Screening Tool For Breast Cancer Research Articles

Benefits and Risks of Mammography Screening in Women Ages 40 to 49 Years

Mammography is a widely used screening tool for breast cancer. While it has been shown to reduce mortality from breast cancer, its benefits and risks vary depending on the age group being screened. This paper aims to provide a comprehensive review of the benefits and risks of mammography screening specifically in women aged 40 to 49 years. The analysis includes statistical data, benefits in terms of early detection and mortality reduction, as well as the potential risks such as false positives, overdiagnosis, and radiation exposure.

Ultrasonography is an effective tool for breast cancer screening in individuals with severe motor and intellectual disabilities.

Individuals with severe motor and intellectual disabilities have become an aging population, and high cancer morbidity and mortality are critical issues affecting their survival. Cancer screening is a crucial method of resolving this issue; however, a suitable screening method for them has not been established. We used ultrasonography alone and performed breast cancer screening for women over 30 years old in our facility from 2016 to 2023. We observed the outcomes and calculated the recall/detection rate in this screening. Three cases among 379 tested positive in this screening, all of which underwent radical surgery. They are alive and well without relapse present. We detected these breast cancer cases with a low recall rate. We were able to successfully detect breast cancer cases using ultrasonography alone. Ultrasonography is an effective and feasible tool for breast cancer screening in individuals with severe motor and intellectual disabilities.

Performance evaluation of six digital mammography systems

The strong advance in digital mammography as a tool for breast cancer screening requires that the new systems be characterized. Quality control protocols describe the methodology to evaluate systems performance. Detective quantum efficiency (DQE) is the most suitable parameter for describing the imaging performance of an X-ray imaging device. The objective of this study was to characterize in terms of DQE six digital mammography systems, at different detector air kerma (DAK) levels for a variety of beam qualities. A Carestream EHR-M3 CR detector, a small field digital mammography system Siemens Opdima, and full field digital mammography systems Planmed Clarity, GE Essential, GE Pristina and GE Crystal Nova were characterized using the European Guidelines protocol. Modulation transfer function (MTF), normalized noise power spectrum (NNPS) and DQE were determined, for an extended detector air kerma range. Attenuated beam qualities (obtained by adding 2 mm Al to the corresponding beam qualities) used were 28 kV with anode/filter combination Mo/Mo for the EHR-M3, Opdima, Essential and Pristina systems; 28 kV W/Ag for the Clarity; 28 kV W/Rh for the Crystal Nova; 28 kV Mo/Rh for the EHR-M3; and 34 kV Rh/Ag for the Pristina. Two groups were identified in MTF analysis, and the group composed of Clarity, Opdima and Essential presented the highest MTF values. In NNPS analysis, three main groups were identified, in which the systems Crystal Nova and Essential presented lower and higher NNPS, respectively. Crystal Nova system also exhibited higher DQE values for low spatial frequencies, while Opdima and Clarity presented higher DQE values for medium to high spatial frequency. Agreement with data from literature indicates that the systems evaluated are operating under typical conditions. Evidence of improvement in detectors performance was found.

Evaluating Clinical Efficacy of Thermalytix as a Breast Cancer Screening Tool in Young Women

