The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study.

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35-45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: -0.16, 0.28). There was little association with dense area (between-women r = -0.12, 95%CI: -0.38, 0.16; within-women r = 0.01, 95%CI: -0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: -0.31 (95%CI: -0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size

Rendszermodellezés mérési adatokból, hibrid-neurális megközelítés = System modelling from measurement data: hybrid-neural approach

A kutatás célja mérési adatok alapján történő rendszermodellezési eljárások kidolgozása és vizsgálata volt, különös tekintettel a nemlineáris rendszerek modellezésére. A kutatás során többféle megközelítést alkalmaztunk: egyrészt a rendszermodellezési feladatok megoldásánál a lineáris rendszerekre kidolgozott eljárásokból indultunk ki nemlineáris hatásokat is figyelembe véve, másrészt fekete doboz megközelítéseket alkalmaztunk, ahol elsődlegesen input-output adatokból történik a modell konstrukció. Az előbbi megközelítés különösen gyengén nemlineáris rendszerek modellezésénél tűnik járható útnak, ahol a gyengén nemlineáris rendszereket, mint nemlineárisan torzított lineáris rendszereket tekintjük. A nemlineáris torzítások hatásának megértésére egy teljes elméletet dolgoztunk ki. A fekete doboz modellezésnél általános modell-struktúrákból indulunk ki, melyek paramétereit a rendelkezésre álló mérési adatok felhasználásával, tanulással határozhatjuk meg. Ekkor az alapvető kérdések a megfelelő kiinduló adatbázis kialakítására és az adatokkal kapcsolatos problémákra (zajos adatok, kiugró adatok, inkonzisztens adatok, redundáns adatok, stb.) irányultak, továbbá arra hogy hogyan lehet a fekete doboz modellstruktúra komplexitását kézben tartani és az adatokon túl meglévő egyéb információ hatékony figyelembevételét biztosítani. A fekete doboz modellezésnél neuronhálókat és szupport vektor gépeket vettünk figyelembe és a minél kisebb modell-komplexitás elérésére törekedtünk. | The goal of the research was to develop and analyse system modelling procedures, especially for modelling non-linear systems. To reach the goal different approaches were applied. One approach is to use procedures developed for linear system modelling, where nonlinear effects are taken into consideration. The other approach applied is black box modelling, where model-construction is mainly based on input-output data. The first approach proved to be successful especially for the modelling of weakly non-linear systems, where these systems are considered as linear ones with the presence of nonlinear distortion. To understand nonlinear distortions a whole theory has been developed. For black box modelling the starting point was the use of certain general model-structures, where the parameters of these structures are determined by training using measurement data. The most relevant questions in this case are related to the construction of data base, and the problems of quality of the available data (noisy data, missing data, outliers, inconsistent data, redundant data, etc.), A further important goal was to find proper ways to utilise additional knowledge and at the same time to reduce model complexity. For black box modelling some special neural network architectures and support vector machines were considered

The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-18, pub-electronic 2021-06-29Publication status: PublishedFunder: Cancer Research UK; Grant(s): C569/A16891, IS-BRC-1215-20007We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35–45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: −0.16, 0.28). There was little association with dense area (between-women r = −0.12, 95%CI: −0.38, 0.16; within-women r = 0.01, 95%CI: −0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: −0.31 (95%CI: −0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size

Compression forces used in the Norwegian Breast Cancer Screening Program

Objectives: Compression is used in mammography to reduce breast thickness, which is claimed to improve image quality and reduce radiation dose. In the Norwegian Breast Cancer Screening Program (NBCSP), the recommended range of compression force for full field digital mammography is 11-18 kg (108-177 Newton [N]). This is the first study to investigate the compression force used in the program. Methods: The study included information from 17,951 randomly selected women screened with FFDM at 14 breast centres in the NBCSP, January-March 2014. We investigated the applied compression force on left breast in craniocaudal (CC) and mediolateral oblique (MLO) view for breast centres, mammography machines within the breast centres and for the radiographers. Results: The mean compression force for all mammograms in the study was 116N and ranged from 91 to 147N between the breast centres. The variation in compression force was wider between the breast centres than between mammography machines (range 137-155N) and radiographers (95-143N) within one breast centre. Approximately 59% of the mammograms in the study complied with the recommended range of compression force. Conclusions: A wide variation in applied compression force was observed between the breast centres in the NBCSP. This variation indicates a need for evidence-based recommendations for compression force aimed at optimizing the image quality and individualising breast compression. Advances in knowledge: There was a wide variation in applied compression force between the breast centres in the NBCSP. The variation was wider between the breast centres than between mammography machines and radiographers within one breast centre

A comparison of five methods of measuring mammographic density: a case-control study.

