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

Prognostic Implications Of Patients With Mammographically Occult, Early Stage Breast Cancer

Purpose: To compare mammographically occult (MamOcc) and mammographically positive (MamPos) early-stage breast cancer patients treated with breast-conservation therapy (BCT), to analyze differences between the two cohorts. Methods: The 2 cohorts were comprised of 214 MamOcc and 2168 MamPos patients treated with BCT. Chart reviews were conducted to assess mammogram reports and method of detection. All clinical-pathologic and outcome parameters were analyzed to detect differences between the two cohorts. Results: Median follow-up was 7 years. There were no differences in final margins, T stage, nodal status, estrogen/progesterone receptor status, or triple-negative status. Significant differences included age at diagnosis (p \u3c 0.0001), more positive family history (p = 0.0033), less HER-2+ disease (p = 0.0294), and 1° histology (p \u3c 0.0001). At 10 years, the differences in overall survival, cause-specific survival, and distant relapse between the two groups did not differ significantly. The MamOcc cohort had more breast relapses (15% vs. 8%; p = 0.0357), but on multivariate analysis this difference was not significant (hazard ratio 1.0, 95% confidence interval 0.993-1.007, p = 0.9296). Breast relapses were more commonly not picked up on mammography in the MamOcc cohort (32% 12% p = 0.0136). Conclusions: Our study suggests that there are clinical-pathologic variations for the MamOcc cohort vs. MamPos patients that may potentially affect management, but that breast relapse rates after BCT are ultimately not significantly different for these 2 cohorts. Breast recurrences were more often mammographically occult in the MamOcc cohort; consideration should be given to closer follow-up and alternative imaging strategies (ultrasound, breast MRI) for routine post-treatment examination. To our knowledge, this represents the largest series addressing the prognostic significance of MamOcc cancers treated with BCT

Measurement challenge : protocol for international case–control comparison of mammographic measures that predict breast cancer risk

Introduction: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. Methods and analysis: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. Ethics and dissemination: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3)

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density

Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer

Analyzing the breast tissue in mammograms using deep learning

La densitat mamogràfica de la mama (MBD) reflecteix la quantitat d'àrea fibroglandular del teixit mamari que apareix blanca i brillant a les mamografies, comunament coneguda com a densitat percentual de la mama (PD%). El MBD és un factor de risc per al càncer de mama i un factor de risc per emmascarar tumors. Tot i això, l'estimació precisa de la DMO amb avaluació visual continua sent un repte a causa del contrast feble i de les variacions significatives en els teixits grassos de fons en les mamografies. A més, la interpretació correcta de les imatges de mamografia requereix experts mèdics altament capacitats: És difícil, laboriós, car i propens a errors. No obstant això, el teixit mamari dens pot dificultar la identificació del càncer de mama i associar-se amb un risc més gran de càncer de mama. Per exemple, s'ha informat que les dones amb una alta densitat mamària en comparació amb les dones amb una densitat mamària baixa tenen un risc de quatre a sis vegades més gran de desenvolupar la malaltia. La clau principal de la computació de densitat de mama i la classificació de densitat de mama és detectar correctament els teixits densos a les imatges mamogràfiques. S'han proposat molts mètodes per estimar la densitat mamària; no obstant això, la majoria no estan automatitzats. A més, s'han vist greument afectats per la baixa relació senyal-soroll i la variabilitat de la densitat en aparença i textura. Seria més útil tenir un sistema de diagnòstic assistit per ordinador (CAD) per ajudar el metge a analitzar-lo i diagnosticar-lo automàticament. El desenvolupament actual de mètodes daprenentatge profund ens motiva a millorar els sistemes actuals danàlisi de densitat mamària. L'enfocament principal de la present tesi és desenvolupar un sistema per automatitzar l'anàlisi de densitat de la mama ( tal com; Segmentació de densitat de mama (BDS), percentatge de densitat de mama (BDP) i classificació de densitat de mama (BDC) ), utilitzant tècniques d'aprenentatge profund i aplicant-la a les mamografies temporals després del tractament per analitzar els canvis de densitat de mama per trobar un pacient perillós i sospitós.La densidad mamográfica de la mama (MBD) refleja la cantidad de área fibroglandular del tejido mamario que aparece blanca y brillante en las mamografías, comúnmente conocida como densidad porcentual de la mama (PD%). El MBD es un factor de riesgo para el cáncer de mama y un factor de riesgo para enmascarar tumores. Sin embargo, la estimación precisa de la DMO con evaluación visual sigue siendo un reto debido al contraste débil y a las variaciones significativas en los tejidos grasos de fondo en las mamografías. Además, la interpretación correcta de las imágenes de mamografía requiere de expertos médicos altamente capacitados: Es difícil, laborioso, caro y propenso a errores. Sin embargo, el tejido mamario denso puede dificultar la identificación del cáncer de mama y asociarse con un mayor riesgo de cáncer de mama. Por ejemplo, se ha informado que las mujeres con una alta densidad mamaria en comparación con las mujeres con una densidad mamaria baja tienen un riesgo de cuatro a seis veces mayor de desarrollar la enfermedad. La clave principal de la computación de densidad de mama y la clasificación de densidad de mama es detectar correctamente los tejidos densos en las imágenes mamográficas. Se han propuesto muchos métodos para la estimación de la densidad mamaria; sin embargo, la mayoría de ellos no están automatizados. Además, se han visto gravemente afectados por la baja relación señal-ruido y la variabilidad de la densidad en apariencia y textura. Sería más útil disponer de un sistema de diagnóstico asistido por ordenador (CAD) para ayudar al médico a analizarlo y diagnosticarlo automáticamente. El desarrollo actual de métodos de aprendizaje profundo nos motiva a mejorar los sistemas actuales de análisis de densidad mamaria. El enfoque principal de la presente tesis es desarrollar un sistema para automatizar el análisis de densidad de la mama ( tal como; Segmentación de densidad de mama (BDS), porcentaje de densidad de mama (BDP) y clasificación de densidad de mama (BDC)), utilizando técnicas de aprendizaje profundo y aplicándola en las mamografías temporales después del tratamiento para analizar los cambios de densidad de mama para encontrar un paciente peligroso y sospechoso.Mammographic breast density (MBD) reflects the amount of fibroglandular breast tissue area that appears white and bright on mammograms, commonly referred to as breast percent density (PD%). MBD is a risk factor for breast cancer and a risk factor for masking tumors. However, accurate MBD estimation with visual assessment is still a challenge due to faint contrast and significant variations in background fatty tissues in mammograms. In addition, correctly interpreting mammogram images requires highly trained medical experts: it is difficult, time-consuming, expensive, and error-prone. Nevertheless, dense breast tissue can make it harder to identify breast cancer and be associated with an increased risk of breast cancer. For example, it has been reported that women with a high breast density compared to women with a low breast density have a four- to six-fold increased risk of developing the disease. The primary key of breast density computing and breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; however, most are not automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. It would be more helpful to have a computer-aided diagnosis (CAD) system to assist the doctor analyze and diagnosing it automatically. Current development in deep learning methods motivates us to improve current breast density analysis systems. The main focus of the present thesis is to develop a system for automating the breast density analysis ( such as; breast density segmentation(BDS), breast density percentage (BDP), and breast density classification ( BDC)), using deep learning techniques and applying it on the temporal mammograms after treatment for analyzing the breast density changes to find a risky and suspicious patient

