Breast compression techniques in screening mammography – A Maltese evaluation project

Introduction: In screening mammography, the radiographer should be responsible for providing mammograms of high diagnostic value, possibly without subjecting clients to a painful experience. This skill is demonstrated via the technique of breast compression and is explored in this study by analysing insights about methods and underlying principles in regards to this procedure. Methods: One-to-one semi-structured interviews were conducted with radiographers who perform screening mammography in Malta. For data analysis, a descriptive phenomenological approach following a simplified version of Hycner's (1985) method was adopted. Results: Five general themes were extracted from the data; meeting the client, preparing the client, the mammography procedure, pain from compression and client turnout. It was determined that the participants alter their breast compression technique according to the client rather than following a rigid step-by-step process and that explanation and requesting client feedback are essential to obtain cooperation. Additionally, mammography positioning and compression application are tailored in a way that encourage compliance, however not at the expense of degrading image quality. Ultimately, it is also believed that a proper breast compression technique positively influences client turnout. Conclusion: The results of this study demonstrate that radiographers should be flexible in their approach in order to carry out a successful breast compression technique. However, it has also been shown that such effectiveness in practice is gained from experience rather than initial training. If exposed to this study's findings, new mammographers would be able to form a robust core of knowledge before embarking on the challenging specialisation of mammography

Breast Cancer : automatic detection and risk analysis through machine learning algorithms, using mammograms

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2021, Universidade de Lisboa, Faculdade de CiênciasCom 2.3 milhões de casos diagnosticados em todo o Mundo, durante o ano de 2020, o cancro da mama tornou-se aquele com maior incidência, nesse mesmo ano, considerando ambos os sexos. Anualmente, em Portugal, são diagnosticados aproximadamente sete mil (7000) novos casos de cancro da mama, com mil oitocentas (1800) mulheres a morrerem, todos os anos, devido a esta doença - indicando uma taxa de mortalidade de aproximadamente 5 mulheres por dia. A maior parte dos diagnósticos de cancro da mama ocorrem ao nível de programas de rastreio, que utilizam mamografia. Esta técnica de imagem apresenta alguns problemas: o facto de ser uma imagem a duas dimensões leva a que haja sobreposição de tecidos, o que pode mascarar a presença de tumores; e a fraca sensibilidade a mamas mais densas, sendo estas caraterísticas de mulheres com risco de cancro da mama mais elevado. Como estes dois problemas dificultam a leitura das mamografias, grande parte deste trabalhou focou-se na verificação do desempenho de métodos computacionais na tarefa de classificar mamografias em duas classes: cancro e não-cancro. No que diz respeito à classe “não cancro” (N = 159), esta foi constituída por mamografias saudáveis (N=84), e por mamografias que continham lesões benignas (N=75). Já a classe “cancro” continha apenas mamografias com lesões malignas (N = 73). A discriminação entre estas duas classes foi feita com recurso a algoritmos de aprendizagem automática. Múltiplos classificadores foram otimizados e treinados (Ntreino=162, Nteste = 70), recorrendo a um conjunto de características previamente selecionado, que descreve a textura de toda a mamografia, em vez de apenas uma única Região de Interesse. Estas características de textura baseiam-se na procura de padrões: sequências de pixéis com a mesma intensidade, ou pares específicos de pixéis. O classificador que apresentou uma performance mais elevada foi um dos Support Vector Machine (SVM) treinados – AUC= 0.875, o que indica um desempenho entre o bom e o excelente. A Percent Mammographic Density (%PD) é um importante fator de risco no que diz respeito ao desenvolvimento da doença, pelo que foi estudado se a sua adição ao set de features selecionado resultaria numa melhor performance dos classificadores. O classificador, treinado e otimizado utilizando as features de textura e os cálculos de %PD, com maior capacidade discriminativa foi um Linear Discriminant Analysis (LDA) – AUC = 0.875. Uma vez que a performance é igual à obtida com o classificador que utiliza apenas features