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http://dx.doi.org/10.21037/atm.2020.04.33 | DOI Listing |
Int J Chron Obstruct Pulmon Dis
September 2025
Department of Cardiovascular Center, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People's Republic of China.
Objective: This study aimed to develop and validate a deep learning radiomics (DLR) nomogram for individualized CHD risk assessment in the COPD population.
Methods: This retrospective study included 543 COPD patients from two different centers. Comprehensive clinical and imaging data were collected for all participants.
Sci Rep
September 2025
Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
In radiomics, feature selection methods are primarily used to eliminate redundant features and identify relevant ones. Feature projection methods, such as principal component analysis (PCA), are often avoided due to concerns that recombining features may compromise interpretability. However, since most radiomic features lack inherent semantic meaning, prioritizing interpretability over predictive performance may not be justified.
View Article and Find Full Text PDFJ Hepatocell Carcinoma
August 2025
Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China.
Purpose: To develop machine learning radiomics models for preoperative risk stratification of multifocal hepatocellular carcinoma (MHCC) beyond Milan criteria.
Methods: Patients with pathologically proven MHCC beyond Milan criteria between January 2015 and January 2019 were retrospectively included. Radiomic features were extracted from tumor, peritumor, and tumor-peritumor regions using multiparametric MRI (mpMRI).
Cardiovasc Diabetol
August 2025
Department of Radiology, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, China.
Background: Pericoronary adipose tissue (PCAT) radiomics derived from coronary computed tomography angiography (CCTA) for predicting major adverse cardiovascular events (MACE) in patients with acute coronary syndrome (ACS) remains unclear. This study aimed to assess whether PCAT radiomics could further provide complementary predictive value for the risk of MACE during long-term follow-up.
Methods: A multicenter retrospective study enrolled 777 subjects who underwent pre-intervention CCTA at 3 medical centers.
Sci Adv
August 2025
Department of Internal Medicine, Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul 03722, South Korea.
Papillary thyroid carcinoma (PTC) generally has a favorable prognosis; however, overtreatment persists because of the lack of reliable noninvasive risk stratification tools. This study developed a radiomics-based approach to enhance the preoperative assessment of PTC. Imaging features from 255 patients were analyzed, and three tumor clusters were identified via unsupervised clustering, with one cluster (Cluster 2) displaying favorable clinical and molecular profiles.
View Article and Find Full Text PDF