The association of mammographic density with risk of contralateral breast cancer and change in density with treatment in the WECARE study.

BACKGROUND: Mammographic density (MD) is an established predictor of risk of a first breast cancer, but the relationship of MD to contralateral breast cancer (CBC) risk is not clear, including the roles of age, mammogram timing, and change with treatment. Multivariable prediction models for CBC risk are needed and MD could contribute to these. METHODS: We conducted a case-control study of MD and CBC risk in phase II of the WECARE study where cases had a CBC diagnosed ≥ 2 years after first diagnosis at age <55 years and controls had unilateral breast cancer (UBC) with similar follow-up time. We retrieved film mammograms of the unaffected breast from two time points, prior to/at the time of the first diagnosis (253 CBC cases, 269 UBC controls) and ≥ 6 months up to 48 months following the first diagnosis (333 CBC cases, 377 UBC controls). Mammograms were digitized and percent MD (%MD) was measured using the thresholding program Cumulus. Odds ratios (OR) and 95% confidence intervals (CI) for association between %MD and CBC, adjusted for age, treatment, and other factors related to CBC, were estimated using logistic regression. Linear regression was used to estimate the association between treatment modality and change in %MD in 467 women with mammograms at both time points. RESULTS: For %MD assessed following diagnosis, there was a statistically significant trend of increasing CBC with increasing %MD (p = 0.03). Lower density (<25%) was associated with reduced risk of CBC compared to 25 to < 50% density (OR 0.69, 95% CI 0.49, 0.98). Similar, but weaker, associations were noted for %MD measurements prior to/at diagnosis. The relationship appeared strongest in women aged < 45 years and non-existent in women aged 50 to 54 years. A decrease of ≥ 10% in %MD between first and second mammogram was associated marginally with reduced risk of CBC (OR 0.63, 95% CI 0.40, 1.01) compared to change of <10%. Both tamoxifen and chemotherapy were associated with statistically significant 3% decreases in %MD (p < 0.01). CONCLUSIONS: Post-diagnosis measures of %MD may be useful to include in CBC risk prediction models with consideration of age at diagnosis. Chemotherapy is associated with reductions in %MD, similar to tamoxifen