Abstract Objective: Breast cancer remains a significant health challenge worldwide, particularly among women under 45 years of age who often present with dense breast tissue. Mammography, the current gold standard for breast cancer screening, is less effective in this demographic due to reduced sensitivity in detecting malignancies within dense breast tissue. This study evaluates the efficacy of Thermalytix, an artificial intelligence-driven, noninvasive, and radiation-free thermal imaging tool, as a screening modality for breast cancer in young women through a meta-analysis of published studies. Materials and Methods: This meta-analysis aggregated the data from three clinical studies involving a total of 1187 women who first underwent a Thermalytix test followed by reference standard of care (SoC) tests, which included one or more mammography, ultrasound, and biopsy. Among these women, 463 were under 45 years of age and were eligible for this study. Thermalytix analyzed high-resolution thermal images of the breast, utilizing novel radiomic features such as hotspots, vascular patterns, and areolar characteristics to predict malignancy. The performance of Thermalytix was evaluated by computing its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with 95% confidence intervals (95% CIs), in both the overall population and the younger cohort by aggregating the data from all studies. Results: Among the 463 young women under 45 years, 43 were diagnosed with breast cancer as per SoC. When raw data were aggregated from this young women cohort, Thermalytix resulted in a sensitivity of 90.7% (95% CI: 82.0%–99.4%), a specificity of 82.1% (95% CI: 78.5%–85.8%), a PPV of 34.2% (95% CI: 25.5%–42.9%), and an NPV of 98.9% (95% CI: 97.7%–100%). The pooled sensitivity and specificity of Thermalytix using the random-effects model were estimated to be 96.0% (95% CI: 88.9%–100%) and 82.3% (95% CI: 78.6%–85.9%), respectively. Further, in the entire population of 1187, Thermalytix showed an aggregated sensitivity of 88.3% (95% CI: 83.3%–93.2%), specificity of 84.7% (95% CI: 82.5%–86.9%), PPV of 47.7% (95% CI: 42.0%–53.3%), and NPV 97.9% (95% CI: 96.9%–98.8%). Conclusions: Thermalytix demonstrated high sensitivity and NPV in women under 45 years of age, suggesting its potential as an effective screening modality for younger women who face challenges with conventional screening methods due to dense breast tissue.

Clinical application of convolutional neural network for mass analysis on mammograms.

The detection of masses on mammogram represents one of the earliest signs of a malignant breast cancer. However, masses may be hard to detect due to dense breast tissue, leading to false negative results. In this study, we aimed to explore the clinical application of the convolutional neural network (CNN)-based deep learning (DL) system constructed in our previous work as an objective and accurate tool for breast cancer screening and diagnosis in Asian women. This retrospective analysis included 324 patients with masses detected on mammograms at Shenzhen People's Hospital between April and December 2019. (I) Detection: images were independently analyzed by two junior radiologists who were blinded to relative results. Then, a senior radiologist analyzed the images after reviewing all the relevant information as the reference. (II) Classification: masses were classified by the same two junior radiologists and in consensus by two other seniors. Images were also input into the DL system. The sensitivity of detection by junior radiologists and the DL system, effects of different factors [breast density; patient age; morphology, margin, size, breast imaging reporting and data system (BI-RADS) category of the mass] on detection, the accuracy, sensitivity, and specificity of classification, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated. A total of 618 masses were detected. The detection sensitivity of the two junior radiologists [78.0% (482/618) and 84.0% (519/618), respectively] was lower than that of the DL system [86.2% (533/618)]. Breast density significantly affected the detection by two junior radiologists (both P=0.030), but not by the DL system (P=0.385). The AUC for classifying masses as negative (BI-RADS 1, 2, 3) or positive (BI-RADS 4A, 4B, 4C, 5) for the DL system was significantly higher compared to those of the two junior radiologists, but not significantly different compared to seniors [DL system, 0.697; junior, 0.612 and 0.620 (P=0.021, 0.019); senior in consensus, 0.748 (P=0.071)]. The CNN-based DL system could assist junior radiologists in improving mass detection and is not affected by breast density. This DL system may have clinical utility in women with dense breasts, including reducing the impact caused by inexperienced radiologists and the potential for missed diagnoses.

New era of Diffusion weighted imaging as screening tool for breast cancer

New era of Diffusion weighted imaging as screening tool for breast cancer

Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening

IntroductionThis study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals.MethodsIn this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method. ResultsThe results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups. DiscussionIn conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.

