Sentence examples for radiological datasets from inspiring English sources

This study is unique as it represents the first study of its kind for CMI where multiple biological and radiological datasets exist across the same set of patients thereby motivating the extension of the original sparse k-means clustering method to accommodate multiple datasets from different sources.

Further integration of the presented dataset with histopathology and radiological databases will provide more opportunities for in-depth analyses of specific clinical factors.

The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging.

By applying both clustering methods to our data, we were able to establish patient classes based solely on biological or radiological data, as well as through the integration of these datasets.

Bioinformatic analysis of our dataset revealed that 32 probesets showed different expression pattern according to radiological subclasses (p < 0.005).

The dataset indicates that there may be a possible role of preoperative radiological characteristics for providing information previously only available postoperatively to aid management decisions.

Our original dataset did not have information on dates of exposure for all the radiological procedures undergone by subjects.

The statistical analysis was performed on patients with a complete dataset (clinical and US evaluation at baseline, and after 4 months of follow-up and radiological evaluation at baseline, and after 2 years of follow-up).

Instead, we have chosen to analyze the full dataset, relying on statistical analysis, model selection, and model averaging to disentangle the web of correlations among radiological disease, OA risk factors, and biomarkers.

Radiological sweeps were done.

The datasets are pretty limited.