic resonance imaging of the breast: Could it be used as a screening test?

To investigate whether magnetic resonance imaging (MRI) best detects early malignancy in high-risk women. A retrospective, cross-sectional study, carried out at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, included 419 female breast cancer patients aged 16-84 years (mean age of 49). Data were collected from the radiological department's database to compare the MRI, ultrasound (US), and mammography results, with or without tissue biopsy. In diagnosing benign versus malignant lesions, MRI showed significant agreement with tissue biopsy, with high sensitivity (70%) and specificity (87%); its positive predictive value (PPV) was 92% and negative predictive value (NPV) was 56%. While US has a PPV of 84% and NPV of 63%; with a sensitivity (79%) and specificity (71%). In patients without tissue biopsy, there was little difference between mammography and US compared with MRI results. Magnetic resonance imaging is more effective than US and mammography for early detection of BC. It showed high sensitivity in detecting breast lesions and high specificity in characterizing their nature when correlated with pathological results. Ultrasound screening followed by MRI is suggested for undetected or suspected lesions. This will increase the breast lesion detection rate, reduce unneeded tissue biopsies, and enhance the disease's survival rate.

Breast Cancer Detection and Localizing the Mass Area Using Deep Learning

Breast cancer presents a substantial health obstacle since it is the most widespread invasive cancer and the second most common cause of death in women. Prompt identification is essential for effective intervention, rendering breast cancer screening a critical component of healthcare. Although mammography is frequently employed for screening purposes, the manual diagnosis performed by pathologists can be laborious and susceptible to mistakes. Regrettably, the majority of research prioritizes mass classification over mass localization, resulting in an uneven distribution of attention. In response to this problem, we suggest a groundbreaking approach that seeks to identify and pinpoint cancers in breast mammography pictures. This will allow medical experts to identify tumors more quickly and with greater precision. This paper presents a complex deep convolutional neural network design that incorporates advanced deep learning techniques such as U-Net and YOLO. The objective is to enable automatic detection and localization of breast lesions in mammography pictures. To assess the effectiveness of our model, we carried out a thorough review that included a range of performance criteria. We specifically evaluated the accuracy, precision, recall, F1-score, ROC curve, and R-squared error using the publicly available MIAS dataset. Our model performed exceptionally well, with an accuracy rate of 93.0% and an AUC (area under the curve) of 98.6% for the detection job. Moreover, for the localization task, our model achieved a remarkably high R-squared value of 97%. These findings highlight that deep learning can boost the efficiency and accuracy of diagnosing breast cancer. The automation of breast lesion detection and classification offered by our proposed method bears substantial benefits. By alleviating the workload burden on pathologists, it facilitates expedited and accurate breast cancer screening processes. As a result, the proposed approach holds promise for improving healthcare outcomes and bolstering the overall effectiveness of breast cancer detection and diagnosis.

Value of digital breast tomosynthesis in characterization of breast lesions in dense breast

BackgroundPatients with dense breasts have a higher risk factor for developing breast cancer. Digital mammography (DM) exhibits a reduced sensitivity in dense breast tissue as a result of overlapping fibro-glandular tissue. Digital breast tomosynthesis (DBT) solves this problem, so allows better characterization of masses, asymmetries, and parenchymal distortion. It is useful in screening studies for early detection of breast lesions by accurate detection and description of their margins, shape, and extent as well as surrounding structures.ObjectiveWe aimed to evaluate the added value of DBT in characterizing of different breast lesions in dense breasts compared to DM.MethodsA Prospective Cohort study involved ninety female cases having dense breast parenchyma (ACR C & ACR D) having breast complaint lesions detected by DM or ultrasound or recalled from screening units, during the period from June 2021 to Jan 2023.All patient underwent DM and DBT with correlation with the pathological finding or follow up.ResultsAs regards the characterization of breast mass DBT had higher sensitivity, higher specificity (98%, 87% respectively). However, DM showed less sensitivity, less specificity (58%, 80%,) with (p < 0.0001, p = 0.0005 respectively).ConclusionsDBT effectively visualizes overlapping tissue in dense breasts. DBT is important due to its ability to detect breast cancer earlier, characterize lesions accurately, and inspect the margins of masses precisely, along with decreasing recall rates for false-positive results.

Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Breast cancer is a prevalent global disease where early detection is crucial for effective treatment and reducing mortality rates. To address this challenge, a novel Computer-Aided Diagnosis (CAD) framework leveraging Artificial Intelligence (AI) techniques has been developed. This framework integrates capabilities for the simultaneous detection and classification of breast lesions. The AI-based CAD framework is meticulously structured into two pipelines (Stage 1 and Stage 2). The first pipeline (Stage 1) focuses on detectable cases where lesions are identified during the detection task. The second pipeline (Stage 2) is dedicated to cases where lesions are not initially detected. Various experimental scenarios, including binary (benign vs. malignant) and multi-class classifications based on BI-RADS scores, were conducted for training and evaluation. Additionally, a verification and validation (V&V) scenario was implemented to assess the reliability of the framework using unseen multimodal datasets for both binary and multi-class tasks. For the detection tasks, the recent AI detectors like YOLO (You Only Look Once) variants were fine-tuned and optimized to localize breast lesions. For classification tasks, hybrid AI models incorporating ensemble convolutional neural networks (CNNs) and the attention mechanism of Vision Transformers were proposed to enhance prediction performance. The proposed AI-based CAD framework was trained and evaluated using various multimodal ultrasound datasets (BUSI and US2) and mammogram datasets (MIAS, INbreast, real private mammograms, KAU-BCMD, and CBIS-DDSM), either individually or in merged forms. Visual t-SNE techniques were applied to visually harmonize data distributions across ultrasound and mammogram datasets for effective various datasets merging. To generate visually explainable heatmaps in both pipelines (stages 1 and 2), Grad-CAM was utilized. These heatmaps assisted in finalizing detected boxes, especially in stage 2 when the AI detector failed to automatically detect breast lesions. The highest evaluation metrics achieved for merged dataset (BUSI, INbreast, and MIAS) were 97.73% accuracy and 97.27% mAP50 in the first pipeline. In the second pipeline, the proposed CAD achieved 91.66% accuracy with 95.65% mAP50 on MIAS and 95.65% accuracy with 96.10% mAP50 on the merged dataset (INbreast and MIAS). Meanwhile, exceptional performance was demonstrated using BI-RADS scores, achieving 87.29% accuracy, 91.68% AUC, 86.72% mAP50, and 64.75% mAP50-95 on a combined dataset of INbreast and CBIS-DDSM. These results underscore the practical significance of the proposed CAD framework in automatically annotating suspected lesions for radiologists.

Comparison of Sonographic Findings with Diagnostic Mammography.

Breast cancer is the leading female cancer worldwide with a high mortality rate. Early detection of the suspicious lesion is crucial for better prognosis. Higher breast density decreases the sensitivity of mammogram. Ultrasound can differentiate between cystic and solid masses and further characterize these as benign or possibly malignant. Our objective was to compare the findings of sonography with diagnostic mammography. This was a cross sectional study including 125 females who underwent diagnostic mammogram in a tertiary care center. The mammograms were evaluated and the patients were scanned by ultrasound and categorized as per ACR- BIRADS category. The findings of diagnostic mammography were compared with that of ultrasonography using SPSS version 25. The heterogeneously dense breast in diagnostic mammography corresponded to the heterogenous- fibroglandular breast in ultrasonography. In majority, ultrasound increased the BIRADS category for the lesion than designated by the diagnostic mammography. It was particularly useful for category 0 and 3 lesions which were indeterminate and required further imaging. Ultrasound was useful in evaluation of dense breasts with ACR-BIRADS 0 and 3 in diagnostic mammogram. For category 3 and 4 in diagnostic mammogram, ultrasound showed category 1 or 2 lesions which aided to alleviate patient anxiety and avoid unnecessary biopsies. With emerging technological advances in ultrasound, it can used as a powerful tool for breast lesion detection and patient management.

