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A multiple regression model for peak skin dose using principal component analysis in interventional radiology.

Noriyuki Kuga1, Katsutoshi Shirieda2,3, Yumi Hirabara4

  • 1Department of Radiological Science, Faculty of Health Sciences, Junshin Gakuen University, 1-1-1 Chikushigaoka, Minami-ku, Fukuoka, 815-810, Japan. kuga.n@junshin-u.ac.jp.

Radiological Physics and Technology
|March 16, 2025
PubMed
Summary

This study predicts patient radiation doses in interventional radiology. Air kerma measurements accurately estimate peak skin dose, with principal component analysis improving prediction models for better radiation safety.

Keywords:
Digital imaging and communication in medicine-radiation dose structured reportInterventional radiologyMultiple regression analysisPeak skin dosePrincipal component analysisRadio-photoluminescence glass dosimeter

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Area of Science:

  • Medical Physics
  • Radiology
  • Radiation Dosimetry

Background:

  • Increasing complexity of interventional radiology procedures leads to higher patient radiation doses.
  • Effective patient radiation dose management is crucial but underutilized in many facilities.
  • Accurate prediction of peak skin dose (PSD) is essential for radiation safety.

Purpose of the Study:

  • To develop and validate models for predicting peak skin dose (PSD) in interventional radiology.
  • To assess the utility of dose parameters from digital imaging and communication in medicine-radiation dose structured reports for PSD prediction.
  • To compare the predictive accuracy of air kerma (Ka,r) and air kerma area product (KAP) for PSD.

Main Methods:

  • Utilized data from radio-photoluminescence glass dosimeters and five dose parameters.
  • Employed single and multiple regression analyses to develop PSD prediction models.
  • Applied principal component analysis (PCA) to consolidate data and enhance regression models.

Main Results:

  • Air kerma (Ka,r) was found to be more accurate in predicting PSD than air kerma area product (KAP).
  • Rotational digital subtraction angiography showed a minimal impact on peak skin dose.
  • Principal component analysis-enhanced multiple regression models significantly improved PSD prediction accuracy.

Conclusions:

  • Predictive models using dose parameters from DICOM-RDSR are feasible for estimating patient radiation doses.
  • Ka,r is a more reliable predictor of PSD than KAP in complex interventional procedures.
  • PCA-enhanced models offer superior accuracy for peak skin dose estimation, aiding radiation dose management.