Diagnosis Of Breast Tumors Research Articles

Investigating tumor immunogenicity in breast cancer: deciphering the tumor immune response to enhance therapeutic approaches

The interplay between immune cells and malignant cells represents an essential chapter in the eradication of breast cancer. This widely distributed and diverse form of cancer represents a major threat to women worldwide. The incidence of breast cancer is related to several risk factors, notably genetic predisposition and family antecedents. Despite progress in treatment modalities varying from surgery and chemotherapy to radiotherapy and targeted therapies, persistently high rates of recurrence, metastasis, and treatment resistance underscore the urgent need for new therapeutic approaches. Immunotherapy has gained considerable ground in the treatment of breast cancer, as it takes advantage of the complex interactions within the tumor microenvironment. This dynamic interplay between immune and tumor cells has become a key point of focus in immunological research. This study investigates the role of various cancer markers, such as neoantigens and immune regulatory genes, in the diagnosis and treatment of breast tumors. Moreover, it explores the future potential of immune checkpoint inhibitors as therapeutically effective agents, as well as the challenges that prevent their efficacy, in particular tumor-induced immunosuppression and the difficulty of achieving tumor specificity.

Current principles of hybrid imaging of breast cancer: a review of the literature

Background. Breast cancer is a major global public health problem and is the most common malignancy affecting women. Bone metastases are a common complication of advanced breast cancer, with nearly 65% of patients developing bone metastases. The presence of bone metastases can cause severe morbidity and mortality, as well as impair quality of life and increase the risk of skeletal damage. Accuratedetection and reliable assessment of therapeutic responses to bone metastases are imperative to inform treatment decisions, preserve quality of life, and ultimately improve overall survival. Thus, early identification of bone metastases is essential for effective treatment strategies and improved patient outcomes. Purpose – describe the modern principles and capabilities of hybrid imaging used for diagnosis, staging and follow-up of breast tumors. Illustrations are provided to better summarize and demonstrate the main concepts. Materials and methods. The analysis of full-text publications, which were selected through a literature search in foreign databases (PubMed, Scopus, ScienceDirectElsevier) for the period 2019–2023, was carried out. Results and discussion. During the analytical review of the literature, the experience of using modern methods of diagnosing breast tumors in Ukraine and abroad was analyzed. The selection of radiopharmaceutical drugs and tracers is considered. It has been established that the use of hybrid PET/CT and PET/MRI is the most promising imaging modality for the evaluation of breast cancer, providing a fully integrated morphological and functional imaging assessment. Conclusions. Hybrid imaging plays a key role in the diagnosis, staging, treatment selection, and follow-up of breast tumors. Awareness of this disease can reduce delay in diagnosis and facilitate interdisciplinary expert care.

Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.

DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited. Here, a molecular learning vector quantization neural network (LVQNN) model based on DNA strand displacement (DSD) technology for breast tumor diagnosis is developed. Compared to previous work, the molecular LVQNN boasts powerful computing abilities, handling high-dimensional data for intelligent cancer diagnosis. To verify the feasibility and versatility of the network, two distinct typical datasets are selected: one from a single source with cell morphology data from 569 cases, and a more extensive one spanning different populations and ages, with miRNA gene expression data from 1881 cases. By using the molecular LVQNN, diagnostic experiments are conducted on 50 and 120 public individuals from these two datasets, respectively, achieving accuracy rates of 94% and 97.5%. This study demonstrates that the LVQNN model exhibits significant advantages in breast cancer diagnosis and enhances diagnostic accuracy while introducing new approaches for intelligent cancer diagnosis, anticipated to bring significant breakthroughs and application prospects to precise cancermedicine.

Convergence of various computer-aided systems for breast tumor diagnosis: a comparative insight

Convergence of various computer-aided systems for breast tumor diagnosis: a comparative insight

Are Meteorin-Like Peptide and Asprosin Important in the Diagnosis of Breast Tumors?

