Calidad sobre la Información de Salud y Cáncer en Internet.

Internet es una fuente de información que cada día es utilizada por miles de personas para consultar temas de salud, y especialmente, sobre cáncer. La Conselleria de Sanitat a través del"Plan oncológico de la Comunitat Valenciana 20072010" orienta y define la política sanitaria frente al cáncer en nuestro territorio durante este periodo. Entre sus ejes de actuación básica se encuentra el apoyo al desarrollo continuado del Sistema de Información sobre Cáncer. Esta información es de gran importancia a la hora de conseguir una participación informada de la población en la toma de decisiones que afectan a su salud, contribuyendo además, a su alfabetización digital en salud, objetivo prioritario de la UNESCO para el desarrollo de los todos los países. El problema es que esta información se presenta en tal cantidad que valorar la calidad de la misma llega a ser un problema para quienes buscan y reclaman herramientas que les ayuden a seleccionar estos contenidos. Con el fin de proporcionar a la población herramientas con las que alcanzar este objetivo se presenta el siguiente informe en el que se muestra el diseño y elaboración de una Guía de Ayuda a la Lectura para Información sobre Cáncer para la población

Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

[EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080Pérez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Pollán, M.; Perez-Gómez, B.; Salas-Trejo, D.; Casals, M.... (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine. 177:123-132. https://doi.org/10.1016/j.cmpb.2019.05.022S12313217

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.This research was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed by nomination to Valencian technological innovation centres under project expedient IMDEEA/2021/100. It was also supported by grants from Instituto de Salud Carlos III FEDER (PI17/00047).S

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

[EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080.Perez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Fuster Bagetto, A.; Pollan, M.; Pérez-Gómez, B.; Salas-Trejo, D.... (2020). A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Computer Methods and Programs in Biomedicine. 195:123-132. https://doi.org/10.1016/j.cmpb.2020.105668S123132195Kuhl, C. K. (2015). The Changing World of Breast Cancer. 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Ecology, 26(3), 297-302. doi:10.2307/1932409Pollán, M., Llobet, R., Miranda-García, J., Antón, J., Casals, M., Martínez, I., … Salas-Trejo, D. (2013). Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-242Llobet, R., Pollán, M., Antón, J., Miranda-García, J., Casals, M., Martínez, I., … Pérez-Cortés, J.-C. (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine, 116(2), 105-115. doi:10.1016/j.cmpb.2014.01.021He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., & Chao, Y. (2017). The connected-component labeling problem: A review of state-of-the-art algorithms. Pattern Recognition, 70, 25-43. doi:10.1016/j.patcog.2017.04.018Wu, K., Otoo, E., & Suzuki, K. (2008). Optimizing two-pass connected-component labeling algorithms. 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Desigualdades de acceso a los programas de cribado del cáncer en España y cómo reducirlas: datos de 2013 y 2020

Fundamentos: La Comisión Europea recomienda asegurar la equidad en el cribado del cáncer. El objetivo de este estudio fue conocer si existían desigualdades en el acceso a los programas de cribado del cáncer en España. Métodos: Se realizó un estudio transversal mediante encuesta dirigida a las personas responsables de los programas de cribado del cáncer de mama, colorrectal (CCR) y cérvix de las diecinueve Comunidades Autónomas (CCAA) del Estado Español en 2013 y 2020. Se recogió información sobre características organizativas, desigualdades de acceso e intervenciones para reducirlas. Se hizo un análisis descriptivo por CCAA y periodo temporal, mediante el cálculo de frecuencias y porcentajes, en función del tipo de programa (mama, CCR y cérvix). Resultados: En 2013 participaron catorce CCAA para el programa de mama, ocho para el de CCR y siete para el de cérvix, y en 2020, catorce, trece y once CCAA, respectivamente. Todos los programas de mama eran poblacionales en ambos periodos (14/14 en 2013 y 14/14 en 2020), así como los de CCR (8/8 en 2013 y 13/13 en 2020), con un aumento en el caso de los programas de cribado del cáncer de cérvix (0/7 en 2013 y 6/11 en 2020). Se identificaron en ambos periodos grupos sociales no incluidos en la población diana y grupos que, estando incluidos, participaban menos, con diferencias según el tipo de programa. Se realizaron un total de cincuenta y tres intervenciones para reducir desigualdades en el acceso (veintisiete en mama, veintidós en CCR y cuatro en cérvix), el 66% de ellas dirigidas a grupos sociales específicos (35/53). Conclusiones: Se identifican desigualdades de acceso a los programas de cribado del cáncer en España, así como intervenciones para reducirlas

