<?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%">Yu, Yang</style></author><author><style face="normal" font="default" size="100%">Li, Xiaoran</style></author><author><style face="normal" font="default" size="100%">Du, Tianming</style></author><author><style face="normal" font="default" size="100%">Rahaman, Md</style></author><author><style face="normal" font="default" size="100%">Grzegorzek, Marcin</style></author><author><style face="normal" font="default" size="100%">Li, Chen</style></author><author><style face="normal" font="default" size="100%">Sun, Hongzan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Medicine</style></secondary-title><short-title><style face="normal" font="default" size="100%">Intelligent Medicine</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2024</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">153 - 160</style></pages><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>