Breast cancer (BC) is one of the most common and leading causes of death in women. Therefore, early and accurate diagnosis is vital.In this study, meteorin-like (METRNL) peptide and asprosin levels were examined in breast tissue in invasive ductal carcinoma (IDC) of the breast, and the roles of these molecules in the diagnosis of BC were investigated. In this retrospective study, tissues from patients with BC in the Pathology Department Laboratory of Fırat University Faculty of Medicine,Elazığ, Turkey, were used. Samples from 30 patients were used. The control group consisted of healthy breast tissues from the same patients. The pathology group consisted of breast tissues with IDC from the same patients.Breast tissue samples from both groups were evaluated immunohistochemically for METRNL and asprosin. Statistically significant differences were observed between both groups in terms of METRNL and asprosin. It was observed that METRNL and asprosin immunoreactivities were higher in breast tissues with IDC than in healthy breast tissues (p<0.001). When the study results were evaluated, it was seen that there was a significant relationship between healthy breast tissuesand the ones with IDC in terms of METRNL and asprosin. It is thought that both METRNL and asprosin may be really important in the future for the early diagnosis and treatment of BC.

Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

PurposeTo conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists. Materials and MethodsThis multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods. ResultsThe findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors. ConclusionThe fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model’s potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis.

IL21R hypomethylation as a biomarker for distinguishing benign and malignant breast tumours

ABSTRACT Some benign and malignant breast tumours are similar in pathological morphology, which are difficult to be distinguished in clinical diagnosis. In this study, we intended to explore novel biomarkers for differential diagnosis of benign and malignant breast tumours. Methylation EPIC 850K beadchip and RNA-sequencing were used to analyse 29 tissue samples from patients with early-stage breast cancer (BC) and benign breast tumours for differently methylated and expressed genes. The altered methylation of IL21R was semi-quantitatively validated in an independent study with 566 tissue samples (279 BC vs. 287 benign breast tumours) using mass spectrometry. Binary logistic regression analysis was performed to evaluate the association between IL21R methylation and BC. BC-associated IL21R hypomethylation and overexpression were identified in the discovery round. In the validation round, BC patients presented significant IL21R hypomethylation compared to women with benign breast tumours (ORs ≥1.29 per-10% methylation, p-values ≤ 5.69E–14), and this hypomethylation was even enhanced in BC patients with ER-negative and PR-negative tumours as well as with triple-negative tumours. The methylation of IL21R showed efficient discriminatory power to distinguish benign breast tumours from BC (area under curve (AUC) = 0.88), and especially from ER-negative BC (AUC = 0.95), PR-negative BC (AUC = 0.93) and triple-negative BC (AUC = 0.96). We disclosed significant IL21R hypomethylation in patients with BC compared to women with benign breast tumours, and revealed the somatic change of DNA methylation could be a potential biomarker for molecular pathology of BC.

Abstract PO5-17-02: Triple-negative breast cancer as an untraceable tumor compared to other immunohistochemical subtypes: a cross-sectional study

Abstract Objective: To compare the form of presentation at diagnosis of triple negative breast tumors with other immunohistochemical subtypes, in relation to clinical examination or imaging examination. Methods: This is a cross sectional study that evaluated data related to the presentation of breast carcinomas at diagnosis (clinical examination or screening imaging examination findings), immunohistochemical subtype, histological grade and tumor size, in diagnosed and referred patients. for the Mastology service at Hospital São Paulo – Unifesp, in São Paulo – Brazil, in the period between 2012 and 2021, by analyzing data from the medical records of these patients. Results: Among the 699 invasive carcinomas diagnosed, 246 patients were diagnosed with luminal profile A (35.2%), 257 luminal B (36.8%), 58 luminal B HER-2+ (8.3%), 26 HER-2 enriched (3.7%) and 112 triple negative (16%). Triple negative carcinomas had a higher rate of diagnosis by clinical examination (86.6%), 6.65 times more likely to be diagnosed by clinical examination than luminal A, in addition to having a larger lesion. The triple negative and HER-2 enriched subtypes presented the worst scenarios of clinical stage at diagnosis. Among the tumors diagnosed by screening examination, those belonging to the luminal A were associated with nodules seen on imaging (in 45.1%) and the subtypes luminal B and luminal B HER-2+ with the appearance of microcalcifications. There were no cases of triple negatives involving microcalcifications in our sample. Asymmetries were more common in images of luminal B HER-2+ tumors. In the multivariate analysis, it was observed that only the histological grade and the Ki-67 value are related to the form of diagnosis. Tumors with histological grade 2 or 3 are more likely to be diagnosed by clinical examination and not be traceable in relation to histological grade 1. As for Ki-67, its increase of 1 percentage point leads to an increase of 1.3% in the chance finding on physical examination. There was no correlation of hormone receptors and HER-2 with the chance of the tumor not being screenable. Conclusions: triple negative subtype breast carcinomas present a higher risk of diagnosis by clinical examination compared to other immunohistochemical subtypes and, therefore, tend not to be traceable. Parameters such as high Ki-67 and histological grade correlate with this trend. Citation Format: Morgana Silva, Afonso Nazário, Vanessa Sanvido. Triple-negative breast cancer as an untraceable tumor compared to other immunohistochemical subtypes: a cross-sectional study [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 PO5-17-02.

