12 research outputs found
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. Investigative Radiology, 50(9), 615-628. doi:10.1097/rli.0000000000000166Boyd, N. F., Rommens, J. M., Vogt, K., Lee, V., Hopper, J. L., Yaffe, M. J., & Paterson, A. D. (2005). Mammographic breast density as an intermediate phenotype for breast cancer. The Lancet Oncology, 6(10), 798-808. doi:10.1016/s1470-2045(05)70390-9Assi, V., Warwick, J., Cuzick, J., & Duffy, S. W. (2011). Clinical and epidemiological issues in mammographic density. Nature Reviews Clinical Oncology, 9(1), 33-40. doi:10.1038/nrclinonc.2011.173Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E., & Zwiggelaar, R. (2008). A Novel Breast Tissue Density Classification Methodology. IEEE Transactions on Information Technology in Biomedicine, 12(1), 55-65. doi:10.1109/titb.2007.903514Pérez-Benito, F. J., Signol, F., Pérez-Cortés, J.-C., Pollán, M., Pérez-Gómez, B., Salas-Trejo, D., … LLobet, R. (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. doi:10.1016/j.cmpb.2019.05.022Ciatto, S., Houssami, N., Apruzzese, A., Bassetti, E., Brancato, B., Carozzi, F., … Scorsolini, A. (2005). Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. The Breast, 14(4), 269-275. doi:10.1016/j.breast.2004.12.004Skaane, P. (2009). Studies comparing screen-film mammography and full-field digital mammography in breast cancer screening: Updated review. Acta Radiologica, 50(1), 3-14. doi:10.1080/02841850802563269Van der Waal, D., den Heeten, G. J., Pijnappel, R. M., Schuur, K. H., Timmers, J. M. H., Verbeek, A. L. M., & Broeders, M. J. M. (2015). Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting. PLOS ONE, 10(9), e0136667. doi:10.1371/journal.pone.0136667Kim, S. H., Lee, E. H., Jun, J. K., Kim, Y. M., Chang, Y.-W., … Lee, J. H. (2019). Interpretive Performance and Inter-Observer Agreement on Digital Mammography Test Sets. Korean Journal of Radiology, 20(2), 218. doi:10.3348/kjr.2018.0193Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246. doi:10.1093/bib/bbx044LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., … Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), 82-97. doi:10.1109/msp.2012.2205597Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3-11. doi:10.1016/j.patrec.2018.02.010Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168-174. doi:10.1038/nature12346Lee, K., Turner, N., Macrina, T., Wu, J., Lu, R., & Seung, H. S. (2019). Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy. Current Opinion in Neurobiology, 55, 188-198. doi:10.1016/j.conb.2019.04.001Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), i121-i129. doi:10.1093/bioinformatics/btu277Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. 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). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666. doi:10.1016/j.patrec.2009.09.011Lee, J., & Nishikawa, R. M. (2018). Automated mammographic breast density estimation using a fully convolutional network. Medical Physics, 45(3), 1178-1190. doi:10.1002/mp.12763D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, arXiv:1412.6980 (2014).Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019). Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology, 290(1), 52-58. doi:10.1148/radiol.2018180694Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. doi:10.1109/tpami.2013.50Wu, G., Kim, M., Wang, Q., Munsell, B. C., & Shen, D. (2016). Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning. IEEE Transactions on Biomedical Engineering, 63(7), 1505-1516. doi:10.1109/tbme.2015.2496253T.P. Matthews, S. Singh, B. Mombourquette, J. Su, M.P. Shah, S. Pedemonte, A. Long, D. Maffit, J. Gurney, R.M. Hoil, et al., A multi-site study of a breast density deep learning model for full-field digital mammography and digital breast tomosynthesis exams, arXiv:2001.08383 (2020)
