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

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.

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Journal
Journal of Orthopaedic Research®
Published
2026-01-29
DOI
https://doi.org/10.1002/jor.70154
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

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

Bruno Gobbato, William R. Aibinder, Christopher Roche, Daniel E. Wessell et al.
Journal of Orthopaedic Research®
Radiomics and Machine Learning in Medical Imaging
article

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

Bruno Gobbato, William R. Aibinder, Christopher Roche, Daniel E. Wessell, Josie Elwell, Vivek Kumar, Bradley Schoch, Hamidreza Rajabzadeh‐Oghaz
article en

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.

Journal of Orthopaedic Research®Vol. 44(2)
University of Michigan (US), Jacksonville College (US), University of Florida (US), Centro Universitário de Jaraguá do Sul (BR), Michigan United (US), Mayo Clinic in Florida (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 74%
Radiomics and Machine Learning in Medical Imaging
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