A review on polymeric nanocomposites for the electrochemical sensing of breast cancer biomarkers

Breast cancer is a rapidly spreading disease and early detection of associated biomarkers in body fluids, particularly blood, is crucial. Electrochemical biosensors, known for their high sensitivity, selectivity, cost-effectiveness, and miniaturization potential, have emerged as a promising tool for breast cancer screening. This review article focuses on the integration of polymers and nanosized objects to enhance the performance of electrochemical biosensors in analyzing breast cancer biomarkers in serum samples, breast cancer cell lysates, and tumor tissues. Recent developments in this field have demonstrated the significant benefits of these sensors, including improved sensitivity and selectivity. By incorporating nanocomposites into the electrochemical sensing interface, signal amplification is achieved, resulting in enhanced detection limits and accuracy. The use of different dimensionalities of nanomaterials (0D, 1D, 2D, and 3D nanostructured interfaces) in polymer-based nanocomposites has further contributed to the development of sensors with unique properties for sensitive and rapid detection. The potential of electrochemical biosensors based on polymer nanocomposites to revolutionize disease management lies in their ability to enable user-friendly breast cancer screening and facilitate early detection. However, there are still limitations that need to be addressed, and further research is required to optimize the performance of these sensors. The article also discusses the future perspectives and real-world applications of electrochemical biosensors based on polymer nanocomposites while considering the challenges that lie ahead.

Autoantibody Diversity Is Augmented in Women with Breast Cancer and Is Related to the Stage of the Disease.

Breast cancer (BC) is the most frequent malignant neoplasia and leading cause of cancer mortality for women. A timely diagnosis of BC is crucial to ensure the best chances of survival. Among the various screening tools for BC, antibodies directed towards self-antigens or tumor-associated antigens (autoantibodies) have emerged as an alternative to image-based screening modalities. However, little attention has been paid to the global diversity of autoantibodies. This work aimed to analyze the diversity of autoantibodies reactive to antigens expressed by the BC cell line T47D in the sera of Mexican women with BC, benign breast pathology (BBP), or without breast pathology (WBP). We found that the diversity of antibodies in the sera was higher in the BC and BBP groups than in the WBP group. Likewise, the diversity changed with the progression of BC. Our results show and measure the complexity of the antibody response in breast health and disease.

Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning.

During Basic screening, it is challenging, if not impossible to detect breast cancer especially in the earliest stage of tumor development. However, measuring the electrical impedance of biological tissue can detect abnormalities even before being palpable. Thus, we used impedance characteristics data of various breast tissue to develop a breast cancer screening tool guided and augmented by a deep learning (DL). A DL algorithm was trained to ideally classify six classes of breast cancer based on electrical impedance characteristics data of the breast tissue. The tool correctly predicted breast cancer in data of patients whose breast tissue impedance was reported to have been measured when other methods detected no anomaly in the tissue. Furthermore, a DL-based approach using Long Short-Term Memory (LSTM) effectively classified breast tissue with an accuracy of 96.67%. Thus, the DL algorithm and method we developed accurately augmented breast tissue classification using electrical impedance and enhanced the ability to detect and differentiate cancerous tissue in very early stages. However, more data and pre-clinical is required to improve the accuracy of this early breast cancer detection and differentiation tool.

Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms.

Rapid Detection of Volatile Organic Metabolites in Urine by High-Pressure Photoionization Mass Spectrometry for Breast Cancer Screening: A Pilot Study.

