Objective: To develop a non-contrast CT based multi-regional radiomics model for predicting contrast medium (CM) extravasation in patients with tumors.
Methods: A retrospective analysis of non-contrast CT scans from 282 tumor patients across two medical centers led to the development of a radiomics model, using 157 patients for training, 68 for validation, and 57 from an external center as an independent test cohort. The different volumes of interest from right common carotid artery/right internal jugular vein, right subclavian artery/vein and thoracic aorta were delineated. Radiomics features from the training cohort were used to calculate radiomics scores (Rad scores) and develop radiomics model. Non-contrast CT radiomics features were combined with clinical factors to develop an integrated model. A nomogram was created to visually represent the integration of radiomic signatures and clinical factors. The model's predictive performance and clinical utility were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA), respectively. Calibration curves were also used to assess the concordance between the model-predicted probabilities and the observed event probabilities.
Results: Thirteen radiomics features were selected to determine the Rad score. The radiomic model outperformed the clinical model in the training, validation, and external test cohorts, achieving a greater area under the ROC curve (AUC) with values of 0.877, 0.866, 0.828 compared to the clinical model's 0.852, 0.806, 0.740. The combined model yielded better AUC of 0.945, 0.911, and 0.869 in the respective cohorts. The nomogram identified females, the elderly, individuals with hypertension, long term chemotherapy, radiomic signatures as independent risk factors for CM extravasation in patients with tumors. Calibration and DCA validated the high accuracy and clinical utility of this model.
Conclusions: Radiomics models based on multi-regional non-contrast CT image offered improved prediction of CM extravasation compared with clinical model alone.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893132 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0314601 | PLOS |
Rheumatology (Oxford)
March 2025
Department of General Internal Medicine, UZ Leuven, Leuven, Belgium.
The breakout session "Imaging in Disease Assessment" featured six abstracts on imaging advancements for vasculitis. Disease extent on cranial MRI and its association with visual complications in giant cell arteritis (GCA) was evaluated, introducing the Propensity for Enhancement for GCA (P EG) score to assess inflammation. Predictors of remission and relapse in chronic periaortitis were analyzed, suggesting the potential for tailored treatment approaches.
View Article and Find Full Text PDFInt J Gen Med
March 2025
Medical Imaging Center, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, Shaanxi Province, People's Republic of China.
Background: Cervical cancer remains a major cause of mortality among women globally, with lymph node metastasis (LNM) being a critical determinant of patient prognosis.
Methods: In this study, MRI scans from 153 cervical cancer patients between January 2018 and January 2024 were analyzed. The patients were assigned to two groups: 103 in the training cohort; 49 in the validation cohort.
J Thorac Imaging
March 2025
Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University.
Purpose: To develop and validate an accurate computed tomography-based radiomics model for predicting high-grade (micropapillary/solid) patterns in T1-stage lung invasive adenocarcinoma (IAC) after propensity score matching (PSM).
Materials And Methods: We enrolled 546 participants from 2 cohorts with histologically diagnosed lung IAC after complete surgical resection between January 2020 and August 2021. The patients were divided into high-grade and non-high-grade groups and matched using PSM.
Acad Radiol
March 2025
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China. Electronic address:
This article reviews the state-of-the-art applications of quantitative magnetic resonance imaging (qMRI) in predicting and evaluating response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). HCC is a highly heterogeneous tumor, and its response to TACE varies significantly among patients. Early identification of treatment response is critical for optimizing management.
View Article and Find Full Text PDFAcad Radiol
March 2025
Department of Radiology, The Affiliated Huai'an Clinical College of Xuzhou Medical University, Huai'an, Jiangsu Province, China (Q.W., C.-C.H., H.-W.X., G.-J.B.). Electronic address:
Rationale And Objectives: Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therapy in breast cancer. This study aimed to develop and validate a deep learning (DL)-based decision-making visual biomarker system (DM-VBS) for predicting HER2 status using radiomics and DL features derived from magnetic resonance imaging (MRI) and mammography (MG).
Materials And Methods: Radiomics features were extracted from MRI, and DL features were derived from MG.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!