Radiomic analysis of the deltoid and scapula: identification of computed tomography-image based measurements...

The goal of this study is to analyze a registry of preoperative computed tomography (CT) images of anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA) patients, quantify the radiomics of the deltoid muscle and scapular bone, and identify the radiomic features that are most predictive of pain, motion, and function before and after aTSA/rTSA.

Preoperative CT images and clinical data from 4,009 primary shoulder arthroplasty patients were retrospectively analyzed. Next, three-dimensional masks of the deltoid (n = 2,597) and scapula (n = 3,358) were auto-segmented from CT images, and radiomic features were extracted using Py-Radiomics. These radiomics features were then used to train machine-learning regression models to predict pain, motion, and function before and after aTSA/rTSA. Finally, a clustering analysis was performed using the most predictive radiomic features to identify unique deltoid and scapula morphological groups/classes relevant to clinical outcomes before and after aTSA/rTSA.

Incorporating radiomic features into the machine-learning models improved the accuracy of 70.5% of deltoid model outcome predictions and 67.3% of scapular model outcome predictions. Analysis of feature importance data demonstrated that the most predictive radiomic features were numerical representations of deltoid and scapula shape and size. Notably, most shape-based radiomic features were more predictive of aTSA/rTSA outcomes than any patient demographic data (except age), comorbidity data, implant data, or diagnosis data. Finally, a radiomic-based clustering analysis identified several deltoid muscle and scapula bone morphologies associated with differences in clinical outcomes before and after aTSA/rTSA.

This analysis of >4,000 preoperative CT scans identified numerous radiomic features of the deltoid and scapula that were highly predictive of pain, motion, and function before and after aTSA/rTSA. These most predictive radiomic features were aggregated into unique morphological clusters of deltoids and scapula that were associated with differences in clinical outcomes before and after aTSA/rTSA. Shape-based radiomic features were more predictive than first-order and second-order radiomic features, suggesting that these more interpretable measurements are more clinically relevant and could be more readily incorporated into future radiomic-based clinical decision support tools. Future work is required to further validate these radiomic findings and refine the proposed clustering analysis.