Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

CNU IR > Pharmacy and Science > Dept. of Health and Nutrition (including master's program) > Periodical Articles > Item 310902800/34572

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34572

Title:	Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters
Authors:	Shih, Yun-Ju Liu, Yan-Lin Chen, Jeon-Hor Ho, Chung-Han Yang, Cheng-Chun Chen, Tai-Yuan Wu, Te-Chang Ko, Ching-Chung Zhou, Jonathan T. Zhang, Yang Su, Min-Ying
Contributors:	Chi Mei Hospital University of California System University of California Irvine E-Da Hospital I Shou University Southern Taiwan University of Science & Technology Chang Jung Christian University Department of Health and Nutrition, Chia Nan University of Pharmacy & Science Rutgers System Rutgers New Brunswick Rutgers Biomedical & Health Sciences Rutgers Cancer Institute of New Jersey Kaohsiung Medical University
Keywords:	early hematoma expansion intracerebral hemorrhage risk-factors cerebral contusions impact
Date:	2022
Issue Date:	2023-12-11 13:58:31 (UTC+8)
Publisher:	MDPI
Abstract:	(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.
Relation:	DIAGNOSTICS, v.12, n.7, 1677
Appears in Collections:	[Dept. of Health and Nutrition (including master's program)] Periodical Articles

Files in This Item:

File	Description	Size	Format
diagnostics12071677.pdf		3113Kb	Adobe PDF	32	View/Open
index.html		0Kb	HTML	223	View/Open

Loading...