Reproducibility of data-driven dietary patterns in two groups of adult Spanish women from different studies

The objective of the present study was to assess the reproducibility of data-driven dietary patterns in different samples extracted from similar populations. Dietary patterns were extracted by applying principal component analyses to the dietary information collected from a sample of 3550 women recruited from seven screening centres belonging to the Spanish breast cancer (BC) screening network (Determinants of Mammographic Density in Spain (DDM-Spain) study). The resulting patterns were compared with three dietary patterns obtained from a previous Spanish case-control study on female BC (Epidemiological study of the Spanish group for breast cancer research (GEICAM: grupo Español de investigación en cáncer de mama)) using the dietary intake data of 973 healthy participants. The level of agreement between patterns was determined using both the congruence coefficient (CC) between the pattern loadings (considering patterns with a CC≥0·85 as fairly similar) and the linear correlation between patterns scores (considering as fairly similar those patterns with a statistically significant correlation). The conclusions reached with both methods were compared. This is the first study exploring the reproducibility of data-driven patterns from two studies and the first using the CC to determine pattern similarity. We were able to reproduce the EpiGEICAM Western pattern in the DDM-Spain sample (CC=0·90). However, the reproducibility of the Prudent (CC=0·76) and Mediterranean (CC=0·77) patterns was not as good. The linear correlation between pattern scores was statistically significant in all cases, highlighting its arbitrariness for determining pattern similarity. We conclude that the reproducibility of widely prevalent dietary patterns is better than the reproducibility of more population-specific patterns. More methodological studies are needed to establish an objective measurement and threshold to determine pattern similarity.This study was supported by Carlos III Institute of Health FIS(Spanish Public Health Research Fund: PI060386 FIS; PS09/00790 and PI15CIII/0029 research grants), the Spanish Ministryof Health (EC11-273), the Spanish Ministry of Economyand Competitiveness (IJCI-2014-20900), the Spanish Federationof Breast Cancer Patients (FECMA: EPY 1169-10) and theAssociation of Women with Breast Cancer from Elche (AMAC-MEC: EPY 1394/15). None of the funders had any role in thedesign, analysis or writing of this article.V.L.,N.A.,B.P.-G.andM.P.designedthestudy;A.C.,J.V.,C.S.,C.P.-P.,S.A.,M.E.,D.S.-T.,C.V.andC.S.-C.collectedthedataand/or prepared the database. A. C. performed statistical analysisand wrote the initial version of the manuscript that M. P. revised andcorrected in its different versions. All the authors have read andapproved thefinal version of the manuscript.The authors declare that there are no conflicts of interest.S

Association Between Western and Mediterranean Dietary Patterns and Mammographic Density

OBJECTIVE: To examine the association between two dietary patterns (Western and Mediterranean), previously linked to breast cancer risk, and mammographic density. METHODS: This cross-sectional study included 3,584 women attending population-based breast cancer screening programs and recruited between October 7, 2007, and July 14, 2008 (participation rate 74.5%). Collected data included anthropometric measurements; demographic, obstetric, and gynecologic characteristics; family and personal health history; and diet in the preceding year. Mammographic density was blindly assessed by a single radiologist and classified into four categories: less than 10%, 10-25%, 25-50%, and greater than 50%. The association between adherence to either a Western or a Mediterranean dietary pattern and mammographic density was explored using multivariable ordinal logistic regression models with random center-specific intercepts. Models were adjusted for age, body mass index, parity, menopause, smoking, family history, hormonal treatment, and calorie and alcohol intake. Differences according to women's characteristics were tested including interaction terms. RESULTS: Women with a higher adherence to the Western dietary pattern were more likely to have high mammographic density (n=242 [27%]) than women with low adherence (n=169 [19%]) with a fully adjusted odds ratio (ORQ4vsQ1) of 1.25 (95% confidence interval [CI] 1.03-1.52). This association was confined to overweight-obese women (adjusted ORQ4vsQ1 [95% CI] 1.41 [1.13-1.76]). No association between Mediterranean dietary pattern and mammographic density was observed. CONCLUSION: The Western dietary pattern was associated with increased mammographic density among overweight-obese women. Our results might inform specific dietary recommendations for women with high mammographic density.S

