Soliman Khaled, Almutairi Ahmed, AlHarbi Murdhi, Almutairi Khaleel, Almutairi Turky, Bakkari Mousa
Department of Medical Physics, Prince Sultan Military Medical City, Riyadh, Saudi Arabia.
Department of Radiodiagnostic and Medical Imaging, Prince Sultan Military Medical City, Riyadh, Saudi Arabia.
Indian J Radiol Imaging. 2021 Apr;31(2):265-269. doi: 10.1055/s-0041-1733814. Epub 2021 Jul 28.
Uterine artery embolization (UAE) is a common interventional radiology procedure used in medicine; the procedure is safe but there is always a concern regarding radiation dose received by the patient. The aim of this study was to use multivariable logistic regression analysis (MLRA) to study a certain number of independent prognostic variables believed to provide an estimate of the likelihood of obtaining a high kerma area product ( ) at the end of the procedure. Radiation dose indices registered by the angiographic system structured dose report, the total fluoroscopy time (FT), the patient' body mass index (BMI), the number of images taken during the procedures (IMGS), and the performing physician experience (EXPER) were used to drive a logistic regression model (LRM). The LRM found was: Logit ( ) = -6.1525 + 0.0416 (FT) + 0.1028 (IMGS) + 0.1675 (BMI) - 0.1012 (EXPER). The prediction accuracy of the LRM was assessed using receiver operating characteristic (ROC) curve; by calculating the area under the curve (AUC), we found AUC = 0.7896, with optimal ROC point of 0.3261, 0.8036. The suggested LRM seems to indicate that patients with higher BMI, have taken longer FT, acquired higher IMGS and the procedure done by a less experienced performing physician is more susceptible to receive a higher at the end. The proposed LRM is useful in predicting the occurrence of higher radiation exposure interventions and can be used in patients' radiation dose optimization strategies during UAE procedures.
子宫动脉栓塞术（UAE）是医学中常用的一种介入放射学手术；该手术是安全的，但患者所接受的辐射剂量一直令人担忧。本研究的目的是使用多变量逻辑回归分析（MLRA）来研究一定数量的独立预后变量，这些变量被认为可以估计在手术结束时获得高比释动能面积乘积（ ）的可能性。由血管造影系统结构化剂量报告记录的辐射剂量指数、总透视时间（FT）、患者体重指数（BMI）、手术过程中拍摄的图像数量（IMGS）以及执行医生的经验（EXPER）被用于驱动一个逻辑回归模型（LRM）。所发现的LRM为：Logit（ ）= -6.1525 + 0.0416（FT）+ 0.1028（IMGS）+ 0.1675（BMI）- 0.1012（EXPER）。使用受试者工作特征（ROC）曲线评估LRM的预测准确性；通过计算曲线下面积（AUC），我们发现AUC = 0.7896，最佳ROC点为0.3261, 0.8036。所建议的LRM似乎表明，BMI较高、FT较长、IMGS较高且由经验较少的执行医生进行手术的患者在手术结束时更易接受较高的 。所提出的LRM有助于预测高辐射暴露干预的发生，并可用于UAE手术期间患者的辐射剂量优化策略。
