Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
We retrospectively compared the thin-section chest computed tomography (CT) findings between 25 patients of polymyalgia rheumatica (PMR) with rheumatoid arthritis (RA) and 29 patients of PMR without RA. PMR patients showed high-frequency CT abnormalities (68.5%) regardless of the association with RA. Ground-glass opacity (56% vs. 24%), traction bronchiectasis (44% vs. 3%), architectural distortion (32% vs. 0%), centrilobular nodules (32% vs. 7%), and honeycombing (20% vs. 0%) were significantly more common in the PMR with RA group than in the PMR without RA group (P<.01). PMR patients with RA have more increased prevalence of chest CT abnormalities than those without RA.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.clinimag.2015.11.013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.
Eur J Radiol Open
December 2025
Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, PR China.
Objective: To develop and validate a machine learning model based on CT radiomics to improve the ability to differentiate pathological subtypes of pulmonary ground-glass nodules (GGN).
Methods: A retrospective analysis was conducted on clinical data and radiological images from 392 patients with lung adenocarcinoma at Binzhou Medical University Hospital between January 1, 2020 to May 31, 2023. All patients underwent preoperative thin-section chest CT scans and surgical resection.
A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.
Quant Imaging Med Surg
June 2025
Department of Endoscopy and Laser, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Background: Chest thin-section computed tomography (TS-CT) has the potential to provide evidence for the prediction of lymph node metastasis (LNM) in synchronous multiple primary lung cancer (SMPLC). The present study aims to develop and validate a new CT-based multi-parametric decision tree algorithm (CT-DTA) model capable of accurate risk evaluation for LNM in SMPLC preoperatively.
Methods: A total of 235 patients with surgically resected SMPLC from Sun Yat-Sen University Cancer Center (SYSUCC), the First Affiliated Hospital of Sun Yat-Sen University (FAH-SYSU) and Sichuan Provincial People's Hospital (SPPH) were finally included.
Current State of Fibrotic Interstitial Lung Disease Imaging.
Radiology
July 2025
Department of Diagnostic Radiology, University of Chicago Medicine, Chicago, Ill.
Interstitial lung disease (ILD) diagnosis is complex, continuously evolving, and increasingly reliant on thin-section chest CT. Multidisciplinary discussion aided by a thorough radiologic review can achieve a high-confidence diagnosis of ILD in the majority of patients and is currently the reference standard for ILD diagnosis. CT also allows the early recognition of interstitial lung abnormalities, possibly reflective of unsuspected ILD and progressive in a substantial proportion of patients.
View Article and Find Full Text PDFMorphology-based radiological-histological correlation on ultra-high-resolution energy-integrating detector CT using cadaveric human lungs: nodule and airway analysis.
Eur Radiol
June 2025
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 5650871, Japan.
Objectives: To evaluate the depiction capability of fine lung nodules and airways using high-resolution settings on ultra-high-resolution energy-integrating detector CT (UHR-CT), incorporating large matrix sizes, thin-slice thickness, and iterative reconstruction (IR)/deep-learning reconstruction (DLR), using cadaveric human lungs and corresponding histological images.
Materials And Methods: Images of 20 lungs were acquired using conventional CT (CCT), UHR-CT, and photon-counting detector CT (PCD-CT). CCT images were reconstructed with a 512 matrix and IR (CCT-512-IR).
Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.
Korean J Radiol
August 2025
Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Objective: To evaluate the impact of deep learning-based image conversion on the accuracy of automated coronary artery calcium quantification using thin-slice, sharp-kernel, non-gated, low-dose chest computed tomography (LDCT) images collected from multiple institutions.
Materials And Methods: A total of 225 pairs of LDCT and calcium scoring CT (CSCT) images scanned at 120 kVp and acquired from the same patient within a 6-month interval were retrospectively collected from four institutions. Image conversion was performed for LDCT images using proprietary software programs to simulate conventional CSCT.