Aportaciones al diagnóstico de cáncer asistido por ordenador

Para diagnosticar un cáncer se realiza, entre otras pruebas, algún test de imagen, como puede ser una radiografía, ecografía o resonancia magnética. Mediante estos tests pueden detectarse zonas con alta sospecha tumoral, cuyo diagnóstico debe confirmase finalmente mediante la realización de una biopsia. Este tipo de imágenes, sin embargo, no son fáciles de interpretar, lo que provoca que el profesional encargado de analizarlas, a pesar de su experiencia, no sea capaz de detectar en ellas un porcentaje importante de tumores (falsos negativos). Una posibilidad para mejorar el diagnóstico y disminuir el número de falsos negativos consiste en utilizar sistemas de diagnóstico asistido por ordenador o computer-aided diagnosis (CAD). Un sistema de CAD analiza la imagen médica y trata de detectar zonas sospechosas de contener alguna anomalía. Estas zonas son marcadas sobre la propia imagen con un doble objetivo: llamar la atención del profesional encargado de analizarla hacia la zona sospechosa y aportar una segunda opinión respecto al diagnóstico. En esta tesis se presentan y evaluan diversas técnicas de visión por computador y reconocimiento de formas orientadas a la detección de tumores en imágenes médicas, con el objetivo de diseñar sistemas de CAD que permitan un mejor diagnóstico. El trabajo se ha centrado en el diagnóstico de cáncer de próstata a partir de imágenes de ecografía, y en el diagnóstico de cáncer de mama a partir de imágenes de radiografía. Se han evaluado diversos métodos de extracción de características basados en la intensidad, frecuencia, texturas o en gradientes. En la etapa de clasificación se ha utilizado un clasificador no paramétrico basado en distancias (k-vecinos más cercanos) y otro paramétrico basado en modelos de Markov. A lo largo del trabajo se evidencian las distintas problemáticas que surgen en este tipode tareas y se proponen soluciones a cada una de ellas. El diagnóstico de cáncer de próstata asistido por ordenador es una tarea extremaLlobet Azpitarte, R. (2006). Aportaciones al diagnóstico de cáncer asistido por ordenador [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1862Palanci

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

[EN] The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.This work was supported in part by the Generalitat Valenciana through the Valencian Institute of Business Competitiveness (IVACE) Distributed Nominatively to Valencian Technological Innovation Centers under Project IMAMCN/2021/1, in part by the Cervera Network of Excellence Project in Data-Based Enabling Technologies (AI4ES) Co-Funded by the Centre for Industrial and Technological Development¿E. P. E. (CDTI), and in part by the European Union through the Next Generation EU Fund within the Cervera Aids Program for Technological Centers under Project CER-20211030.Ortiz, V.; Pérez-Benito, FJ.; Del Tejo Catalá, O.; Salvador Igual, I.; Llobet Azpitarte, R.; Perez-Cortes, J. (2022). Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks. IEEE Access. 10:120074-120085. https://doi.org/10.1109/ACCESS.2022.32219361200741200851

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

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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K., Yuan, Y., Scheckel, C., … Troyanskaya, O. G. (2019). Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nature Genetics, 51(6), 973-980. doi:10.1038/s41588-019-0420-0Kallenberg, M., Petersen, K., Nielsen, M., Ng, A. Y., Diao, P., Igel, C., … Lillholm, M. (2016). Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Transactions on Medical Imaging, 35(5), 1322-1331. doi:10.1109/tmi.2016.2532122Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, Overfeat: integrated recognition, localization and detection using convolutional networks, arXiv:1312.6229 (2013).Dice, L. R. (1945). Measures of the Amount of Ecologic Association Between Species. 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. Pattern Analysis and Applications, 12(2), 117-135. doi:10.1007/s10044-008-0109-yShen, R., Yan, K., Xiao, F., Chang, J., Jiang, C., & Zhou, K. (2018). Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection. Journal of Digital Imaging, 31(5), 680-691. doi:10.1007/s10278-018-0068-9Yin, K., Yan, S., Song, C., & Zheng, B. (2018). A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms. International Journal of Computer Assisted Radiology and Surgery, 14(2), 237-248. doi:10.1007/s11548-018-1867-7James, J. . (2004). The current status of digital mammography. Clinical Radiology, 59(1), 1-10. doi:10.1016/j.crad.2003.08.011Sáez, C., Robles, M., & García-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Jain, A. K. (2010). 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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