Detection and classification of breast lesions in thermographic images by deep transfer learning hybrid architectures

Detection and classification of breast lesions in thermographic images by deep transfer learning hybrid architectures

Diagnostic Accuracy of Combined Mammography and Ultrasound in the Detection of Malignant Breast Lesions Using Bi-RADS Classification Taking Histopathology as the Gold Standard

Diagnostic Accuracy of Combined Mammography and Ultrasound in the Detection of Malignant Breast Lesions Using Bi-RADS Classification Taking Histopathology as the Gold Standard

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

BackgroundDeep convolutional neural networks have garnered considerable attention in numerous machine learning applications, particularly in visual recognition tasks such as image and video analyses. There is a growing interest in applying this technology to diverse applications in medical image analysis. Automated three-dimensional Breast Ultrasound is a vital tool for detecting breast cancer, and computer-assisted diagnosis software, developed based on deep learning, can effectively assist radiologists in diagnosis. However, the network model is prone to overfitting during training, owing to challenges such as insufficient training data. This study attempts to solve the problem caused by small datasets and improve model detection performance. MethodsWe propose a breast cancer detection framework based on deep learning (a transfer learning method based on cross-organ cancer detection) and a contrastive learning method based on breast imaging reporting and data systems (BI-RADS). ResultsWhen using cross organ transfer learning and BIRADS based contrastive learning, the average sensitivity of the model increased by a maximum of 16.05%. ConclusionOur experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced, and contrastive learning method based on BI-RADS can improve the detection performance of the model.

X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis

Objective. Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications. Approach. We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior. Main results. We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling. Significance. Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality.

BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations

Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation). Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists. In this paper, we propose a middle-ground solution for the dilemma, which is to formulate the training as a weakly- and semi-supervised learning problem that we refer to as malignant breast lesion detection with incomplete annotations. To address this problem, our new method comprises two stages, namely: (1) pre-training a multi-view mammogram classifier with weak supervision from the whole dataset, and (2) extending the trained classifier to become a multi-view detector that is trained with semi-supervised student–teacher learning, where the training set contains fully and weakly-annotated mammograms. We provide extensive detection results on two real-world screening mammogram datasets containing incomplete annotations and show that our proposed approach achieves state-of-the-art results in the detection of malignant breast lesions with incomplete annotations.

Implementation of a clinical breast exam and referral program in a rural district of Pakistan

BackgroundThe role of clinical breast examination (CBE) for early detection of breast cancer is extremely important in lower-middle-income countries (LMICs) where access to breast imaging is limited. Our study aimed to describe the outcomes of a community outreach breast education, home CBE and referral program for early recognition of breast abnormalities and improvement of breast cancer awareness in a rural district of Pakistan.MethodsEight health care workers (HCW) and a gynecologist were educated on basic breast cancer knowledge and trained to create breast cancer awareness and conduct CBE in the community. They were then deployed in the Dadu district of Pakistan where they carried out home visits to perform CBE in the community. Breast cancer awareness was assessed in the community using a standardized questionnaire and standard educational intervention was performed. Clinically detectable breast lesions were identified during home CBE and women were referred to the study gynecologist to confirm the presence of clinical abnormalities. Those confirmed to have clinical abnormalities were referred for imaging. Follow-up home visits were carried out to assess reasons for non-compliance in patients who did not follow-through with the gynecologist appointment or prescribed imaging and re-enforce the need for follow-up.ResultsBasic breast cancer knowledge of HCWs and study gynecologist improved post-intervention. HCWs conducted home CBE in 8757 women. Of these, 149 were warranted a CBE by a physician (to avoid missing an abnormality), while 20 were found to have a definitive lump by HCWs, all were referred to the study gynecologist (CBE checkpoint). Only 50% (10/20) of those with a suspected lump complied with the referral to the gynecologist, where 90% concordance was found between their CBEs. Follow-up home visits were conducted in 119/169 non-compliant patients. Major reasons for non-compliance were a lack of understanding of the risks and financial constraints. A significant improvement was observed in the community’s breast cancer knowledge at the follow-up visits using the standardized post-test.ConclusionsBasic and focused education of HCWs can increase their knowledge and dispel myths. Hand-on structured training can enable HCWs to perform CBE. Community awareness is essential for patient compliance and for early-detection, diagnosis, and treatment.

