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Predicting long-term outcomes of ultrasound-guided percutaneous irrigation of calcific tendinopathy with the use of machine learning

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Skeletal Radiology Aims and scope Submit manuscript

Abstract

Objective

To evaluate the performance of two machine learning models in predicting the long-term complete pain resolution in patients undergoing ultrasound-guided percutaneous irrigation of calcific tendinopathy (US-PICT).

Materials and methods

Within a 3-year period, 100 consecutive patients who underwent US-PICT for rotator cuff disease were prospectively enrolled. The location, maximum diameter, and type of each calcification were recorded. The degree of calcium retrieval was graded as complete or incomplete. Shoulder pain was assessed with the visual analogue scale (VAS) at baseline, 1-week, 1-month, and 1-year post-treatment. Measurements related to procedural details, patient, and calcification characteristics were used to construct a machine learning model for the prediction of pain at 1-year post-US-PICT. Two distinct models were built, one including VAS data at 1 week and another additionally including pain data at 1-month post-treatment. Variable importance analysis was performed for the 1-week model. Model performance was evaluated by using receiver operating characteristics (ROC) curves and the respective areas under the curve (AUC).

Results

The model exhibited an AUC of 69.2% for the prediction of complete pain resolution at 1 year. The addition of VAS scores at 1 month did not significantly alter the performance of the algorithm. Age and baseline VAS scores were the most important variables for classification performance.

Conclusion

The presented machine learning model exhibited an AUC of almost 70% in predicting complete pain resolution at 1 year. Pain data at 1 month do not appear to improve the performance of the algorithm.

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Author information

Author notes

  1. Evangelia E. Vassalou and Michail E. Klontzas contributed equally to this study. 

Authors and Affiliations

  1. Department of Medical Imaging, University Hospital, Heraklion, 71110, Voutes, Crete, Greece

    Evangelia E. Vassalou, Michail E. Klontzas & Apostolos H. Karantanas

  2. Department of Medical Imaging, General Hospital of Sitia, 72300, Xerokamares, Crete, Greece

    Evangelia E. Vassalou

  3. Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), N. Plastira 100, 70013, Heraklion, Crete, Greece

    Michail E. Klontzas & Apostolos H. Karantanas

  4. Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), N. Plastira 100, 70013, Heraklion, Crete, Greece

    Michail E. Klontzas & Kostas Marias

  5. Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71110, Heraklion, Greece

    Michail E. Klontzas & Apostolos H. Karantanas

  6. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410, Heraklion, Crete, Greece

    Kostas Marias

Authors
  1. Evangelia E. Vassalou
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  2. Michail E. Klontzas
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  3. Kostas Marias
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  4. Apostolos H. Karantanas
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Corresponding author

Correspondence to Apostolos H. Karantanas.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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256_2021_3893_MOESM1_ESM.png

Supplementary figure 1 Receiver operating characteristics (ROC) curves and the respective area under the curve (AUC) for a logistic regression model built and trained with the inclusion of visual analogue scale (VAS) scores at 1 week (PNG 188 KB)

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Vassalou, E.E., Klontzas, M.E., Marias, K. et al. Predicting long-term outcomes of ultrasound-guided percutaneous irrigation of calcific tendinopathy with the use of machine learning. Skeletal Radiol 51, 417–422 (2022). https://doi.org/10.1007/s00256-021-03893-7

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  • DOI: https://doi.org/10.1007/s00256-021-03893-7

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