de textura, conclui-se que a %PD parece não contribuir com informação relevante. Tal pode ocorrer porque as próprias características de textura já têm informação sobre a densidade da mama. De forma a estudar-se de que modo o desempenho destes métodos computacionais pode ser afetado por piores condições de aquisição de imagem, foi simulado ruído gaussiano, e adicionado ao set de imagens utilizado para testagem. Este ruído, adicionado a cada imagem com quatro magnitudes diferentes, resultou numa AUC de 0.765 para o valor mais baixo de ruído, e numa AUC de 0.5 para o valor de ruído mais elevado. Tais resultados indicam que, para níveis de ruído mais baixo, o classificador consegue, ainda assim, manter uma performance satisfatória – o que deixa de se verificar para valores mais elevados de ruído. Estudou-se, também, se a aplicação de técnicas de filtragem – com um filtro mediana – poderia ajudar a recuperar informação perdida aquando da adição de ruído. A aplicação do filtro a todas as imagens ruidosas resultou numa AUC de 0.754 para o valor mais elevado de ruído, atingindo assim um desempenho similar ao set de imagens menos ruidosas, antes do processo de filtragem (AUC=0.765). Este resultados parecem indicar que, na presença de más condições de aquisição, a aplicação de um filtro mediana pode ajudar a recuperar informação, conduzindo assim a um melhor desempenho dos métodos computacionais. No entanto, esta mesma conclusão parece não se verificar para valores de ruído mais baixo onde a AUC após filtragem acaba por ser mais reduzida. Tal resultado poderá indicar que, em situações onde o nível de ruído é mais baixo, a técnica de filtragem não só remove o ruído, como acaba também por, ela própria, remover informação ao nível da textura da imagem. De modo a verificar se mamas com diferentes densidades afetavam a performance do classificador, foram criados três sets de teste diferentes, cada um deles contendo imagens de mamas com a mesma densidade (1, 2, e 3). Os resultados obtidos indicam-nos que um aumento na densidade das mamas analisadas não resulta, necessariamente, numa diminuição da capacidade em discriminar as classes definidas (AUC = 0.864, AUC = 0.927, AUC= 0.905; para as classes 1, 2, e 3 respetivamente). A utilização da imagem integral para analisar de textura, e a utilização de imagens de datasets diferentes (com dimensões de imagem diferentes), poderiam introduzir um viés na classificação, especialmente no que diz respeito às diferentes áreas da mama. Para verificar isso mesmo, utilizando o coeficiente de correlação de Pearson, ρ = 0.3, verificou-se que a área da mama (e a percentagem de ocupação) tem uma fraca correlação com a classificação dada a cada imagem. A construção do classificador, para além de servir de base a todos os testes apresentados, serviu também o propósito de criar uma interface interativa, passível de ser utilizada como ficheiro executável, sem necessidade de instalação de nenhum software. Esta aplicação permite que o utilizador carregue imagens de mamografia, exclua background desnecessário para a análise da imagem, extraia features, teste o classificador construído e dê como output, no ecrã, a classe correspondente à imagem carregada. A análise de risco de desenvolvimento da doença foi conseguida através da análise visual da variação dos valores das features de textura ao longo dos anos para um pequeno set (N=11) de mulheres. Esta mesma análise permitiu descortinar aquilo que parece ser uma tendência apresentada apenas por mulheres doentes, na mamografia imediatamente anterior ao diagnóstico da doença. Todos os resultados obtidos são descritos profundamente ao longo deste documento, onde se faz, também, uma referência pormenorizada a todos os métodos utilizados para os obter. O resultado da classificação feita apenas com as features de textura encontra-se dentro dos valores referenciados no estado-da-arte, indicando que o uso de features de textura, por si só, demonstrou ser profícuo. Para além disso, tal resultado serve também de indicação que o recurso a toda a imagem de mamografia, sem o trabalho árduo de definição de uma Região de Interesse, poderá ser utilizado com relativa segurança. Os resultados provenientes da análise do efeito da densidade e da área da mama, dão também confiança no uso do classificador. A interface interativa que resultou desta primeira fase de trabalho tem, potencialmente, um diferenciado conjunto de aplicações: no campo médico, poderá servir de auxiliar de diagnóstico ao médico; já no campo da análise computacional, poderá servir para a definição da ground truth de potenciais datasets