High mammographic density in long-term night shift workers: DDM-Spain /Var-DDM

[EN] Background: Night-shift work (NSW) has been suggested as a possible cause of breast cancer, and its association with mammographic density (MD), one of the strongest risk factors for breast cancer, has been scarcely addressed. This study examined NSW and MD in Spanish women. Methods: The study covered 2,752 women aged 45-68 years recruited in 2007-2008 in 7 population-based public breast cancer screening centers, which included 243 women who had performed NSW for at least one year. Occupational data and information on potential confounders were collected by personal interview. Two trained radiologist estimated the percentage of MD assisted by a validated semiautomatic computer tool (DM-scan). Multivariable mixed linear regression models with random screening center-specific intercepts were fitted using log-transformed percentage of MD as the dependent variable and adjusting by known confounding variables. Results: Having ever worked in NSW was not associated with MD [e(beta):0.96; 95% confidence interval (CI), 0.86-1.06]. However, the adjusted geometric mean of the percentage of MD in women with NSW for more than 15 years was 25% higher than that of those without NSW history (MD>15 (years):20.7% vs. MDnever:16.5%; e(beta):1.25; 95% CI, 1.01-1.54). This association was mainly observed in postmenopausal participants (e(beta):1.28; 95% CI, 1.00-1.64). Among NSW-exposed women, those with <= 2 night-shifts per week had higher MD than those with 5 to 7 nightshifts per week (e(beta):1.42; 95% CI, 1.10-1.84). Conclusions: Performing NSW was associated with higherMD only in women with more than 15 years of cumulated exposure. These findings warrant replication in futures studies. (C)2017 AACR.We would like to thank the participants in the DDM-Spain/Var-DDM-Spain study for their contribution to breast cancer research. Other members of DDM-Spain/Var-DDM: Gonzalo. López-Abente, Roberto Pastor-Barriuso, Pablo Fernández-Navarro, Anna Cabanes, Soledad Laso, Manuela Alcaraz, María Casals, Inmaculada Martínez, Juan Carlos Pérez Cortés, Joaquín Antón, Nieves Ascunce, Isabel González-Román, Ana Belén Fernández, Montserrat Corujo, Soledad Abad, and Jesús Vioque. A.M. Pedraza-Flechas FI14CIII/00013 PFIS; B. Perez-Gomez FIS PS09/0790; M. Pollán FIS PI060386, EPY1306/06 collaboration agreement between Astra-Zeneca and ISCIII, and FECMA 485 EPY 1170 10; R. LLobet Gent per Gent Fund (EDEMAC Project); All authors: European Regional Development Fund (FEDER). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.Pedraza-Flechas, AM.; Lope, V.; Sanchez-Contador, C.; Santamarina, C.; Pedraz-Pingarron, C.; Moreo, P.; Ederra, M.... (2017). High mammographic density in long-term night shift workers: DDM-Spain /Var-DDM. Cancer Epidemiology Biomarkers & Prevention. 26(6):905-913. https://doi.org/10.1158/1055-9965.EPI-16-0507S90591326

Sleep patterns, sleep disorders and mammographic density in spanish women: The DDM-Spain/Var-DDM study

[EN] We explored the relationship between sleep patterns and sleep disorders and mammographic density (MD), a marker of breast cancer risk. Participants in the DDM-Spain/var-DDM study, which included 2878 middle-aged Spanish women, were interviewed via telephone and asked questions on sleep characteristics. Two radiologists assessed MD in their left craneo-caudal mammogram, assisted by a validated semiautomatic-computer tool (DM-scan). We used log-transformed percentage MD as the dependent variable and fitted mixed linear regression models, including known confounding variables. Our results showed that neither sleeping patterns nor sleep disorders were associated with MD. However, women with frequent changes in their bedtime due to anxiety or depression had higher MD (e¿:1.53;95%CI:1.04¿2.26).This work was supported by grants from the Spanish Ministry of Economy and Competitiveness - Carlos III Institute of Health (ISCIII) (FI14CIII/00013, FIS PI060386 & PS09/0790), from the Spanish Federation of Breast Cancer Patients (FECMA 485 EPY 1170-10), Gent per Gent Fund (EDEMAC Project), the EPY1306/06 collaboration agreement between Astra-Zeneca and the ISCIII and partially funded by the European Regional Development Fund (FEDER)Pedraza-Flechas, AM.; Lope, V.; Moreo, P.; Ascunce, N.; Miranda-García, J.; Vidal, C.; Sánchez-Contador, C.... (2017). Sleep patterns, sleep disorders and mammographic density in spanish women: The DDM-Spain/Var-DDM study. Maturitas. 99:105-108. https://doi.org/10.1016/j.maturitas.2017.02.015S1051089