首页|Second Affiliated Hospital of Xi'an Jiaotong University Reports Findings in Pers onalized Medicine (A comprehensive predictive model for postoperative joint func tion in robot-assisted total hip arthroplasty patients: combining radiomics and ...)
Second Affiliated Hospital of Xi'an Jiaotong University Reports Findings in Pers onalized Medicine (A comprehensive predictive model for postoperative joint func tion in robot-assisted total hip arthroplasty patients: combining radiomics and ...)
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New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting ou t of Xi'an, People's Republic of China, by NewsRx editors, research stated, "Tot al hip arthroplasty (THA) effectively treats various end-stage hip conditions, o ffering pain relief and improved joint function. However, surgical outcomes are influenced by multifaceted factors." Our news journalists obtained a quote from the research from the Second Affiliat ed Hospital of Xi'an Jiaotong University, "This research aims to create a predic tive model, incorporating radiomic and clinical information, to forecast post-su rgery joint function in robot-assisted THA (RA-THA) patients. The study set comp rised 136 patients who underwent unilateral RA-THA, which were subsequently part itioned into a training set (n = 95) and a test set (n = 41) for analysis. Preop erative CT imaging was employed to derive 851 radiomic characteristics, selectin g those with an intra-class correlation coefficient > 0. 75 for analysis. Least absolute shrinkage and selection operator regression redu ced redundancy to six significant radiomic features. Clinical data including pre operative Visual Analog Scale (VAS), Harris Hip Score (HHS), and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score were collected. Logi stic regression identified significant predictors, and three models were develop ed. Receiver operating characteristic and decision curves evaluated the models. Preoperative VAS, HHS, WOMAC score, and radiomics feature scores were significan t predictors. In the training set, the AUCs were 0.835 (clinical model), 0.757 ( radiomic model), and 0.891 (combined model). In the test set, the AUCs were 0.77 7 (clinical model), 0.824 (radiomic model), and 0.881 (combined model). The cons tructed nomogram prediction model combines radiological features with relevant c linical data to accurately predict functional outcomes 3 years after RA-THA."
Xi'anPeople's Republic of ChinaAsiaArthroplastyDrugs and TherapiesEmerging TechnologiesHealth and MedicineMachine LearningOrthopedic ProceduresPersonalized MedicinePersonalized The rapyRobotRoboticsSurgery
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