Despite surpassing lung cancer as the most frequently diagnosed cancer, female breast cancer (BC) still lacks rapid detection methods for screening that can be implemented on a large scale in practical clinical settings. However, urine is a readily available biofluid obtained non-invasively and contains numerous volatile organic metabolites (VOMs) that offer valuable metabolic information concerning the onset and progression of diseases. In this work, a rapid method for analysis of VOMs in urine by using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) coupled with dynamic purge injection. A simple pretreatment process of urine samples by adding acid and salt was employed for efficient VOM sampling, and the numbers of metabolites increased and the detection sensitivity was improved after the acid (HCl) and salt (NaCl) addition. The established mass spectrometry detection method was applied to analyze a set of training samples collected from a local hospital, including 24 breast cancer patients and 27 healthy controls. Statistical analysis techniques such as principal component analysis, partial least squares discriminant analysis, and the Mann-Whitney U test were used, and nine VOMs were identified as differential metabolites. Finally, acrolein, 2-pentanone, and methyl allyl sulfide were selected to build a metabolite combination model for distinguishing breast cancer patients from the healthy group, and the achieved sensitivity and specificity were 92.6% and 91.7%, respectively, according to the receiver operating characteristic curve analysis. The results demonstrate that this technology has potential to become a rapid screening tool for breast cancer, with significant room for further development.

Efficient infrared image processing and machine learning algorithm for breast cancer screening

ABSTRACT Breast cancer is a significant global health concern, and early detection is crucial for improving survival rates. The article reviews various studies that investigate different methods for detecting breast cancer using non-invasive imaging techniques with a focus on thermal imaging, including feature extraction, image segmentation, and machine learning. It concludes that developing efficient and accurate breast cancer screening software requires an integrated approach to data collection, image processing, and machine learning algorithms. The article presents a novel technique for developing breast cancer screening software using Rotational Thermographic Imaging, dynamic temperature-based data collection, Colour-based Infrared Image Processing, and a Machine Learning algorithm to provide a complete breast imaging in sitting position to reduce the chances of missing abnormalities. Image processing and machine learning techniques are utilised to extract a comprehensive relevant feature set from the captured images and used to train a machine learning model. The system was tested on an increasing patient population in a clinical setting deployed at a hospital. The algorithm’s performance was evaluated using several metrics, including sensitivity (82.14%), specificity (98.33%), and accuracy (93.27%). The results demonstrate that the proposed algorithm achieved high accuracy and sensitivity, making it a promising tool for breast cancer screening.

Knowledge and use of different breast cancer screening tools among primary care physicians: A cross-sectional study.

e13673 Background: In Uruguay, breast cancer (BC) is the leading cause of cancer incidence and mortality in women. Objective: To understand the implementation of BC screening among primary care physicians in routine clinical practice and the degree of adherence to the recommendations put forward in 2015 by the Ministry of Public Health (MPH) for the early detection of BC. Methods: This was a descriptive, cross-sectional, observational study. An anonymous survey was administered to physicians working in primary care. Results: 169 physicians were included, 89.4% (151) consider the use of screening mammography decreases mortality from BC, 54.4% (92) indicate mammography from 40 years of age and 38.5% (65) from 50 years of age. The majority (56.8%, 96) indicate mammography every 2 years in the population of women aged 50-69 years. Of the respondents, 65.7% (111) were aware of the national guidelines and 47.9% (81) followed them, while 18.9% (32) followed recommendations from other scientific bodies. Conclusions: This study showed that primary care physicians make correct use of the different BC screening tools. Active measures are needed to develop educational programs for healthcare personnel, which may enable them to disseminate knowledge and positively influence patients' attitudes.

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

Digital mammography is currently the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks associated with the x-ray exposure, the radiation dose must be kept as low as possible while maintaining the diagnostic utility of the generated images, thus minimizing patient risks. Many studies investigated the feasibility of dose reduction by restoring low-dose images using deep neural networks. In these cases, choosing the appropriate training database and loss function is crucial and impacts the quality of the results. In this work, we used a standard residual network (ResNet) to restore low-dose digital mammography images and evaluated the performance of several loss functions. For training purposes, we extracted 256,000 image patches from a dataset of 400 images of retrospective clinical mammography exams, where dose reduction factors of 75% and 50% were simulated to generate low and standard-dose pairs. We validated the network in a real scenario by using a physical anthropomorphic breast phantom to acquire real low-dose and standard full-dose images in a commercially available mammography system, which were then processed through our trained model. We benchmarked our results against an analytical restoration model for low-dose digital mammography. Objective assessment was performed through the signal-to-noise ratio (SNR) and the mean normalized squared error (MNSE), decomposed into residual noise and bias. Statistical tests revealed that the use of the perceptual loss (PL4) resulted in statistically significant differences when compared to all other loss functions. Additionally, images restored using the PL4 achieved the closest residual noise to the standard dose. On the other hand, perceptual loss PL3, structural similarity index (SSIM) and one of the adversarial losses achieved the lowest bias for both dose reduction factors. The source code of our deep neural network is available at https://github.com/WANG-AXIS/LdDMDenoising.

