  The latest advances in computer-assisted precision medicine are making it
feasible to move from population-wide models that are useful to discover
aggregate patterns that hold for group-based analysis to patient-specific
models that can drive patient-specific decisions with regard to treatment
choices, and predictions of outcomes of treatment. Body Composition is
recognized as an important driver and risk factor for a wide variety of
diseases, as well as a predictor of individual patient-specific clinical
outcomes to treatment choices or surgical interventions. 3D CT images are
routinely acquired in the oncological worklows and deliver accurate rendering
of internal anatomy and therefore can be used opportunistically to assess the
amount of skeletal muscle and adipose tissue compartments. Powerful tools of
artificial intelligence such as deep learning are making it feasible now to
segment the entire 3D image and generate accurate measurements of all internal
anatomy. These will enable the overcoming of the severe bottleneck that existed
previously, namely, the need for manual segmentation, which was prohibitive to
scale to the hundreds of 2D axial slices that made up a 3D volumetric image.
Automated tools such as presented here will now enable harvesting whole-body
measurements from 3D CT or MRI images, leading to a new era of discovery of the
drivers of various diseases based on individual tissue, organ volume, shape,
and functional status. These measurements were hitherto unavailable thereby
limiting the field to a very small and limited subset. These discoveries and
the potential to perform individual image segmentation with high speed and
accuracy are likely to lead to the incorporation of these 3D measures into
individual specific treatment planning models related to nutrition, aging,
chemotoxicity, surgery and survival after the onset of a major disease such as
cancer.