BACKGROUND: High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. METHODS: Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. RESULTS: The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05). CONCLUSIONS: Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification

Practitioner compression force variability in mammography : a preliminary study

Objective: This preliminary study determines whether the absolute amount of breast compression in mammography varies between and within practitioners. Methods: Ethics approval was granted. 488 clients met the inclusion criteria. Clients were imaged by 14 practitioners. Collated data included Breast Imaging Reporting and Data System (BI-RADS) density, breast volume, compression and practitioner code. Results: A highly significant difference in mean compression used by different practitioners (p,0.0001 for each BI-RADS density) was demonstrated. Practitioners applied compression in one of three ways using either low, intermediate or high compression force, with no significant difference in mean compression within each group (p50.99, p50.70, p50.54, respectively). Six practitioners showed a significant correlation (p,0.05) between compression and BI-RADS grade, with a tendency to apply less compression with increasing BI-RADS density. When compression was analysed by breast volume there was a wide variation in compression for a given volume. The general trend was the application of higher compression to larger breast volumes by all three practitioner groups. Conclusion: This study presents an insight into practitioner variation of compression application in mammography. Three groups of practitioners were identified: those who used low, intermediate and high compression across the BI-RADS density grades. There was wide variation in compression for any given breast volume, with trends of higher compression demonstrated for increasing breast volumes. Collation of further studies will facilitate a new perspective on the analysis of practitioner, client and equipment variables in mammography imaging. Advances in knowledge: For the first time, it has been practically demonstrated that practitioners vary in the amount of compression applied to breast tissue during routine mammography

Imaging performance of phase-contrast breast computed tomography with synchrotron radiation and a CdTe photon-counting detector

Purpose: Within the SYRMA-CT collaboration based at the ELETTRA synchrotron radiation (SR) facility (Trieste, Italy) the authors investigated the imaging performance of the phase-contrast computed tomography (CT) system dedicated to monochromatic in vivo 3D imaging of the female breast, for breast cancer diagnosis. Methods: Test objects were imaged at 38 keV using monochromatic SR and a high-resolution CdTe photon-counting detector. Signal and noise performance were evaluated using modulation transfer function (MTF) and Noise Power Spectrum (NPS). Phase-contrast CT images as well as images obtained after the application of a phase-retrieval algorithm were evaluated. The contrast to noise ratio (CNR) and the capability of detecting test microcalcification clusters and soft masses were explored. Results: For a voxel size of (60 \u3bcm)3, phase-contrast images showed higher spatial resolution (6.7 mm-1 at 10% MTF) than corresponding phase retrieval images (2.5 mm-1). Phase retrieval produced a reduction of the noise level as well as an increase of the CNR by more than one order of magnitude, compared to raw phase-contrast images. CaCO3 microcalcifications with a diameter down to 130 \u3bcm were detected both in phase-contrast and in phase retrieval images of the test object. Conclusions: The investigation on test objects indicates that breast CT with a monochromatic SR source is technically feasible in terms of spatial resolution, image noise and contrast, for in vivo 3D imaging with a dose comparable to that of two-view mammography. Phase-retrieved images showed the best performance in the trade-off between spatial resolution and image noise

Practitioner variation of applied breast compression force in mammography

Rationale:Mammography practitioners control the amount of compression force applied to the breast. There are no quantifiable recommendations for optimal compression force levels for practitioners to follow. Clients report variations in pain and discomfort when compression force is applied. Until now practitioner compression force variability has not been investigated; even though this might lead to variations in client pain and discomfort. The primary purpose of this thesis was to investigate whether practitioner compression force variability exists.Method:Three research papers investigated practitioner compression force variability: one used a cross sectional design; two used longitudinal designs, one was single centre and the other was multicentre. Three further research papers investigated important issues which might confound practitioner variability results: the first investigated compression paddle bend and distortion; the second investigated how breast thickness and compression force vary; the third evaluated practitioner ability to grade breast density, visually. The final research paper was a ‘within client’ investigation to determine how image quality varied with breast thickness and compression force. Key findings:The research firmly demonstrates that practitioner compression force variability exists. Multicentre analysis (4500 client visits) confirmed two out of three screening sites with significant practitioner variability, with the third screening site having a minimum dictate of compression force at 100N. As displayed by MLO/CC projections clients underwent a 55%/57% (site one), 66%/60% (site two) and 27%/26% (site three) change in compression force through their three screening visits. The research confirmed that the compression force received by a client was highly dependent upon the practitioner, and not the client. Within an individual clients screening pathway the research has demonstrated that clients could receive significantly different compression force levels over time. Conclusion and further research:For the first time practitioner compression force variability has been identified. Novel methods for reducing breast thickness need investigating; an example of a novel method is the use of pressure rather than force