Supervoxel-based image-to-biomarker conversions - An initial study on morphological age prediction from whole-body MRI and its clinical relevance in the UK Biobank

Biological aging remains a central focus of research, from the scale of sub-cellular processes to whole-organism tissue morphology and function. In this work, we developed a novel and quantitatively interpretable method for the prediction of variables, such as age, from tomographic medical images. The method uses supervoxels (whose granularity is selected by the user), standardized through inter-subject image registration, and tissue-specific feature extractions from each supervoxel to convert all image data collected into a set of well-defined imaging biomarkers. We applied the method to age prediction, using linear modelling and whole-body water-fat MRI data of 38,235 subjects (age span = 45 - 82 years) in the multicentre UK Biobank study, resulting in predictions representing morphological age (MA). We observed state-of-the-art whole-body age prediction performance on a held-out test set with a mean absolute error of 1.951/2.057 years, and R ² of 0.884/0.892 for females/males, respectively. The method was observed to outperform both previously reported CNN-based results from the UK Biobank, and predictions from explicit biomarkers, from a multi-organ/tissue segmentation approach, in a direct comparison. The image-based interpretability of the model allowed for detailed and tissue-specific analysis of the utilized body-wide associations with age. Important regions for the predictions included volumes of the aorta, regional muscle, bone marrow, and adipose tissue depots, and lean tissue fat content. The predicted MAs were of clinical relevance as they were significantly related to both type 2 diabetes and all-cause mortality. A key finding was an accuracy/utility trade-off where the more parsimonious models showed lower CA predictive performance but higher clinical relevance and interpretability.

The proposed method facilitates automated image-to-biomarker conversions and predictions based on subsets of anatomies, tissues, and image features, for potential application in numerous future medical studies. Code is shared at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://github.com/Radiological-Image-Analysis-Group-UU/ukbb_age_prediction">github.com/Radiological-Image-Analysis-Group-UU/ukbb_age_prediction</ext-link> .