Purpose: To investigate the test-retest repeatability of radiomic features in myocardial native T1 and T2 mapping.
Methods: In this prospective study, 50 healthy volunteers (29 women and 21 men, mean age 39.4 ± 13.7 years) underwent two identical cardiac magnetic resonance imaging (MRI) examinations at 1.5 T. The protocol included native T1 and T2 mapping in both short-axis and long-axis orientation. For T1 mapping, we investigated standard (1.9 × 1.9 mm) and high (1.4 × 1.4 mm) spatial resolution. After manual segmentation of the left ventricular myocardium, 100 radiomic features from seven feature classes were extracted and analyzed. Test-retest repeatability of radiomic features was assessed using the intraclass correlation coefficient (ICC) and classified as poor (ICC < 0.50), moderate (0.50-0.75), good (0.75-0.90), and excellent (> 0.90).
Results: For T1 maps acquired in short-axis orientation at standard resolution, repeatability was excellent for 6 features, good for 29 features, moderate for 19 features, and poor for 46 features. We identified 15 features from 6 classes which showed good to excellent reproducibility for T1 mapping in all resolutions and all orientations. For short-axis T2 maps, repeatability was excellent for 6 features, good for 25 features, moderate for 23 features, and poor for 46 features. 12 features from 5 classes were found to have good to excellent repeatability in T2 mapping independent of slice orientation.
Conclusion: We have identified a subset of features with good to excellent repeatability independent of slice orientation and spatial resolution. We recommend using these features for further radiomics research in myocardial T1 and T2 mapping.
Key Points: Question The study addresses the need for reliable radiomic features for quantitative analysis of the myocardium to ensure diagnostic consistency in cardiac MRI. Findings We have identified a subset of radiomic features demonstrating good to excellent repeatability in native T1 and T2 mapping independent of slice orientation and resolution. Clinical relevanceRadiomic features have been proposed as diagnostic and prognostic biomarkers in various heart diseases. By identifying a subset of particularly reproducible radiomic features our study serves to inform the selection of radiomic features in future research and clinical applications.
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http://dx.doi.org/10.1007/s00330-024-11337-8 | DOI Listing |
Radiol Med
January 2025
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Purpose: Bodyweight loss is commonly found in Nasopharyngeal Carcinoma patients during Concurrent Chemo-radiotherapy (CCRT) and has implications for treatment decisions. However, the prognostic value of this weight loss remains uncertain. We addressed it by proposing a novel index Weight Censorial Score (WCS) that characterizes the patient-specific CCRT response on actual to estimated weight loss.
View Article and Find Full Text PDFPurpose: We hypothesised that applying radiomics to [F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. We compared the performance of radiomic features to human visual interpretation.
Materials And Methods: We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard).
Front Cardiovasc Med
January 2025
Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Objectives: To evaluate the feasibility of utilizing cardiac computer tomography (CT) images for extracting the radiomic features of the myocardium at the junction between the left atrial appendage (LAA) and the left atrium (LA) in patients with atrial fibrillation (AF) and to evaluate its asscociation with the risk of AF.
Methods: A retrospective analysis was conducted on 82 cases of AF and 56 cases in the control group who underwent cardiac CT at our hospital from May 2022 to May 2023, with recorded clinical information. The morphological parameters of the LAA were measured.
Front Neurol
January 2025
Department of Radiology, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, Jiangsu, China.
Introduction: Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features to achieve early and precise prediction of AIS prognosis.
Methods: This study enrolled 102 AIS patients admitted between December 2020 and September 2024.
J Res Med Sci
December 2024
Department of Biostatistics, Student Research Committee, University of Medical Sciences, Kermanshah, Iran.
Background: The initial assessment of trauma is a time-consuming and challenging task. The purpose of this research is to examine the diagnostic effectiveness and usefulness of machine learning models paired with radiomics features to identify blunt traumatic liver injury in abdominal computed tomography (CT) images.
Materials And Methods: In this study, 600 CT scan images of people with mild and severe liver damage due to trauma and healthy people were collected from the Kaggle dataset.
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