Abstract PO5-19-11: A novel portable breast cancer screening method - initial institutional experience

Abstract Background: Breast ultrasound (US) is a remarkable supplementary tool alongside mammography and magnetic resonance imaging (MRI) for the detection of breast lesions. US imaging is a fast, reliable method to identify and localize breast pathology. Innovative technologies that aim to timely identification and prompt diagnosis of suspicious lesions in breast cancer screening remain a clinical need. ATUSA is a B-type rechargeable US, shaped as a wearable device. It uses a linear array of transducers with water acting as a coupling medium that recreates a 3-dimensional image of the scanned breast. The ATUSA portability makes this device a practical breast screening method during a healthcare office visit. We report the initial feasibility of ATUSA to detect breast lesions in an outpatient setting from a trial in progress. Methodology: Interim prospective data from 35 female patients presenting with a mass of equal or greater than 1 cm in size, with a baseline handheld US image and pathology reports that evidenced a cancer diagnosis or a lesion suspicious of malignancy. Breast cancer types included invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and invasive lobular carcinoma (ILC), invasive mammary carcinoma (IMC) with mixed ductal and lobular features and atypical lobular hyperplasia (ALH). ATUSA and baseline handheld reference US images were cross-referenced from June 2022 to June 2023. Basic demographics, clinical characteristics, mammogram and/or MRI, and pathology reports were included in the analysis. Patients were followed up for a 30-day period to note occurrence of any adverse event (AE). Institutional Review Board (IRB) approved this study (protocol H-42616). Results: The 35 patients had a mean age of 57 years (range: 25 – 79), mean BMI 29.6 kg/m2 (range: 18.1 – 49.3). Mammographic breast density was fibroglandular tissue 15 (42.8%), followed by heterogeneously dense (HD) 13 (3.7%), mixed HD and fibroglandular 1 (2.8%), mildly dense 1 (2.8%), extremely dense 2 (5.7%), and extremely fatty 1 (2.8%). Breast sizes were 1 (2.8 %) A cup, 11 (31.4%) B cups, 1 (2.8%) B – C cups, 5 (14.2%) C cups, 1 (2.8%) C – D cups, 13 (37.1%) D cups, and 3 (8.5%) DD cups. The 35 scanned lesions had a diagnosis (compared with final resected pathology) classified as IDC 18 (51.4 %), DCIS 2 (5.7 %), mixed IDC and DCIS 6 (17.1 %), ILC 3 (8.5 %), IMC with mixed features 2 (5.7 %), IMC with DCIS 1 (2.8 %), ALH 1 (2.8 %), and 2 (5.7 %) benign masses. ATUSA successfully identified 17/35 (48.5 %) lesions from this group as malignant, from which 4 were hard to distinguish due to shadowing artifact and 3 were underestimated in size. A total of 15 (42.8 %) malignant lesions were missed by ATUSA. No AEs related to the ATUSA device were reported. Conclusion: The ATUSA provides the means to produce a good quality US image during a clinic visit or where a facility lacks conventional US imaging. When compared to the handheld US, ATUSA can be safely used in the outpatient setting without the need of trained specialists but requires further development and testing to match conventional breast imaging. Table 1. Basic clinical characteristics &amp; demographics Citation Format: Margarita Riojas-Barrett, Jessica Montalvan, Mary Bajomo, Ivan Marin, Quan Dang Nguyen, Karla A Sepulveda, Elizabeth Bonefas, Alastair Thompson, Stacey Carter. A novel portable breast cancer screening method - initial institutional experience [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-19-11.

Awareness and Practice of Breast Self-examination for Early Detection of Breast Lesions in Pregnant Women

Awareness and Practice of Breast Self-examination for Early Detection of Breast Lesions in Pregnant Women

AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Accuracy of positron emission tomography-computed tomography in the assessment of metabolically negative axillary lymph nodes in breast cancer

Background Breast cancer is a significant public health concern, and early detection of breast lesions is crucial for improved patient outcomes. Accurate assessment of axillary lymph node involvement in breast cancer is vital for staging and treatment decisions. The purpose of this study was to assess to what extent positron emission tomography (PET)-computed tomography (CT) can be used in the assessment of negative axillary lymph nodes in breast cancer and to determine if pathological variability can affect the result of the PET-CT. Patients and methods This prospective study was carried out on 50 female patients with locally advanced breast cancer and with negative PET-CT scans for active axillary lymph nodes. All studied cases underwent postoperative pathological examination to be compared with preoperative PET-CT results. Results Of the studied cases, 84% showed negative axillary lymph node involvement. PET/CT exhibited high sensitivity (95.24%) and specificity (87.5%) in predicting positive nodes, with an axillary maximum standardized uptake value cutoff value of 4.22 (area under the curve=0.923). Logistic regression revealed a significant association between higher T stage and positive lymph nodes (P&lt;0.05). Conclusion PET/CT demonstrates strong predictive accuracy in detecting metabolically negative axillary lymph nodes in breast cancer patients.