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Predictors of mammographic microcalcifications.

Shadi Azam | Mikael Eriksson | Arvid Sjölander | Marike Gabrielson | Roxanna Hellgren | Kamila Czene | Per Hall
International journal of cancer | 2021

We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of microcalcification clusters and the average mammographic density area were measured using a Computer Aided Detection system and the STRATUS method, respectively. A polygenic risk score for breast cancer, including 313 single nucleotide polymorphisms, was calculated for those women genotyped (N = 7387). Odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders, were estimated. Age was strongly associated with microcalcification clusters. Both high mammographic density (>40 cm2 ), and high polygenic risk score (80-100 percentile) were associated with microcalcification clusters, OR = 2.08 (95% CI = 1.93-2.25) and OR = 1.22 (95% CI = 1.06-1.48), respectively. Among reproductive risk factors, life-time breastfeeding duration >1 year was associated with microcalcification clusters OR = 1.22 (95% CI = 1.03-1.46). The association was confined to postmenopausal women. Among lifestyle risk factors, women with a body mass index ≥30 kg/m2 had the lowest risk of microcalcification clusters OR = 0.79 (95% CI = 0.73-0.85) and the association was stronger among premenopausal women. Our results suggest that age, mammographic density, genetic predictors of breast cancer, having more than two children, longer duration of breast-feeding are significantly associated with increased risk of microcalcification clusters. However, most lifestyle risk factors for breast cancer seem to protect against presence of microcalcification clusters. More research is needed to study biological mechanisms behind microcalcifications formation.

Pubmed ID: 32949149

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Karma (tool)

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An information integration software tool that enables users to integrate data from a variety of data sources including databases, spreadsheets, delimited text files, XML, JSON, KML and Web APIs. Users integrate information by modeling it according to an ontology of their choice using a graphical user interface that automates much of the process. Karma learns to recognize the mapping of data to ontology classes and then uses the ontology to propose a model that ties together these classes. Users then interact with the system to adjust the automatically generated model. During this process, users can transform the data as needed to normalize data expressed in different formats and to restructure it. Once the model is complete, users can publish the integrated data as RDF or store it in a database.

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