Effect of contrast agent on T2-weighted fat-suppressed imaging and diffusion-weighted imaging in the diagnosis of breast tumors.

Although previous studies have shown that the injection of contrast agents can improve image quality, the specific impact of this on T2-weighted fat-suppressed (T2 FS) and diffusion-weighted imaging (DWI) sequences in the diagnosis of breast cancer remains incompletely understood. In particular, there is insufficient research on how contrast agents affect the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values within these sequences, and how these changes influence the diagnosis of benign and malignant breast tumors. Breast magnetic resonance images (MRI) were obtained from 178 consecutive patients on a 3T scanner. The SNR and CNR of lesions on T2 FS sequence were calculated before and after contrast agent injection and compared. Differences between pre- and post-contrast ADC in identifying different tumor types were compared using the Kruskal-Wallis H-test and the paired comparison test. The accuracy of ADC values between pre- and post-contrast in distinguishing benign and malignant breast masses was assessed using receiver operating characteristic (ROC) curves. The SNR and CNR of T2 FS sequence increased after contrast injection, and especially for invasive cancer and benign tumor, the increase was significant. For DWI, there was a slight increase or decrease of ADC values after contrast injection, but the ADC values before and after contrast had a similar effect in identifying different types of tumors. In the ROC curve analysis for assessing benign and malignant breast tumors, the area under the curve (AUC) before and after contrast showed similar results. Contrast agent injection can improve the SNR and CNR of T2 FS sequence, thus providing higher quality images for the diagnosis of breast lesions. Furthermore, injection of contrast agent had little effect on the ability of ADC values to identify different types of lesions and both ADC values before and after the contrast agent were able to distinguish between benign and malignant tumors with almost the same accuracy.

Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis.

Breast cancer is becoming a significant global health challenge, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods from multi-parametric MRI still have limitations in exploring inter-modality information and focusing task-informative modality/modalities. To address these shortcomings, we propose a Modality-Specific Information Disentanglement (MoSID) framework to extract both inter- and intra-modality attention maps as prior knowledge for guiding tumor segmentation. Specifically, by disentangling modality-specific information, the MoSID framework provides complementary clues for the segmentation task, by generating modality-specific attention maps to guide modality selection and inter-modality evaluation. Our experiments on two 3D breast datasets and one 2D prostate dataset demonstrate that the MoSID framework outperforms other state-of-the-art multi-modality segmentation methods, even in the cases of missing modalities. Based on the segmented lesions, we further train a classifier to predict the patients' response to radiotherapy. The prediction accuracy is comparable to the case of using manually-segmented tumors for treatment outcome prediction, indicating the robustness and effectiveness of the proposed segmentation method. The code is available at https://github.com/Qianqian-Chen/MoSID.

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

Application Exploration of Medical Image-aided Diagnosis of Breast Tumour Based on Deep Learning.

Nowadays, people attach increasing importance to accurate and timely disease diagnosis and personalized treatment. Because of the uncertainty and latency of the pathogenesis, it is difficult to detect breast tumour early. With higher resolution, magnetic resonance imaging (MRI) has become an important method for early detection of cancer in recent years. At present, DL technology can automatically study imaging features of different depths. This work aimed to use DL to study medical image-assisted diagnosis. The image data were collected from the patients. ROI (region of interest) containing the complete tumor area in the medical image was generated. The ROI image was extracted, and the extracted feature data were expanded. By constructing a three-dimensional (3D) CNN model, the evaluation indicators of breast tumour diagnosis results have been proposed. In the experiment part, 3D CNN model and other models have been used to diagnose the medical image of breast tumour. The 3D CNN model exhibited good ROI region extraction effect and breast tumor image diagnosis effect, and the average diagnostic accuracy of breast tumor image diagnosis was 0.736, which has been found to be much higher than other models and could be applied to breast tumor medical image-aided diagnosis. The 3D CNN model has been trained by combining the two-dimensional CNN training mode, and the evaluation index of diagnostic results has been established. The experimental part verified the medical image diagnosis effect of the 3D CNN model. The model had exhibited a high ROI region extraction effect and breast tumor image diagnosis effect.

