Identification of Reproducible CT‐Image Based Radiomic Features That Predict Shoulder Arthroplasty Outcomes

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Authors: Hamidreza Rajabzadeh-Oghaz, Josie A. Elwell, Bradley S. Schoch, William Reuben Aibinder, Bruno Borralho Gobbato, Daniel E. Wessell, Vikas Kumar, Christopher P. Roche
Cited by: 0
References: 37

Abstract

The goal of this radiomic analysis is to quantify the sensitivity of radiomic features on computed tomography (CT) image pre-processing parameters and use machine learning (ML) techniques to identify the radiomic features that are highly predictive of shoulder arthroplasty outcomes. An ML framework auto-segmented 3D masks of the deltoid muscle and scapula bone from pre-operative CT images of 1949 primary anatomic total shoulder arthroplasty (aTSA)/reverse total shoulder arthroplasty (rTSA) patients. Radiomic features were extracted after various image pre-processing protocols and assessed for reproducibility. The radiomic features deemed robust to image pre-processing were used to train ML predictive outcomes models. Feature importance data were rank-ordered to identify the radiomic features that were highly predictive of pain, motion, and function before and after aTSA/rTSA. A sensitivity analysis identified 37 deltoid muscle and 38 scapular bone radiomic features that were robust, reproducible, and unique across image pre-processing parameters. The most predictive deltoid muscle radiomic measurements were normalized volume, elongation, flatness, fat percentage, sphericity, and max 2D diameter column. The most predictive scapular bone radiomic measurements were flatness, sphericity, elongation, max 2D diameter column, and max 2D diameter slice. Radiomic data of the deltoid and scapula were highly predictive of pain, motion, and function before and after aTSA and rTSA. Radiomic data were more predictive than patient comorbidities, diagnosis, and implant type/size data, but less predictive than pre-operative active range of motion measurements and patient reported outcome measures, 3D measurements from planning software, or patient demographic data. Future work is required to clinically validate these radiomic features before they can be deployed in clinical decision support tools. LEVEL OF EVIDENCE: Level III, Retrospective Comparative Outcome Study.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Shoulder Injury and Treatment Image Retrieval and Classification Techniques

Publication details

Journal / source
Journal of Orthopaedic Research®
Volume
44
Issue
2
Pages
e70154–e70154
Publication date
29 January 2026
Language
EN