Background And Purpose: Myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD) have a higher prevalence among children. For children undergoing the initial manifestation of MOGAD, prompt diagnosis has paramount importance. This study assessed the performance of multiparameter MRI-based radiomics in distinguishing patients with and without MOGAD with idiopathic inflammatory demyelinating diseases.
Materials And Methods: We enrolled a cohort of 121 patients diagnosed with idiopathic inflammatory demyelinating diseases, including 68 children with MOGAD and 53 children without MOGAD. Radiomics models (T1WI, T2WI, FLAIR, and compound model) using features extracted from demyelinating lesions within the brain parenchyma were developed in the training set. The performance of these models underwent validation within the internal testing set. Additionally, we gathered clinical factors and MRI features of brain parenchymal lesions at their initial presentation. Subsequently, these variables were used in the construction of a clinical prediction model through multivariate logistic regression analysis.
Results: The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the training set were 0.781 (95% CI, 0.689-0.864), 0.959 (95% CI, 0.924-0.987), 0.939 (95% CI, 0.898-0.979), and 0.989 (95% CI, 0.976-0.999), respectively. The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the testing set were 0.500 (95% CI, 0.304-0.652), 0.833 (95% CI, 0.697-0.944), 0.804 (95% CI, 0.664-0.918), and 0.905 (95% CI, 0.803-0.979), respectively. The areas under the curve of the clinical prediction model in the training set and testing set were 0.700 and 0.289, respectively.
Conclusions: Multiparameter MRI-based radiomics helps distinguish MOGAD from non-MOGAD in patients with idiopathic inflammatory demyelinating diseases.
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http://dx.doi.org/10.3174/ajnr.A8045 | DOI Listing |
Insights Imaging
November 2024
Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, People's Republic of China.
J Hepatocell Carcinoma
October 2024
Medical Imaging Key Laboratory of Sichuan Province, Science and Technology Innovation Center, Interventional Medical Center, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.
Objective: To develop a model for predicting the overall survival (OS) of hepatocellular carcinoma (HCC) patients after transarterial chemoembolization (TACE) on the basis of multisequence MRI radiomic features and clinical variables.
Methods: The DCE-MRI and clinical data of 116 HCC patients treated with TACE for the first time were retrospectively analyzed. The included patients were randomly divided into training and validation cohorts at a ratio of 7:3.
J Imaging Inform Med
July 2024
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and validate a machine learning (ML) model for the non-invasive assessment of STA-MCA bypass surgery efficacy in MMD. This study enrolled 118 MMD patients undergoing STA-MCA bypass surgery.
View Article and Find Full Text PDFHeliyon
June 2024
Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
Objectives: This study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC).
Methods: A total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps.
Neurospine
June 2024
Department of Medicine, Maharat Nakhon Ratchasima Hospital, Nakhon Ratchasima, Thailand.
Objective: This study aimed to compare and analyze differences in clinical and magnetic resonance imaging (MRI) findings between tuberculous spondylodiscitis (TbS) and pyogenic spondylodiscitis (PyS), and to develop and validate a simplified multiparameter MRIbased scoring system for differentiating TbS from PyS.
Methods: We compared predisposing factors in 190 patients: 123 with TbS and 67 with PyS, confirmed by laboratory tests, culture, or pathology. Data encompassing patient demographics, clinical characteristics, laboratory results, and MRI findings were collected between 2015 and 2020.
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