que não tenham legendas definidas. No que diz respeito à análise de risco, a utilização de um dataset de dimensões reduzidas permitiu, ainda assim, compreender que existem tendências nas variações das features ao longo dos anos, que são especificas de mulheres que desenvolveram a doença. Os resultados obtidos servem, então, de indicação que a continuação desta linha de trabalho, procurando avaliar/predizer o risco, deverá ser seguida, com recurso não só a datasets mais completos, como também a métodos computacionais de aprendizagem automática.Two million and three hundred thousand Breast Cancer (BC) cases were diagnosed in 2020, making it the type of cancer with the highest incidence that year, considering both sexes. Breast Cancer diagnosis usually occurs during screening programs using mammography, which has some downsides: the masking effect due to its 2-D nature, and its poor sensitivity concerning dense breasts. Since these issues result in difficulties reading mammograms, the main part of this work aimed to verify how a computer vision method would perform in classifying mammograms into two classes: cancer and non-cancer. The ‘non-cancer group’ (N=159) was composed by images with healthy tissue (N=84) and images with benign lesions (N=75), while the cancer group (N=73) contained malignant lesions. To achieve this, multiple classifiers were optimized and trained (Ntrain = 162, Ntest = 70) with a previously selected ideal sub-set of features that describe the texture of the entire image, instead of just one small Region of Interest (ROI). The classifier with the best performance was Support Vector Machine (SVM), (AUC = 0.875), which indicates a good-to-excellent capability discriminating the two defined groups. To assess if Percent Mammographic Density (%PD), an important risk factor, added important information, a new classifier was optimized and trained using the selected sub-set of texture features plus the %PD calculation. The classifier with the best performance was a Linear Discriminant Analysis (LDA), (AUC=0.875), which seems to indicate, once it achieves the same performance as the classifier using only texture features, that there is no relevant information added from %PD calculations. This happens because texture already includes information on breast density. To understand how the classifier would perform in worst image acquisition conditions, gaussian noise was added to the test images (N=70), with four different magnitudes (AUC= 0.765 for the lowest noise value vs. AUC ≈ 0.5 for the highest). A median filter was applied to the noised images towards evaluating if information could be recovered. For the highest noise value, after filtering, the AUC was very close to the one obtained for the lowest noise value before filtering (0.754 vs 0.765), which indicates information recovery. The effect of density in classifier performance was evaluated by constructing three different test sets, each containing images from a density class (1,2,3). It was seen that an increase in density did not necessarily resulted in a decrease in performance, which indicates that the classifier is robust to density variation (AUC = 0.864, AUC= 0.927, AUC= 0.905 ; for class 1, 2, and 3 respectively). Since the entire image is being analyzed, and images come from different datasets, it was verified if breast area was adding bias to classification. Pearson correlation coefficient provided an output of ρ = 0.22, showing that there is a weak correlation between these two variables. Finally, breast cancer risk was assessed by visual texture feature analysis through the years, for a small set of women (N=11). This visual analysis allowed to unveil what seems to be a pattern amongst women who developed the disease, in the mammogram immediately before diagnosis. The details of each phase, as well as the associated final results are deeply described throughout this document. The work done in the first classification task resulted in a state-of-the-art performance, which may serve as foundation for new research in the area, without the laborious work of ROI definition. Besides that, the use of texture features alone proved to be fruitful. Results concerning risk may serve as basis for future work in the area, with larger datasets and the incorporation of Computer Vision methods

Evaluation of image receptor angulation during mediolateral oblique positioning for optimised pressure and area distribution in mammography

