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

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This work was supported by research grants from Gent per Gent Fund (EDEMAC Project); Spain's Health Research Fund (Fondo de Investigacion Santiaria) (PI060386 & FIS PS09/00790); Spanish MICINN grants TIN2009-14205-C04-02 and Consolider-Ingenio 2010: MIPRCV (CSD2007-00018); Spanish Federation of Breast Cancer Patients (Federacion Espanola de Cancer de Mama) (FECMA 485 EPY 1170-10). The English revision of this paper was funded by the Universitat Politecnica de Valencia, Spain.Llobet Azpitarte, R.; Pollán, M.; Antón Guirao, J.; Miranda-García, J.; Casals El Busto, M.; Martinez Gomez, I.; Ruiz Perales, F.... (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine. 116(2):105-115. https://doi.org/10.1016/j.cmpb.2014.01.021S105115116

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

Mammographic breast density: comparison of methods for quantitative evaluation.

PURPOSE: To evaluate the results from two software tools for measurement of mammographic breast density and compare them with observer-based scores in a large cohort of women. MATERIALS AND METHODS: Following written informed consent, a data set of 36 281 mammograms from 8867 women were collected from six United Kingdom centers in an ethically approved trial. Breast density was assessed by one of 26 readers on a visual analog scale and with two automated density tools. Mean differences were calculated as the mean of all the individual percentage differences between each measurement for each case (woman). Agreement in total breast volume, fibroglandular volume, and percentage density was assessed with the Bland-Altman method. Association with observer's scores was calculated by using the Pearson correlation coefficient (r). RESULTS: Correlation between the Quantra and Volpara outputs for total breast volume was r = 0.97 (P < .001), with a mean difference of 43.5 cm(3) for all cases representing 5.0% of the mean total breast volume. Correlation of the two measures was lower for fibroglandular volume (r = 0.86, P < .001). The mean difference was 30.3 cm(3) for all cases representing 21.2% of the mean fibroglandular tissue volume result. Quantra gave the larger value and the difference tended to increase with volume. For the two measures of percentage volume density, the mean difference was 1.61 percentage points (r = 0.78, P < .001). Comparison of observer's scores with the area-based density given by Quantra yielded a low correlation (r = 0.55, P < .001). Correlations of observer's scores with the volumetric density results gave r values of 0.60 (P < .001) and 0.63 (P < .001) for Quantra and Volpara, respectively. CONCLUSION: Automated techniques for measuring breast density show good correlation, but these are poorly correlated with observer's scores. However automated techniques do give different results that should be considered when informing patient personalized imaging. (©) RSNA, 2015 Clinical trial registration no. ISRCTN 73467396.Supported by the National Institute for Health Research’s Health Technology Assessment Programme.This is the final version of the article. It first appeared at http://pubs.rsna.org/doi/full/10.1148/radiol.1414150

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

Purpose: Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation, however the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. Methods: Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased to simulate measurement error. Increments of 1mm were used up to ±15% error of recorded CBT was achieved. New images were created for each 1mm step in thickness resulting in a total of 974 images which then had Volpara Density Grade (VDG) and volumetric density percentage assigned. Results: A change in VDG was recorded in 38.5% (n= 20) of mammograms when applying ±15% error to the recorded CBT and 11.5 % (n= 6) were within the QC standard prescribed error of ±5mm. Conclusion: The current QC standard of ±5mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered