Body Charts from CT Segmentations across the Adult Lifespan: Large-scale Cross-sectional and Longitudinal Analyses

Purpose To model the distribution of CT-derived whole-body anatomic volumes across adulthood and establish comprehensive cross-sectional and longitudinal reference charts, addressing the current lack of nonbrain CT-based whole-body standards. Materials and Methods Retrospective CT scans acquired from March 2017 to April 2025 (189 710 scans, 106 563 patients) from the institutional picture archiving and communication system and two external datasets (19 393 and 1158 patients, respectively) were automatically segmented into 104 structures (totaling 7.8 million volumes). An automated quality control pipeline, incorporating a novel outlier removal strategy based on strong correlation between organ sizes, ensured data reliability. Cross-sectional normative models were constructed using generalized additive models for location, scale, and shape to capture nonlinear age effects through fractional polynomial functions. A generalized additive mixed model was used for longitudinal analyses to assess within-patient changes over follow-up visits. Results All anatomic structures followed complex, nonlinear age trajectories, with marked sex differences and distinct CT contrast material effects on vascular structures. Bootstrap resampling confirmed the stability and precision of these volume trajectories in both central tendency and variability. An exemplary cardiomegaly case-control analysis showed significantly increased centile scores (<i>P</i> < .001) for heart volume. The longitudinal analysis further revealed significant age-sex interactions influencing within-patient trajectories. Conclusion Cross-sectional and longitudinal reference models were developed from CT-derived anatomic volumes that map the trajectories of structural body changes across adulthood. These body charts facilitate robust quantification of individual deviations via centile scores. <b>Keywords:</b> Anatomy, Statistics, Multivariate Adaptive Regression Splines (MARS), Regression Algorithms, Machine Learning Algorithms, Segmentation <i>Supplemental material is available for this article.</i> © RSNA, 2026. See also commentary by Chai and Shi in this issue.