Overeating, caloric restriction and mammographic density in Spanish women. DDM-Spain study

[EN] Objectives: Mammographic density (MD) is a strong risk factor for breast cancer. The present study evaluates the association between relative caloric intake and MD in Spanish women. Study design: We conducted a cross-sectional study in which 3517 women were recruited from seven breast cancer screening centers. MD was measured by an experienced radiologist using craniocaudal mammography and Boyd's semi-quantitative scale. Information was collected through an epidemiological survey. Predicted calories were calculated using linear regression models, including the basal metabolic rate and physical activity as explanatory variables. Overeating and caloric restriction were defined taking into account the 99% confidence interval of the predicted value. Odds ratios (OR) and 95% confidence intervals (95%CI) were estimated using center-specific mixed ordinal logistic regression models, adjusted for age, menopausal status, body mass index, parity, tobacco use, family history of breast cancer, previous biopsies, age at menarche and adherence to a Western diet. Main outcome measure: Mammographic density. Results: Those women with an excessive caloric intake ( > 40% above predicted) presented higher MD (OR = 1.41, 95%CI = 0.97-2.03; p = 0.070). For every 20% increase in relative caloric consumption the probability of having higher MD increased by 5% (OR = 1.05, 95%CI = 0.98-1.14; p = 0.178), not observing differences between the categories of explanatory variables. Caloric restriction was not associated with MD in our study. Conclusions: This is the first study exploring the association between MD and the effect of caloric deficit or excessive caloric consumption according to the energy requirements of each woman. Although caloric restriction does not seem to affect breast density, a caloric intake above predicted levels seems to increase this phenotypeThis study was supported by the Research Grant FIS PI060386 from Spanish Public Health Research Fund (Fondo de Investigacion Sanitaria); the Carlos III Institute of Health (Institute de Salud Carlos III)PI15CIII/0029 and PI15CIII/00013; the EPY 1306/06Collaboration Agreement between Astra-Zeneca and the Carlos III Institute of Health; and a grant from the Spanish Federation of Breast Cancer Patients (FECMA EPY 1169/10).Del Pozo, MDP.; Castelló, A.; Vidal, C.; Salas -Trejo, D.; Sanchez Contador, C.; Pedraz-Pingarrón, C.; Moreno, MP.... (2018). Overeating, caloric restriction and mammographic density in Spanish women. DDM-Spain study. Maturitas. 117:57-63. https://doi.org/10.1016/j.maturitas.2018.09.006S576311

Semiautomatic estimation of breast density with DM-Scan software

[EN] Objective To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. Material and methods The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreement between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. Results For visual inspection, the mean interobserver agreement was 0,876 (95% CI: 0,873-0,879) on the Boyd scale and 0,823 (95% CI: 0,818-0,829) on the BI-RADS® scale. The mean intraobserver agreement was 0,813 (95% CI: 0,796-0,829) on the Boyd scale and 0,770 (95% CI: 0,742-0,797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0,92, considerably higher than the agreement for visual inspection. Conclusion The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density.[ES] Objetivo Evaluar la reproducibilidad del cálculo de la densidad mamaria con la aplicación informática DM-Scan, basada en la segmentación semiautomática del tejido fibroglandular, y compararla con la de la inspección visual. Material y métodos El estudio incluyó 655 mamografías digitales directas en proyección cráneo-caudal. Tres expertos radiólogos analizaron la densidad de las mamografías con DM-Scan, y se calcularon las concordancias inter e intraobservador entre pares de radiólogos para las escalas Boyd y BI-RADS®, utilizando el índice de correlación intraclase. Las concordancias se compararon con las obtenidas previamente para la inspección visual, en el mismo conjunto de imágenes, utilizando el índice Kappa. Resultados Con el análisis visual, la concordancia media interobservador fue de 0,876 (IC 95%: 0,873-0,879) para la escala de Boyd, y 0,823 (IC 95%: 0,818-0,829) para la clasificación BI-RADS®. La concordancia intraobservador fue de 0,813 (IC 95%: 0,796-0,829) para la escala de Boyd, y 0,770 (IC 95%: 0,742-0,797) para la clasificación BI-RADS®. Con DM-Scan, la concordancia media inter e intraobservador fue de 0,92, notablemente superior a las concordancias de la clasificación visual. Conclusión El cálculo de la densidad mamaria con la aplicación semiautomática DM-Scan es más fiable y reproducible, y disminuye la subjetividad y variabilidad de la estimación visual.Martinez Gomez, I.; Casals El Busto, M.; Antón Guirao, J.; Ruiz Perales, F.; Llobet Azpitarte, R. (2014). Estimación semiautomática de la densidad mamaria con DM-Scan. Radiología. 56(5):429-434. https://doi.org/10.1016/j.rx.2012.11.007S42943456