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. Breast Cancer Research and Treatment. 132(1):287-295. https://doi.org/10.1007/s10549-011-1833-3S2872951321Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, Yaffe MJ (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236Stone J, Dite GS, Gunasekara A, English DR, McCredie MR, Giles GG, Cawson JN, Hegele RA, Chiarelli AM, Yaffe MJ, Boyd NF, Hopper JL (2006) The heritability of mammographically dense and nondense breast tissue. Cancer Epidemiol Biomarkers Prev 15:612–617Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MR, Giles GG, Tritchler D, Chiarelli A, Yaffe MJ, Hopper JL (2002) Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med 347:886–894El Bastawissi AY, White E, Mandelson MT, Taplin S (2001) Variation in mammographic breast density by race. Ann Epidemiol 11:257–263El Bastawissi AY, White E, Mandelson MT, Taplin SH (2000) Reproductive and hormonal factors associated with mammographic breast density by age (United States). Cancer Causes Control 11:955–963Vacek PM, Geller BM (2004) A prospective study of breast cancer risk using routine mammographic breast density measurements. Cancer Epidemiol Biomarkers Prev 13:715–722Kelemen LE, Pankratz VS, Sellers TA, Brandt KR, Wang A, Janney C, Fredericksen ZS, Cerhan JR, Vachon CM (2008) Age-specific trends in mammographic density: the Minnesota Breast Cancer Family Study. Am J Epidemiol 167:1027–1036Cuzick J, Warwick J, Pinney E, Warren RM, Duffy SW (2004) Tamoxifen and breast density in women at increased risk of breast cancer. J Natl Cancer Inst 96:621–628Boyd N, Martin L, Stone J, Little L, Minkin S, Yaffe M (2002) A longitudinal study of the effects of menopause on mammographic features. Cancer Epidemiol Biomarkers Prev 11:1048–1053Stone J, Gunasekara A, Martin LJ, Yaffe M, Minkin S, Boyd NF (2003) The detection of change in mammographic density. Cancer Epidemiol Biomarkers Prev 12:625–630McCormack VA, dos SS I, De Stavola BL, Perry N, Vinnicombe S, Swerdlow AJ, Hardy R, Kuh D (2003) Life-course body size and perimenopausal mammographic parenchymal patterns in the MRC 1946 British birth cohort. Br J Cancer 89:852–859American College of Radiology (1993) Breast imaging reporting and data system (BIRADS). American College of Radiology, Reston, VaBoyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, Lockwood GA, Tritchler DL, Yaffe MJ (1995) Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst 87:670–675Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ (1994) The quantitative analysis of mammographic densities. Phys Med Biol 39:1629–1638Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, Catarzi S, Lamberini MP, Marcelli G, Pellizzoni R, Pesce B, Risso G, Russo F, Scorsolini A (2005) Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. Breast 14:269–275Gao J, Warren R, Warren-Forward H, Forbes JF (2008) Reproducibility of visual assessment on mammographic density. Breast Cancer Res Treat 108:121–127Ooms EA, Zonderland HM, Eijkemans MJ, Kriege M, Mahdavian DB, Burger CW, Ansink AC (2007) Mammography: interobserver variability in breast density assessment. Breast 16:568–576Tagliafico A, Tagliafico G, Tosto S, Chiesa F, Martinoli C, Derchi LE, Calabrese M (2009) Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one. Breast 18:35–40Lee-Han H, Cooke G, Boyd NF (1995) Quantitative evaluation of mammographic densities: a comparison of methods of assessment. Eur J Cancer Prev 4:285–292Nicholson BT, LoRusso AP, Smolkin M, Bovbjerg VE, Petroni GR, Harvey JA (2006) Accuracy of assigned BI-RADS breast density category definitions. Acad Radiol 13:1143–1149Berg WA, Campassi C, Langenberg P, Sexton MJ (2000) Breast imaging reporting and data system: inter- and intraobserver variability in feature analysis