A deep learning based holistic diagnosis system for immunohistochemistry interpretation and molecular subtyping

BackgroundBreast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), and Ki67, exhibit distinct symptom characteristics and sensitivity to different treatment. The immunohistochemical method, one of the most common detecting tools for tumour markers, is heavily relied on artificial judgment and in clinical practice, with an inherent limitation in interpreting stability and operating efficiency. Here, a holistic intelligent breast tumour diagnosis system has been developed for tumour-markeromic analysis, combining the automatic interpretation and clinical suggestion. MethodsThe holistic intelligent breast tumour diagnosis system included two main modules. The interpreting modules were constructed based on convolutional neural network, for comprehensively extracting and analyzing the multi-features of immunostaining. Referring to the clinical classification criteria, the interpreting results were encoded in a low-dimensional feature representation in the subtyping module, to efficiently output a holistic detecting result of the critical tumour-markeromic with diagnosis suggestions on molecular subtypes. ResultsThe overexpression rates of HER2, ER, PR, and Ki67, as well as an effective determination of molecular subtypes were successfully obtained by this diagnosis system, with an average sensitivity of 97.6 % and an average specificity of 96.1 %, among those, the sensitivity and specificity for interpreting HER2 were up to 99.8 % and 96.9 %. ConclusionThe holistic intelligent breast tumour diagnosis system shows improved performance in the interpretation of immunohistochemical images over pathologist-level, which can be expected to overcome the limitations of conventional manual interpretation in efficiency, precision, and repeatability.

Dielectric Characterization of Ex-Vivo Breast Tissues: Differentiation of Tumor Types through Permittivity Measurements.

Early analysis and diagnosis of breast tumors is essential for either quickly launching a treatment or for seeing the evolution of patients who, for instance, have already undergone chemotherapy treatment. Once tissues are excised, histological analysis is the most frequent tool used to characterize benign or malignant tumors. Dielectric microwave spectroscopy makes use of an open-ended coaxial probe in the 1-8 GHz frequency range to quickly identify the type of tumor (ductal carcinoma, lobular carcinoma, mucinous carcinoma and fibroadenoma). The experiment was undertaken with data from 70 patients who had already undergone chemotherapy treatment, which helped to electrically map the histological tissues with their electric permittivity. Thus, the variations in the permittivity of different types of tumors reveal distinctive patterns: benign tumors have permittivity values lower than 35, while malignant ones range between 40 and 60. For example, at a frequency of 2 GHz, the measured permittivity was 45.6 for ductal carcinoma, 33.1 for lobular carcinoma, 59.5 for mucinous carcinoma, and 27.6 for benign tumors. This differentiation remains consistent in a frequency range of 1 to 4.5 GHz. These results highlight the effectiveness of these measurements in the classification of breast tumors, providing a valuable tool for quick and accurate diagnosis and effective treatment.

Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data Augmentation

To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform the classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a conditional generative adversarial network-based data augmentation model on the classifier’s performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge.

UWB Resonator-based Supervised Learning for Breast Tumor Diagnosis

UWB Resonator-based Supervised Learning for Breast Tumor Diagnosis

Leveraging an optimized deep belief network based on a developed version of artificial rabbits optimization for breast tumor diagnosis

Artificial intelligence can be extremely useful for identifying the location and size of a tumor by analyzing medical breast cancer images. This can help doctors to make more accurate diagnoses and plan more effective treatment strategies. It can also be used to analyze the texture and shape of tumors, which can provide important information about their malignancy and potential for metastasis. This information can be used to guide treatment decisions and monitor the progress of therapy. This paper presents a study designed to leverage an optimized deep belief network (DBN) based on a developed version of artificial Rabbits optimization (ARO) to diagnose breast tumors. The proposed model was designed and evaluated using data from the MIAS breast cancer database in reference. The performance of the proposed model was compared with other existing models for accuracy, speed, and sensitivity metrics. Results show that the proposed model can achieve better accuracy and faster performance than existing deep-learning techniques when applied to the classification of breast tumors. Furthermore, the proposed model exhibited improved sensitivity in detecting malignant tumors compared to existing models.

