1,586 research outputs found
Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?
Aim
To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates.
Materials and methods
The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient.
Results
In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud
Conclusion
This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management
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Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.
BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection
Digital mammography, cancer screening: Factors important for image compression
The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers
INbreast: Toward a Full-field Digital Mammographic Database
Rationale and Objectives
Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database.
Materials and Methods
Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used.
Results
The new database—INbreast—has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format.
Conclusion
The strengths of the actually presented database—INbreast—relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging
Implementation of medical imaging with telemedicine for the early detection and diagnoses of breast cancer to women in remote areas
Nowadays, the cancer topic has become a global concern. Furthermore, breast cancer persists to be the top leading cause of death to women population and the second cause of cancer death after the lung cancer globally. Various technologies and techniques have been searched, developed and studied over the years to detect the disease at the early stage; the early diagnosis saves many lives in both developed and developing countries. The detection of cancer through a screening process before its symptoms emerge increases the survival rate dramatically (Li, Meaney and Paulsen). Moreover, sufficient knowledge of the disease, qualified staff, accurate, appropriate treatment and diagnosis contribute to the successful cure of the disease; however, the cancer treatment is not affordable by many and sometimes not available to the very needy, and more precisely in developing countries. In this research, we aimed to explore the early detection of breast cancer using the new image compression algorithm: DYNAMAC, a compression tool that finds its basis in nonlinear dynamical systems theory; we implemented this algorithm through the D-transform, a digital sequence used to compress the digital media (Wang and Huang) & (Antoine, Murenzi and Vandergheynst). The goal is to use this method to analyze the average profile of diseased and healthy breast images obtained from a digital mammography to detect diseased tissues. After the detection of cancerous tumors, we worked to establish a remote care to women victims of breast cancer using the Telecommunication infrastructure through primarily Teleradiology and the Next Generation Internet (NGI) technology. Over the methods and techniques previously used in the area of medical imaging techniques, DYNAMAC algorithm is the most easily implemented along with its features that include cost saving in addition to best meeting the requirements of the breast imaging technology
Avaliação comparativa entre a mamografia digital e mamografia em filme: revisão sistemática e metanálise
CONTEXT AND OBJECTIVE: Mammography is the best method for breast-cancer screening and is capable of reducing mortality rates. Studies that have assessed the clinical impact of mammography have been carried out using film mammography. Digital mammography has been proposed as a substitute for film mammography given the benefits inherent to digital technology. The aim of this study was to compare the performance of digital and film mammography. DESIGN: Systematic review and meta-analysis. METHOD: The Medline, Scopus, Embase and Lilacs databases were searched looking for paired studies, cohorts and randomized controlled trials published up to 2009 that compared the performance of digital and film mammography, with regard to cancer detection, recall rates and tumor characteristics. The reference lists of included studies were checked for any relevant citations. RESULTS: A total of 11 studies involving 190,322 digital and 638,348 film mammography images were included. The cancer detection rates were significantly higher for digital mammography than for film mammography (risk relative, RR = 1.17; 95% confidence interval, CI = 1.06-1.29; I² = 19%). The advantage of digital mammography seemed greatest among patients between 50 and 60 years of age. There were no significant differences between the two methods regarding patient recall rates or the characteristics of the tumors detected. CONCLUSION: The cancer detection rates using digital mammography are slightly higher than the rates using film mammography. There are no significant differences in recall rates between film and digital mammography. The characteristics of the tumors are similar in patients undergoing the two methods.CONTEXTO E OBJETIVO: A mamografia é o melhor método para rastreamento do câncer de mama, capaz de reduzir a mortalidade. Os estudos que avaliam seu impacto clínico foram realizados com mamografia em filme. A mamografia digital é proposta para substituir a mamografia em filme com benefícios inerentes à tecnologia digital. O objetivo do estudo foi comparar o desempenho da mamografia digital com a mamografia em filme. TIPO DE ESTUDO: Revisão sistemática e metanálise. MÉTODO: Foram pesquisadas as bases Medline, Scopus, Embase e Lilacs, buscando-se por estudos pareados, coortes e ensaios clínicos randomizados comparando a mamografia digital e a mamografia em filme, quanto à taxa de detecção de câncer, de reconvocação e características dos tumores, publicados até 2009. As referências dos estudos incluídos foram verificadas em busca de citações relevantes. RESULTADOS: Foi incluído um total de 11 estudos, somando 190.322 mamografias digitais e 638.348 em filme. A taxa de detecção do câncer pela mamografia digital foi significantemente maior (risco relativo, RR: 1,17 [95% intervalo de confiança, IC = 1,06-1,29 I² = 19%]) do que pela mamografia em filme. A vantagem da mamografia digital parece maior em pacientes entre 50 e 60 anos. Não houve diferenças significantes nas taxas de reconvocação de pacientes e nas características dos tumores encontrados. CONCLUSÃO: A mamografia digital apresenta taxa de detecção de câncer pouco maior que a mamografia em filme. Não há diferenças significantes nas taxas de reconvocação entre a mamografia digital e a em filme. As características dos tumores são semelhantes em pacientes em ambos os métodos.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal de São Paulo (UNIFESP) Department of Diagnostic ImagingUniversidade Federal de São Paulo (UNIFESP)Universidade Federal de São Paulo (UNIFESP) Department of MedicineUNIFESP, Department of Diagnostic ImagingUNIFESP, Department of MedicineSciEL
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