Potential Role of Fourier Transform Infrared Spectroscopy as a Screening Approach for Breast Cancer.

Breast cancer is a heterogeneous disease, and its spread involves a succession of clinical and pathological stages. Screening is predominantly based on mammography, which has critical limitations related to the effectiveness and production of false-positive or false-negative results, generating discomfort and low adherence. In this context, infrared with attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy emerges as a non-destructive sample tool, which is non-invasive, label-free, has a low operating-cost, and requires only a small amount of sample, including liquid plasma samples. We sought to evaluate the clinical applicability of ATR FT-IR in breast cancer screening. ATR FT-IR spectroscopy through its highest potential spectral biomarker could distinguish, by liquid plasma biopsy, breast cancer patients and healthy controls, obtaining a sensitivity of 97%, specificity of 93%, a receiver operating characteristic ROC curve of 97%, and a prediction accuracy of 94%. The main variance between the groups was mainly in the band 1511 cm-1 of the control group, 1502 and 1515 cm-1 of the cancer group, which are the peaks of the bands referring to proteins and amide II. ATR FT-IR spectroscopy has demonstrated to be a promising tool for breast cancer screening, given its time efficiency, cost of approach, and its high ability to distinguish between the liquid plasma samples of breast cancer patients and healthy controls.

Monitoring Methodology for an AI Tool for Breast Cancer Screening Deployed in Clinical Centers.

We propose a methodology for monitoring an artificial intelligence (AI) tool for breast cancer screening when deployed in clinical centers. An AI trained to detect suspicious regions of interest in the four views of a mammogram and to characterize their level of suspicion with a score ranging from one (low suspicion) to ten (high suspicion of malignancy) was deployed in four radiological centers across the US. Results were collected between April 2021 and December 2022, resulting in a dataset of 36,581 AI records. To assess the behavior of the AI, its score distribution in each center was compared to a reference distribution obtained in silico using the Pearson correlation coefficient (PCC) between each center AI score distribution and the reference. The estimated PCCs were 0.998 [min: 0.993, max: 0.999] for center US-1, 0.975 [min: 0.923, max: 0.986] for US-2, 0.995 [min: 0.972, max: 0.998] for US-3 and 0.994 [min: 0.962, max: 0.982] for US-4. These values show that the AI behaved as expected. Low PCC values could be used to trigger an alert, which would facilitate the detection of software malfunctions. This methodology can help create new indicators to improve monitoring of software deployed in hospitals.

Look how far we have come: BREAST cancer detection education on the international stage.