and final assessment. AJR Am J Roentgenol 174:1769–1777Benichou J, Byrne C, Capece LA, Carroll LE, Hurt-Mullen K, Pee DY, Salane M, Schairer C, Gail MH (2003) Secular stability and reliability of measurements of the percentage of dense tissue on mammograms. Cancer Detect Prev 27:266–274Jong R, Fishell E, Little L, Lockwood G, Boyd NF (1996) Mammographic signs of potential relevance to breast cancer risk: the agreement of radiologists’ classification. Eur J Cancer Prev 5:281–286Kerlikowske K, Grady D, Barclay J, Frankel SD, Ominsky SH, Sickles EA, Ernster V (1998) Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System. J Natl Cancer Inst 90:1801–1809Antonio AL, Crespi CM (2010) Predictors of interobserver agreement in breast imaging using the Breast Imaging Reporting and Data System. Breast Cancer Res Treat 120:539–546Garrido-Estepa M, Ruiz-Perales F, Miranda J, Ascunce N, Gonzalez-Roman I, Sanchez-Contador C, Santamarina C, Moreo P, Vidal C, Peris M, Moreno MP, Vaquez-Carrete JA, Collado-Garcia F, Casanova F, Ederra M, Salas D, Pollan M (2010) Evaluation of mammographic density patterns: reproducibility and concordance among scales. BMC Cancer 10:485Cabanes A, Pastor-Barriuso R, Garcia-Lopez M, Pedraz-Pingarron C, Sanchez-Contador C, Vazquez Carrete JA, Moreno MP, Vidal C, Salas D, Miranda-Garcia J, Peris M, Moreo P, Santamarina MC, Collado-Garcia F, Gonzalez-Roman I, Ascunce N, Pollan M (2011) Alcohol, tobacco, and mammographic density: a population-based study. Breast Cancer Res Treat 129(1):135–147Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20:37–46Cohen J (1968) Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 70:213–220Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, CambridgeCaldwell CB, Stapleton SJ, Holdsworth DW, Jong RA, Weiser WJ, Cooke G, Yaffe MJ (1990) Characterisation of mammographic parenchymal pattern by fractal dimension. Phys Med Biol 35:235–247Jeffreys M, Warren R, Smith GD, Gunnell D (2003) Breast density: agreement of measures from film and digital image. Br J Radiol 76:561–563Fischmann A, Siegmann KC, Wersebe A, Claussen CD, Muller-Schimpfle M (2005) Comparison of full-field digital mammography and film-screen mammography: image quality and lesion detection. Br J Radiol 78:312–315Venta LA, Hendrick RE, Adler YT, DeLeon P, Mengoni PM, Scharl AM, Comstock CE, Hansen L, Kay N, Coveler A, Cutter G (2001) Rates and causes of disagreement in interpretation of full-field digital mammography and film-screen mammography in a diagnostic setting. AJR Am J Roentgenol 176:1241–1248Maclure M, Willett WC (1987) Misinterpretation and misuse of the kappa-statistic. Am J Epidemiol 126:161–169Cicchetti DV, Allison T (1971) A new procedure for assessing reliability of scoring EEG sleep recordings. Am J EEG Technol 11:101–110Fleiss JL, Cohen J (1973) Equivalence of weighted kappa and intraclass correlation coefficient as measures of reliability. Educ Psychol Measur 33:613–619Yaffe MJ, Mainprize JG, Jong RA (2008) Technical developments in mammography. Health Phys 95:599–611Tice JA, Feldman MD (2008) Full-field digital mammography compared with screen-film mammography in the detection of breast cancer: rays of light through DMIST or more fog? Breast Cancer Res Treat 107:157–165Vinnicombe S, Pinto Pereira SM, McCormack VA, Shiel S, Perry N, dos Santos Silva I (2009) Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data. Radiology 251:347–358Del Turco MR, Mantellini P, Ciatto S, Bonardi R, Martinelli F, Lazzari B, Houssami N (2007) Full-field digital versus screen-film mammography: comparative accuracy in concurrent screening cohorts. AJR Am J Roentgenol 189:860–866Boyd NF (2011) Tamoxifen, mammographic density, and breast cancer prevention. J Natl Cancer Inst 103:704–70
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