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Background: Although radiomics features of the left ventricular wall have been used to assess cardiac diseases, radiomics features of the cardiac blood pool have been relatively ignored.
Purpose: To test the hypothesis that cine MRI-derived radiomics features of the cardiac blood pool are associated with cardiac function and motion.
Study Type: Retrospective.
Population: A total of 26 healthy volunteers (51.2 ± 15.6 years; 17 males).
Field Strength/sequence: A 1.5 T/balanced steady-state free precession (bSSFP).
Assessment: The radiomics features (107 features in seven classes) of the blood pool of the left/right ventricle/atrium (LV/RV/LA/RA) were extracted on four-chamber cine images (25 phases). Conventional cardiac function parameters (volumes, ejection fraction [EF] and longitudinal strain) were assessed in each cardiac chamber. Intraobserver- and interobserver agreements of radiomics features of all chambers acquired at all phases were assessed, as well as scan-rescan agreement in a subset of 13 volunteers.
Statistical Tests: Pearson correlation coefficients (r) were used to assess the associations between peak values of radiomics features and end-diastolic (or maximal) volume, end-systolic (or minimal) volume, EF, and longitudinal strain of corresponding chambers. Good intraobserver, interobserver, and scan-rescan agreements for radiomics features acquired were defined as intraclass correlation coefficient (ICC) > 0.7 or coefficient of variation (CoV) < 20%.
Results: Most radiomics features of the blood pool varied periodically throughout the cardiac cycle. Peak values of chamber-specific blood pool radiomics features were correlated with traditional cardiac function and motion indices of corresponding chambers (r: 0.4-0.87). Ninety-three (87%), 86 (80%), and 73 (68%) radiomics features demonstrated good intraobserver, interobserver, and scan-rescan reproducibility, respectively.
Conclusion: Cine MRI-derived radiomics features within LV/RV/LA/RA are associated with traditional cardiac function and motion indices of corresponding chambers and may have the potential to become novel quantitative imaging biomarkers in cardiovascular medicine.
Evidence Level: 3.
Technical Efficacy: 1.
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http://dx.doi.org/10.1002/jmri.28572 | DOI Listing |
J Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.
Eur Radiol
September 2025
Quantitative Imaging Biomarkers in Medicine, Quibim, Valencia, Spain.
Objectives: In non-small cell lung cancer (NSCLC), non-invasive alternatives to biopsy-dependent driver mutation analysis are needed. We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status.
Materials And Methods: A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team.
Radiother Oncol
September 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address:
Purpose: To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma (PCa) treated with androgen deprivation therapy (ADT) and external radiotherapy using clinical factors and radiomics extracted from primary tumor and node volumes in pre-treatment PSMA PET/CT scans.
Materials/methods: Our cohort includes 134 PCa patients (nodal involvement in 28 patients). Gross tumor volumes of primary tumor (GTVp) and nodes (GTVn) on CT and PET scans were segmented.
Radiother Oncol
September 2025
Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, China. Electronic address:
Background: Accurate delineation of regions of interest (ROIs) is critical for feature extraction and selection in radiomics-based prediction models.
Purpose: To develop a combined dosiomics and deep learning (DL) model for predicting grade ≥ 2 radiation esophagitis (RE) in lung cancer patients undergoing radiotherapy, we propose a multi-task auxiliary learning approach to define accurate and objective ROIs based on radiation dose distribution (RDD) images.
Materials And Methods: Lung cancer patients who underwent radiotherapy were gathered retrospectively from hospital 1 (January 2020 and December 2022) for model development.