Visually assessed breast density, breast cancer risk and the importance of the craniocaudal view

Contains fulltext : 69403.pdf (publisher's version ) (Open Access)INTRODUCTION: Mammographic density is known to be a strong risk factor for breast cancer. A particularly strong association with risk has been observed when density is measured using interactive threshold software. This, however, is a labour-intensive process for large-scale studies. METHODS: Our aim was to determine the performance of visually assessed percent breast density as an indicator of breast cancer risk. We compared the effect on risk of density as measured with the mediolateral oblique view only versus that estimated as the average density from the mediolateral oblique view and the craniocaudal view. Density was assessed using a visual analogue scale in 10,048 screening mammograms, including 311 breast cancer cases diagnosed at that screening episode or within the following 6 years. RESULTS: Where only the mediolateral oblique view was available, there was a modest effect of breast density on risk with an odds ratio for the 76% to 100% density relative to 0% to 25% of 1.51 (95% confidence interval 0.71 to 3.18). When two views were available, there was a considerably stronger association, with the corresponding odds ratio being 6.77 (95% confidence interval 2.75 to 16.67). CONCLUSION: This indicates that a substantial amount of information on risk from percentage breast density is contained in the second view. It also suggests that visually assessed breast density has predictive potential for breast cancer risk comparable to that of density measured using the interactive threshold software when two views are available. This observation needs to be confirmed by studies applying the different measurement methods to the same individuals

Clinical and epidemiological issues and applications of mammographic density

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMammographic density, the amount of radiodense tissue on a mammogram, is a strong risk factor for breast cancer, with properties that could be an asset in screening and prevention programmes. Its use in risk prediction contexts is currently limited, however, mainly due to di culties in measuring and interpreting density. This research investigates rstly, the properties of density as an independent marker of breast cancer risk and secondly, how density should be measured. The rst question was addressed by analysing data from a chemoprevention trial, a trial of hormonal treatment, and a cohort study of women with a family history of breast cancer . Tamoxifen-induced density reduction was observed to be a good predictor of breast cancer risk reduction in high-risk una ected subjects. Density and its changes did not predict risk or treatment outcome in subjects with a primary invasive breast tumour. Finally absolute density predicted risk better than percent density and showed a potential to improve existing risk-prediction models, even in a population at enhanced familial risk of breast cancer. The second part of thesis focuses on density measurement and in particular evaluates two fully-automated volumetric methods, Quantra and Volpara. These two methods are highly correlated and in both cases absolute density (cm3) discriminated cases from controls better than percent density. Finally, we evaluated and compared di erent measurement methods. Our ndings suggested good reliability of the Cumulus and visual assessments. Quantra volumetric estimates appeared negligibly a ected by measurement error, but were less variable than visual bi-dimensional ones, a ecting their ability to discriminate cases from controls. Overall, visual assessments showed the strongest association with breast cancer risk in comparison to computerised methods. Our research supports the hypothesis that density should have a role in personalising screening programs and risk management. Volumetric density measuring methods, though promising, could be improved.Cancer Research U

First steps in optimizing breast screening in Mongolia: Understanding radiologists’ performance in reading mammograms and mammographic breast density

Advanced diagnoses of breast cancer have become a serious public health issue in Mongolia. Whilst mammography has been proven to be an effective screening approach for breast cancer and well established amongst developed countries, such program has not been introduced in Mongolia. In addition in Mongolia, a lack of research around breast cancer continues to exist. The purpose of this thesis is to understand mammographic diagnostic accuracy and mammographic breast density (MD) in Mongolia, both of which are important considerations, which will inform a future national screening program. To address this aim, three studies were conducted; the first two were radiologists’ performance studies in reading mammograms with different levels of difficulty. The mammographic detection of Mongolian radiologists (case sensitivity of 63% and lesion sensitivity of 34%) was substantially lower compared with that of Australian radiologists. The third study investigated the MD features of 1985 Mongolian women using the Breast Imaging Reporting and Data system (BI-RADS) density categories. The majority of women (58%) were found to have low-density categories (category A and B) and significant associations were observed between MD; age (OR = 6.8, 95% CI: 5.5, 8.0), weight (OR = 4.5, 95% CI: 3.4, 6.0) and BMI (OR=13.2, 95% CI: 8.6, 20.0). Findings from this research have demonstrated that mammographic diagnostic accuracy is sub-optimal in Mongolia. Moreover, images with different levels of difficulty did not alter the reading performance of Mongolian radiologists suggesting the need for improving breast cancer detection skills urgently. The output of this work also demonstrated that low density was predominant in Mongolia. The results will impact on health policy around screening in Mongolia. They will inform educational strategies that are needed to transform diagnostic efficacy and will provide a good basis for decision making around screening modality choices