Mammographic density and exposure to air pollutants in premenopausal women: a cross-sectional study.

Background: Mammographic density (MD) is a well-established risk factor for breast cancer. Air pollution is a major public health concern and a recognized carcinogen. We aim to investigate the association between MD and exposure to specific air pollutants (SO, CO, NO, NO, NO, PM, PM, and O) in premenopausal females.

Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model.

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists.

Residential exposure to traffic pollution and mammographic density in premenopausal women.

Background: Mammographic density (MD) is the most important breast cancer biomarker. Ambient pollution is a carcinogen, and its relationship with MD is unclear. This study aims to explore the association between exposure to traffic pollution and MD in premenopausal women.

Mammographic density in the environs of multiple industrial sources.

Background: Mammographic density (MD), defined as the percentage of dense fibroglandular tissue in the breast, is a modifiable marker of the risk of developing breast cancer. Our objective was to evaluate the effect of residential proximity to an increasing number of industrial sources in MD.

Methods: A cross-sectional study was conducted on 1225 premenopausal women participating in the DDM-Madrid study.

SEQENS: An ensemble method for relevant gene identification in microarray data.

This paper describes an ensemble feature identification algorithm called SEQENS, and measures its capability to identify the relevant variables in a case-control study using a genetic expression microarray dataset. SEQENS uses Sequential Feature Search on multiple sample splitting to select variables showing stronger relation with the target, and a variable relevance ranking is finally produced. Although designed for feature identification, SEQENS could also serve as a basis for feature selection (classifier optimisation).

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.

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.

Residential proximity to industrial pollution and mammographic density.

Background: Mammographic density (MD), expressed as percentage of fibroglandular breast tissue, is an important risk factor for breast cancer. Our objective is to investigate the relationship between MD and residential proximity to pollutant industries in premenopausal Spanish women.

Methods: A cross-sectional study was carried out in a sample of 1225 women extracted from the DDM-Madrid study.

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients.

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.

Occupation, occupational exposures and mammographic density in Spanish women.

Introduction: Mammographic density (MD), the proportion of radiologically dense breast tissue, is a strong risk factor for breast cancer. Our objective is to investigate the influence of occupations and occupational exposure to physical, chemical, and microbiological agents on MD in Spanish premenopausal women.

Methods: This is a cross-sectional study based on 1362 premenopausal workers, aged 39-50, who attended a gynecological screening in a breast radiodiagnosis unit of Madrid City Council.

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

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.

Serum Phospholipid Fatty Acids and Mammographic Density in Premenopausal Women.

Background: The role of fatty acids (FAs) on mammographic density (MD) is unclear, and available studies are based on self-reported dietary intake.

Objectives: This study assessed the association between specific serum phospholipid fatty acids (PLFAs) and MD in premenopausal women.

Methods: The cross-sectional study DDM-Madrid recruited 1392 Spanish premenopausal women, aged 39-50 y, who attended a screening in a breast radiodiagnosis unit of Madrid City Council.

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

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.

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

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.

Occupational exposures and mammographic density in Spanish women.

Objectives: The association between occupational exposures and mammographic density (MD), a marker of breast cancer risk, has not been previously explored. Our objective was to investigate the influence of occupational exposure to chemical, physical and microbiological agents on MD in adult women.

Methods: This is a population-based cross-sectional study based on 1476 female workers aged 45-65 years from seven Spanish breast cancer screening programmes.

Occupation and mammographic density: A population-based study (DDM-Occup).

Introduction: High mammographic density is one of the main risk factors for breast cancer. Although several occupations have been associated with breast cancer, there are no previous occupational studies exploring the association with mammographic density. Our objective was to identify occupations associated with high mammographic density in Spanish female workers.

Thyroid disorders and mammographic density in Spanish women: Var-DDM study.

Objectives: The association between breast cancer (BC) and thyroid disorders has been widely explored with unclear results. Mammographic density (MD) is one of the strongest risk factor for BC. This study explores the relationship between thyroid diseases and MD in Spanish women.

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

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).

High Mammographic Density in Long-Term Night-Shift Workers: DDM-Spain/Var-DDM.

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. 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.

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

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool.

Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms.

We developed a semi-automated tool to assess mammographic density (MD), a phenotype risk marker for breast cancer (BC), in full-field digital images and evaluated its performance testing its reproducibility, comparing our MD estimates with those obtained by visual inspection and using Cumulus, verifying their association with factors that influence MD, and studying the association between MD measures and subsequent BC risk. Three radiologists assessed MD using DM-Scan, the new tool, on 655 processed images (craniocaudal view) obtained in two screening centers. Reproducibility was explored computing pair-wise concordance correlation coefficients (CCC).

Women's features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN).

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.

Computer-aided detection of prostate cancer.

Background: Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in developed countries. Detection of prostate carcinoma at an early stage is crucial for successful treatment.

Material And Methods: A method for the analysis of transrectal ultrasound images aimed at computer-aided diagnosis of prostate cancer is tested in this paper.