BackgroundMammography is the gold standard diagnostic tool for the screening and diagnosis of breast cancer; however, it is associated with pain and discomfort. The pain and discomfort are mostly due to positioning and the compression applied during the procedure. Currently there are variations in the way clients are positioned for mammography and the amount of compression applied during the procedure. In addition, there are sparse guidelines and published literature on mammographic positioning and the application of compression. It is suggested that for the medio lateral oblique (MLO) position, for an effective compression force balance and increased breast footprint, the sternal angle and the image receptor (IR) be parallel to each other. This aim of this research is to evaluate the angle of IR during MLO positioning for optimised pressure and area distribution; this in turn may help reduce pain and discomfort associated with the procedure.MethodThe experimental work described in this report is in two phases. Phase one was an anthropomorphic phantom study to establish a structured and reproducible method of using the angle of the sternum to measure the correct angle of the IR for MLO projection. An inclinometer was used to measure the sternal angle of phantom model used. Six sets of compressions were made on the breast phantom with the IR at different angles ranging from 400 to 700 at 50 angle increments. Contact pressure and contact area footprint readings between breast phantom/paddle interface and breast phantom/IR interface were recorded using Xsensor pressure mapping system. Pressure uniformity (PU) and area uniformity (AU) between phantom breast/paddle interface and phantom breast phantom/IR interface were then calculated.Phase two was a human study with participants to investigate contact pressure and area balance on MLO compressions using two angles. A digital inclinometer was used to measure the angle at which the sternum for each participant. This angle was referred to as the ‘experimental angle’. The other angle was a ‘reference angle’ of 450. Compression at the ‘experimental angle’ may result into a better distribution of pressure through the breast and juxtathoracic structures, this may reduce the pain associated with the procedure. In addition to this, compression at this angle may increase breast surface area. The hypotheses set out to ascertain if there is no significant difference between contact pressure distribution when the IR is positioned parallel to the sternal angle (experimental angle) and it is positioned at a reference angle.An Xsensor pressure mapping system was used to record and analyse pressure distribution and surface area for compressions at the ‘experimental angle’ and the ‘reference angle’ (450). Pressure and area balance between the IR and compression paddle on both of these angles were compared and T-test conducted to accept or reject the hypotheses set out. In addition, participants were asked to score their pain experience after each compression, that is, compression at the ‘reference angle’ and the ‘experimental angle’. ResultsThe results from phase one indicated there was greater balance of pressure between breast/IR interface and breast/paddle interface at IR angle 600 compared the rest of IR angles investigated. PU of zero indicated equal distribution of pressure from the IR and the paddle. IR angled at 600 recorded a PU value of 0.21 which was the closest to zero from the PU recorded for the various angles. AU of zero indicates equal distribution of area footprint from the IR and the paddle. IR at 600 (Sternal angle for phantom model) produced the greatest area footprint balance compared to the other angles with AU of 0.05. An IR angled at 600, being parallel to the sternal angle of the phantom model which was recorded at 600 on the inclinometer, was the angle which produced the greatest balance of pressure and area footprint.The results from human study indicated there was no significant difference between contact pressure and area distribution when the IR is positioned parallel to the experimental angle or positioned at a reference angle.ConclusionFor the phantom study it has been shown that positioning the IR parallel to the angle of the sternum produces a more balanced contact pressure distribution and improved breast surface area footprint. The human study demonstrated no statistically significant difference between pressure and area balance on the reference angle and the experimental angle.For pain experienced score, although there was a 95% chance that the actual pain score for the compression on the reference angle fell within 3.81 and 5.76. and that of the experimental angle fell within 3.02 and 4.79, there was no statistically significant difference between pain experienced from compression on both angles

