Purpose: This study aimed to develop and validate a cine cardiovascular magnetic resonance (CMR)-based radiomics nomogram model for predicting microvascular obstruction (MVO) following reperfusion in patients with ST-segment elevation myocardial infarction (STEMI).
Methods: In total, 167 consecutive STEMI patients were retrospectively enrolled. The patients were randomly divided into training and validation cohorts with a ratio of 7:3. All patients were diagnosed with myocardial infarction with or without MVO based on late gadolinium enhancement imaging. Radiomics features were extracted from the cine CMR end-diastolic volume phase of the entire left ventricular myocardium (3D volume). The least absolute shrinkage and selection operator (LASSO) regression was employed to select the features that were most relevant to the MVO; these features were then used to calculate the radiomics score (Rad-score). A combined model was developed based on independent risk factors screened using multivariate regression analysis and visualized using a nomogram. Performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).
Results: The univariate analysis of clinical features demonstrated that only cardiac troponin I (cTNI) was significantly associated with MVO. LASSO regression revealed that 12 radiomics features were strongly associated with MVO. Multivariate regression analysis indicated that cTNI and Rad-score were independent risk factors for MVO. The nomogram based on these two features achieved an area under the curve of 0.86 and 0.78 in the training and validation cohorts, respectively. Calibration curves and DCA indicated the clinical feasibility and utility of the nomogram.
Conclusions: A CMR-based radiomics nomogram offers an effective means of predicting MVO without contrast agents and radiation, which could facilitate risk stratification of patients with STEMI after PCI for reperfusion.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655024 | PMC |
| http://dx.doi.org/10.3389/fcvm.2023.1274267 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Spectral CT radiomics features of the tumor and perigastric adipose tissue can predict lymph node metastasis in gastric cancer.
Abdom Radiol (NY)
January 2025
Department of Radiology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.
Objectives: To develop a nomogram based on the radiomics features of tumour and perigastric adipose tissue adjacent to the tumor in dual-layer spectral detector computed tomography (DLCT) for lymph node metastasis (LNM) prediction in gastric cancer (GC).
Methods: A retrospective analysis was conducted on 175 patients with gastric adenocarcinoma. They were divided into training cohort (n = 125) and validation cohort (n = 50).
Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection.
J Neurosurg
January 2025
Departments of1Neurosurgery.
Objective: Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endonasal approach (EEEA) has evolved as a more reliable surgical alternative and yields better visual outcomes than traditional craniotomy, postoperative visual deterioration remains one of the most common complications, and relevant risk factors are still poorly defined. Hence, identifying risk factors and developing a predictive model for postoperative visual deterioration is indeed necessary.
View Article and Find Full Text PDFCT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study.
Eur J Radiol Open
June 2025
Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, No. 181 Hanyu road, Shapingba district, Chongqing 400030, China.
Purpose: The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.
Methods: This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort).
CT-based radiomics predicts HRD score and HRD status in patients with ovarian cancer.
Front Oncol
January 2025
Department of Gynecology, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China.
Introduction: This study predicted HRD score and status based on intra- and peritumoral radiomics in patients with ovarian cancer (OC) for better guiding the use of PARPi in clinical.
Methods: A total of 106 and 95 patients with OC were included between January 2022 and November 2023 for predicting HRD score and status, respectively. Radiomics features were extracted and quantitatively analyzed from intra- and peri-tumor regions in the CT image.
Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less.
Front Oncol
January 2025
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Purpose: To develop and validate a radiomics nomogram model for predicting the micropapillary pattern (MPP) in lung adenocarcinoma (LUAD) tumors of ≤2 cm in size.
Methods: In this study, 300 LUAD patients from our institution were randomly divided into the training cohort (n = 210) and an internal validation cohort (n = 90) at a ratio of 7:3, besides, we selected 65 patients from another hospital as the external validation cohort. The region of interest of the tumor was delineated on the computed tomography (CT) images, and radiomics features were extracted.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!