Longitudinal Case-Control Study of Active and Passive Dense Mammographic Breast Tissue.

Mammography is used as secondary prevention for breast cancer. Computer-aided detection and image-based short-term risk estimation were developed to improve the accuracy of mammography. However, most approaches inherently lack the ability to connect observations at the mammography level to observations of cancer onset and progression seen at a smaller scale, which can occur years before imageable cancer and lead to primary prevention.

WAVELET-BASED AUTOMATIC PECTORAL MUSCLE SEGMENTATION FOR MAMMOGRAMS.

The computational analysis to assist radiologists in the interpretation of mammograms usually requires a pre-processing step where the image is converted into a black and white mask to separate breast tissue from the pectoral muscle and the image background. The manual delineation of the breast tissue from the mammogram image is subjective and time-consuming. The 2D Wavelet Transform Modulus Maxima (WTMM) segmentation method, a powerful and versatile multi-scale edge detection approach, is adapted and presented as a novel automated breast tissue segmentation method.

CT esophagogram in the emergency setting: typical findings and suggested workflow.

Purpose: Esophageal perforation (EP) can be a diagnostic challenge. Computed tomography (CT) and CT esophagography (CTE) are often used to rule out EP in the emergency setting with promising diagnostic performance, but the standard of care remains fluoroscopic esophagography (FE). We assess the diagnostic performance of CT and CTE when interpreted by expert and generalist radiologists and created an imaging workflow guide.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy.

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats.

Generalizing experimental results by leveraging knowledge of mechanisms.

We show how experimental results can be generalized across diverse populations by leveraging knowledge of local mechanisms that produce the outcome of interest, only some of which may differ in the target domain. We use structural causal models and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails the invariance of probabilities of causation across populations, which then enables generalization. We further provide: (i) bounds for the target effect when some of these conditions are violated; (ii) new identification results for probabilities of causation and the transported causal effect when trials from multiple source domains are available; as well as (iii) a Bayesian approach for estimating the transported causal effect from finite samples.