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

Hamidreza Rajabzadeh-Oghaz1, Josie Elwell1, Bradley Schoch2

  • 1Advita Ortho, Gainesville, Florida, USA.

Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society
|January 29, 2026
PubMed
Summary
This summary is machine-generated.

Radiomic features from CT scans predict shoulder arthroplasty outcomes. Machine learning identified key deltoid and scapula features, outperforming other clinical data for predicting pain, motion, and function.

Keywords:
CT image processingclinical outcomes predictionsmachine learningradiomic analysisshoulder arthroplasty

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Area of Science:

  • Radiology and Medical Imaging
  • Machine Learning in Healthcare
  • Orthopedic Surgery Outcomes

Background:

  • Shoulder arthroplasty outcomes are influenced by various factors, but precise pre-operative prediction remains challenging.
  • Radiomics, the extraction of quantitative features from medical images, offers potential for improved outcome prediction.

Purpose of the Study:

  • To quantify the sensitivity of radiomic features to computed tomography (CT) image pre-processing.
  • To identify radiomic features predictive of shoulder arthroplasty outcomes using machine learning (ML).

Main Methods:

  • An ML framework auto-segmented deltoid muscle and scapula from 1949 pre-operative CT scans.
  • Radiomic features were extracted, assessed for reproducibility across pre-processing parameters, and used to train ML models.
  • Feature importance analysis identified predictors of pain, motion, and function.

Main Results:

  • 37 deltoid and 38 scapular radiomic features were robust and reproducible across pre-processing parameters.
  • Key deltoid features included normalized volume and fat percentage; key scapular features included flatness and sphericity.
  • Radiomic data were highly predictive of post-operative pain, motion, and function, outperforming comorbidities and diagnosis.

Conclusions:

  • Robust radiomic features from the deltoid muscle and scapula can predict shoulder arthroplasty outcomes.
  • Radiomics shows promise for clinical decision support, though further validation is needed.
  • Radiomic features offer a novel, quantitative approach to assessing pre-operative risk and potential outcomes.