The impact of simulated motion blur on breast cancer detection performance in full field digital mammography (FFDM)

Objective: Full-field Digital Mammography (FFDM) is employed in breast screening for the early detection of breast cancer. High quality, artefact free, diagnostic images are crucial to the accuracy of this process. Unwanted motion during the image acquisition phase and subsequent image blurring is an unfortunate occurrence in some FFDM images. The research detailed in this thesis seeks to understand the impact of motion blur on cancer detection performance in FFDM images using novel software to perform simulation of motion, an observer study to measure the lesion detection performance and physical measures to assess the impact of simulated motion blur on image characteristics of the lesions. Method: Seven observers (15±5 years’ reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for three conditions: no motion blur (0.0 mm) and two magnitudes of simulated motion blur (0.7 mm and 1.5 mm). Abnormal cases were biopsy proven. A free-response observer study was conducted to compare lesion detection performance for the three conditions. Equally weighted jackknife alternative free-response receiver operating characteristic (wJAFROC) was used as the figure of merit. A secondary analysis of data was deemed important to simulate ‘double reporting’. In this secondary analysis, six of the observers are combined with the seventh observer to evaluate the impact of combined free-response data for lesion detection and to assess if combined two observers data could reduce the impact of simulated motion blur on detection performance. To compliment this, the physical characteristics of the lesions were obtained under the three conditions in order to assess any change in characteristics of the lesions when blur is present in the image. The impact of simulated motion blur on physical characteristics of malignant masses was assessed using a conspicuity index; for microcalcifications, a new novel metric, known as dispersion index, was used. Results: wJAFROC analysis found a statistically significant difference in lesion detection performance for both masses (F (2,22) = 6.01, P=0.0084) and microcalcifications (F(2,49) = 23.14, P<0.0001). For both lesion types, the figure of merit reduced as the magnitude of simulated motion blur increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0mm v 0.7mm, and 0.0mm v 1.5mm) and all pairs for microcalcifications (0.0 mm v 0.7 mm, 0.0 mm v 1.5 mm, and 0.7 mm v 1.5 mm). No difference was detected between 0.7 mm and 1.5 mm for masses. For combined two observers’ data of masses, there was no statistically significant difference between single and combined free-response data for masses (F(1,6) = 4.04, p=0.1001, -0.031 (-0.070, 0.008) [treatment difference (95% CI)]. For combined data of microcalcifications, there was a statistically significant difference between single and combined free-response data (F(1,6) = 12.28, p=0.0122, -0.056 (-0.095, -0.017) [treatment difference (95% CI)]. Regarding the physical measures of masses, conspicuity index increases as the magnitude of simulated motion blur increases. Statistically significant differences were demonstrated for 0.0–0.7 mm t(22)=-6.158 (p<0.000); 0.0–1.5 mm t(22)=-6.273 (p<0.000); and 0.7–1.5 mm (t(22)=-6.231 (p<0.000). Lesion edge angle decreases as the magnitude of simulated motion blur increases. Statistically significant differences were demonstrated for 0.0–0.7 mm t(22)=3.232 (p<0.004); for 0.0–1.5 mm t(22)=6.592 (p<0.000); and 0.7–1.5mm t(22)=2.234 (p<0.036). For the grey level change there was no statistically significant difference as simulated motion blur increases to 0.7 and then to 1.5mm. For image noise there was a statistically significant difference, where noise reduced as simulated motion blur increased: 0.0–0.7 mm t(22)=22.95 (p<0.000); 0.0–1.5mm t(22)=24.66 (p<0.000); 0.7–1.5 mm t(22)=18.11 (p<0.000). For microcalcifications, simulated motion blur had a negative impact on the ‘dispersion index’. Conclusion: Mathematical simulations of motion blur resulted in a statistically significant reduction in lesion detection performance. This reduction in performance could have implications for clinical practice. Simulated motion blur has a negative impact on the edge angle of breast masses and a negative impact on the image characteristics of microcalcifications. These changes in the image lesion characteristics appear to have a negative effect on the visual identification of breast cancer