Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

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Recent evidence suggests that seeking out extra-pair paternity (EPP) can be a viable alternative reproductive strategy for both males and females in many pair-bonded species, including humans. Accurate data on EPP rates in humans, however, are scant and mostly restricted to extant populations. Here, we provide the first large-scale, unbiased genetic study of historical EPP rates in a Western European human population based on combining Y-chromosomal data to infer genetic patrilineages with genealogical and surname data, which reflect known historical presumed paternity. Using two independent methods, we estimate that over the last few centuries, EPP rates in Flanders (Belgium) were only around 1-2% per generation. This figure is substantially lower than the 8-30% per generation reported in some behavioural studies on historical EPP rates, but comparable with the rates reported by other genetic studies of contemporary Western European populations. These results suggest that human EPP rates have not changed substantially during the last 400 years in Flanders and imply that legal genealogies rarely differ from the biological ones. This result has significant implications for a diverse set of fields, including human population genetics, historical demography, forensic science and human sociobiology.

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