The development of screening mammography over 30 years has remarkedly reduced breast cancer-associated mortality by 20%-30% through detection of small cancer lesions at early stages. Yet breast screening programmes may function differently in each nation depending on the incidence rate, national legislation, local health infrastructure and training opportunities including feedback on performance. Mammography has been the frontline breast cancer screening tool for several decades; however, it is estimated that there are 15% to 35% of cancers missed on screening which are owing to perceptual and decision-making errors by radiologists and other readers. Furthermore, mammography screening is not available in all countries and the increased speed in the number of new breast cancer cases among less developed countries exceeds that of the developed world in recent decades. Studies conducted through the BreastScreen Reader Assessment Strategy (BREAST) training tools for breast screening readers have documented benchmarking and significant variation in diagnostic performances in screening mammogram test sets in different countries. The performance of the radiologists from less well-established breast screening countries such as China, Mongolia and Vietnam were significant lower in detecting early-stage cancers than radiologists from developed countries such as Australia, USA, Singapore, Italy. Differences in breast features and cancer presentations, discrepancies in the level of experiences in reading screening mammograms, the availability of high-quality national breast screening program and breast image interpretation training courses between developed and less developed countries are likely to have impact on the variation of readers' performances. Hence dedicated education training programs with the ability to tailor to different reader cohorts and different population presentations are suggested to ameliorate challenges in exposure to a range of cancer cases and improve the interpretation skills of local radiologists. Findings from this review provide a good understanding of the radiologist' performances and their improvement using the education interventions, primarily the BREAST program, which has been deployed in a large range of developing and developed countries in the last decade. Self-testing and immediate feedback loops have been shown to have important implications for benchmarking and improving the diagnostic accuracy in radiology worldwide for better breast cancer control.

Integrating age, BMI, and serum N-glycans detected by MALDI mass spectrometry to classify suspicious mammogram findings as benign lesions or breast cancer

While mammograms are the standard tool for breast cancer screening, there remains challenges for mammography to effectively distinguish benign lesions from breast cancers, leading to many unnecessary biopsy procedures. A blood-based biomarker could provide a minimally invasive supplemental assay to increase the specificity of breast cancer screening. Serum N-glycosylation alterations have associations with many cancers and several of the clinical characteristics of breast cancer. The current study utilized a high-throughput mass spectrometry workflow to identify serum N-glycans with differences in intensities between patients that had a benign lesion from patients with breast cancer. The overall N-glycan profiles of the two patient groups had no differences, but there were several individual N-glycans with significant differences in intensities between patients with benign lesions and ductal carcinoma in situ (DCIS). Many N-glycans had strong associations with age and/or body mass index, but there were several of these associations that differed between the patients with benign lesions and breast cancer. Accordingly, the samples were stratified by the patient’s age and body mass index, and N-glycans with significant differences between these subsets were identified. For women aged 50–74 with a body mass index of 18.5–24.9, a model including the intensities of two N-glycans, 1850.666 m/z and 2163.743 m/z, age, and BMI were able to clearly distinguish the breast cancer patients from the patients with benign lesions with an AUROC of 0.899 and an optimal cutoff with 82% sensitivity and 84% specificity. This study indicates that serum N-glycan profiling is a promising approach for providing clarity for breast cancer screening, especially within the subset of healthy weight women in the age group recommended for mammograms.

Comparison of Mammography, Sonography, Fine-Needle Aspiration Cytology, and Excision Biopsy for the Diagnosis of Breast Lesions

Abstract Background and Objective : Problem of invasive tests in breast lesion diagnosis can be addressed by comparing non-invasive tests with final Histopathological diagnosis obtained after excision biopsy. Present study was carried out to study diagnostic utility of mammography, sonography, FNAC compared to excision biopsy for diagnosis of breast lesions Methods: Prospective Observational study was carried out among 81 women with Breast related symptoms. Digital Mammography Machine, ACUSON S3000™ Ultrasound System; FNAC and surgery for excision Biopsy were used. Sensitivity, Specificity, Accuracy, Positive and Negative Predictive values were measured and p<0.05 was considered as statistically significant. Results: Sonography and FNAC composite have sensitivity, specificity, accuracy, positive and negative predictive values 100.00%, 81.40%, 90.12%, 82.61% and 100.00% respectively. Mammography+FNAC (97.3%) is not as sensitive as Sonography+FNAC (100%), Sonography combined with FNAC is equally sensitive to excision biopsy (100%) and therefore can be used for screening purpose. Conclusion: Contrary to present guidelines which consider Mammography as most important screening tool for Breast Cancer, combination of Sonography and FNAC can be considered equally reliable.