Wonen's features and inter/intra rater agreement on mammographic density assesment in full-field digital mammograms

Measurement of mammographic density (MD), one of the leading risk factors for breast cancer, still relies on subjective assessment. However, the consistency of MD measurement in full-digital mammograms has yet to be evaluated. We studied inter- and intra-rater agreement with respect to estimation of breast density in full-digital mammograms, and tested whether any of the women’s characteristics might have some influence on them. After an initial training period, three experienced radiologists estimated MD using Boyd scale in a left breast craniocaudal mammogram of 1,431 women, recruited at three Spanish screening centres. A subgroup of 50 randomly selected images was read twice to estimate short-term intra-rater agreement. In addition, a reading of 1,428 of the images, performed 2 years before by one rater, was used to estimate long-term intra-rater agreement. Pair-wise weighted kappas with 95% bootstrap confidence intervals were calculated. Dichotomous variables were defined to identify mammograms in which any rater disagreed with other raters or with his/her own assessment, respectively. The association between disagreement and women’s characteristics was tested using multivariate mixed logistic models, including centre as a random-effects term, and taking into account repeated measures when required. All quadratic-weighted kappa values for inter- and intra-rater agreement were excellent (higher than 0.80). None of the studied women’s features, i.e. body mass index, brassiere size, menopause, nulliparity, lactation or current hormonal therapy, was associated with higher risk of inter- or intrarater disagreement. However, raters differed significantly more in images that were classified in the higher-density MD categories, and disagreement in intra-rater assessment was also lower in low-density mammograms. The reliability of MD assessment in full-field digital mammograms is comparable to that for original or digitised images. The reassuring lack of association between subjects’ MD-related characteristics and agreement suggests that bias from this source is unlikely.This study was supported by research grants from Fundacion Gent per Gent (EDEMAC Project); grants FIS PI09/1230 & PI060386 from Spain's Health Research Fund (Fondo de Investigacion Sanitaria); the EPY 1306/06 Collaboration Agreement between Astra-Zeneca and the Carlos III Institute of Health (Instituto de Salud Carlos III); and a grant from the Spanish Federation of Breast Cancer Patients (FECMA). We thank the participants of the study DDM-Spain for their contribution to breast cancer research. We wish also to acknowledge the collaboration from other DDM-Spain members: Pilar Moreo, Pilar Moreno and Soledad Abad (Aragon); Francisca Collado and Magdalena Moya (Baleares); Isabel Gonzalez, Carmen Pedraz and Francisco Casanova (Castilla-Leon); Merce Peris (Cataluna); Carmen Santamarina, Jose Antonio Vazquez Carrete, Montserrat Corujo and Ana Belen Fernandez (Galicia); Nieves Ascunce, Maria Ederra, Milagros Garcia and Ana Barcos (Navarra); Manuela Alcaraz, Jesus Vioque (C. Valenciana); Virginia Lope, Nuria Aragones, Anna Cabanes (Madrid).Perez-Gomez, B.; Ruiz, F.; Martinez, I.; Casals, M.; Miranda, J.; Sanchez-Contador, C.; Vidal, C.... (2012). Wonen's features and inter/intra rater agreement on mammographic density assesment in full-field digital mammograms. 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Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients

[EN] Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.This work was supported by Generalitat Valenciana through the "Instituto Valenciano de Competitividad Empresarial-IVACE'' under Grant IMDEEA/2020/69.Omar del Tejo Catalá; Salvador Igual, I.; Perez-Benito, FJ.; Millan-Escriva, D.; Ortiz, V.; Llobet Azpitarte, R.; Perez-Cortes, J. (2021). Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients. IEEE Access. 9:42370-42383. https://doi.org/10.1109/ACCESS.2021.3065456S4237042383