<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">René Pallenberg</style></author><author><style face="normal" font="default" size="100%">Fleitmann, Marja</style></author><author><style face="normal" font="default" size="100%">Andreas Martin Stroth</style></author><author><style face="normal" font="default" size="100%">Jan Gerlach</style></author><author><style face="normal" font="default" size="100%">Fürschke, Alexander</style></author><author><style face="normal" font="default" size="100%">Barkhausen, Jörg</style></author><author><style face="normal" font="default" size="100%">Bischof, Arpad</style></author><author><style face="normal" font="default" size="100%">Heinz Handels</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Random Forest and Gradient Boosted Trees for Patient Individualized Contrast Agent Dose Reduction in CT Angiography</style></title><secondary-title><style face="normal" font="default" size="100%">Studies in health technology and informatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stud Health Technol Inform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computed Tomography Angiography</style></keyword><keyword><style  face="normal" font="default" size="100%">Contrast Media</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Tapering</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Logistic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">random forest</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 May 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">302</style></volume><pages><style face="normal" font="default" size="100%">952-956</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work aims to recognize the patient individual possibility of contrast dose reduction in CT angiography. This system should help to identify whether the dose of contrast agent in CT angiography can be reduced to avoid side effects. In a clinical study, 263 CT angiographies were performed and, in addition, 21 clinical parameters were recorded for each patient before contrast agent administration. The resulting images were labeled according to their contrast quality. It is assumed that the contrast dose could be reduced for CT angiography images with excessive contrast. These data was used to develop a model for predicting excessive contrast based on the clinical parameters using logistic regression, random forest, and gradient boosted trees. In addition, the minimization of clinical parameters required was investigated to reduce the overall effort. Therefore, models were tested with all subsets of clinical parameters and each parameter's importance was examined. In predicting excessive contrast in CT angiography images covering the aortic region, a maximum accuracy of 0.84 was achieved by a random forest with 11 clinical parameters; for the leg-pelvis region data, an accuracy of 0.87 was achieved by a random forest with 7 parameters; and for the entire data set, an accuracy of 0.74 was achieved by gradient boosted trees with 9 parameters.</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/37203543?dopt=Abstract</style></custom1></record></records></xml>