Diagnostic value of the vascular index measured by superb microvascular imaging for evaluating breast tumors: a meta-analysis.

This meta-analysis aims to assess the accuracy of superb microvascular imaging (SMI) using the vascular index (VI) in the diagnosis of breast tumors. PubMed, Web of Science, Embase, Cochrane Library and Scopus were searched for relevant literature by two researchers until March 14, 2023. The Stata Version 16.0 software was utilized to compute the pooled values for sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odd ratios (DOR). Heterogeneity among the included literature was assessed using the I2 statistic and Q test. Conducting influence analysis was used to ensure the robustness of the pooled conclusions and the Deeks' funnel plot asymmetry test to assess publication bias. We also performed the summary receiver operating characteristic (SROC) curve. Six studies included 1200 breast lesions. The ultimate results in the VI of SMI are as follows: The pooled Sen was 0.80 (95% confidence interval(CI), 0.75-0.85), the pooled Spe was 0.68 (95% CI 0.63-0.74), the pooled PLR was 2.54 (95% CI 2.07-3.12), the pooled NLR was 0.29 (95% CI 0.22-0.38), the pooled DOR was 8.91 (95% CI 5.62-14.13), and the area under the SROC (AUC) was 0.81. The application of SMI using VI may have the potential to benefit the patients and represents a valuable quantitative parameter of SMI for the diagnosis of breast neoplasms.

Contribution of mammography in the diagnosis of breast tumours at the senology unit of the chu aristid le dantec hospital

Introduction: In Senegal, breast cancer is the second most common cancer in women after cervical cancer.1 It is responsible for a high mortality rate of around 70% after five years,2 as in many other developing countries. Mammography plays an important role in the screening and diagnosis of breast diseases. The unfavourable conditions in our regions are at the origin of the inaccessibility and unavailability of this diagnostic tool. Objectives: The objective of our study was to evaluate the availability, accessibility and use of mammography in the diagnosis of breast diseases in the Senology Unit of the University Hospital Aristide Le Dantec in Dakar. Materials and methods: This was a descriptive and analytical cross-sectional study of 120 patients followed at the Senology Unit of the Aristide Le Dantec University Hospital in Dakar, during the period from 1 January 2010 to 31 December 2014. The parameters studied were: the socio-economic profile of women, the geographical distribution of functional mammography centres, the time taken to perform mammography, its cost, its quality and its contribution to diagnosis. Results: In our study, the majority of patientes lived in the suburbs of Dakar (67.5%). The average age was 44.2 years. More than half (54.2%) had no regular source of income. The rate of use of mammography was 8.9%. The average cost was 48,944 FCFA. The average time taken to carry out a mammogram after it was prescribed was 28.8 days. In 76.6% of cases, mammography was performed for diagnostic purposes. In no case was an additional examination performed. As regards the quality of the mammographic examination, in 17.5% of cases the usual marking was not respected and the transparency of the images was not optimal in 26.6% of cases. The validity criteria were not met in 18.3% of the results and the American College of Radiology (ACR) BIRADS classification was used in 95 patients (79.2%). Payment before the examination was carried out was in cash in the majority of cases (70.8%). However, the difference in cost was significant according to the type of imaging centre (p = 0.015). Conclusion: Mammography was characterised by the unavailability of centres, a fairly long time to carry out the examination and a lower quality of the examinations.

Organization of work on prevention and early diagnosis of breast tumors

A constant object of careful and comprehensive study is the issue of breast diseases, which has become particularly relevant due to their significant growth, especially, of cancer of this localization. Promising forms of preventive work on breast diseases are patient schools. The experience of existing schools has demonstrated new professional capabilities of nursing specialists who are the schools’ organizers and leaders. Target. To evaluate the impact of the School for Breast Tumor Prevention on the incidence of malignant neoplasms in the female population. Results. The existing system for the prevention and early diagnosis of mammary tumors in a medical organization was analyzed, the level of knowledge on the prevention and early diagnosis of mammary tumors among the attached female population and nursing specialists was assessed, and the role of nursing staf f in sanitary educational work was determined. Conclusion. The results of sanitary education work are one of the determining factors influencing the reduction in the incidence of malignant neoplasms among the female population.