Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.

Geographic disparities of breast cancer incidence in Portugal at the district level: A spatial age-period-cohort analysis, 1998-2011.

BACKGROUND: Breast cancer is the most common malignancy in women world-wide and the most common cause of cancer deaths, which can often be managed with early diagnosis and subsequent treatment. Here, we focus on geographic disparities in incidence within Portugal for three age groups of women (30-49; 50-69; 70-84 years). METHODS: Age-period-cohort (APC) models are widely used in cancer surveillance, and these models have recently been extended to allow spatially-varying effects. We apply novel spatial APC models to estimate relative risk and age-adjusted temporal trends at the district level for the 20 districts in Portugal. Our model allows us to report on country-wide trends, but also to investigate geographic disparities between districts and trends within districts. RESULTS: Age-adjusted breast cancer incidence was increasing over 1998-2011 for all three age groups and in every district in Portugal. However, we detect spatially-structured between-district heterogeneity in relative risk and age-adjusted trends (Net Drifts) for each of the three age groups, which is most pronounced in the highly-screened (50-69yo) and late-onset (70-84yo) groups of women. CONCLUSIONS: We present evidence of disparities in breast cancer incidence at a more granular geographic level than previously reported. Some disparities may be due to latent risk factors, which cannot be accounted for by age, birth year, and geographic location alone. IMPACT: Our study motivates resuming data collection for breast cancer incidence at the district level in Portugal, as well as the study of exogenous risk factors.