Background and Objectives: Medical imaging is a key element in the clinical workup of patients with suspected oncological disease. In Hungary, due to the high number of patients, waiting lists for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were created some years ago. The Municipality of Budapest and Semmelweis University signed a cooperation agreement with an extra budget in 2020 (HBP: Healthy Budapest Program) to reduce the waiting lists for these patients. The aim of our study was to analyze the impact of the first experiences with the HBP. Material and Methods: The study database included all the CT/MRI examinations conducted at Semmelweis University with a referral diagnosis of suspected oncological disease within the first 13 months of the HBP (6804 cases). In our retrospective, two-armed, comparative clinical study, different components of the waiting times in the oncology diagnostics pathway were analyzed. Using propensity score matching, we compared the data of the HBP-funded patients (n = 450) to those of the patients with regular care provided by the National Health Insurance Fund (NHIF) (n = 450). Results: In the HBP-funded vs. the NHIF-funded patients, the time interval from the first suspicion of oncological disease to the request for imaging examinations was on average 15.2 days shorter (16.1 vs. 31.3 days), and the mean waiting time for the CT/MRI examination was reduced by 13.0 days (4.2 vs. 17.2 days, respectively). In addition, the imaging medical records were prepared on average 1.7 days faster for the HBP-funded patients than for the NHIF-funded patients (3.4 vs. 5.1 days, respectively). No further shortening of the different time intervals during the subsequent oncology diagnostic pathway (histological investigation and multidisciplinary team decision) or in the starting of specific oncological therapy (surgery, irradiation, and chemotherapy) was observed in the HBP-funded vs. the NHIF-funded patients. We identified a moderately strong negative correlation (r = −0.5736, p = 0.0350) between the CT/MR scans requested and the active COVID-19 case rates during the pandemic waves. Conclusion: The waiting lists for diagnostic CT/MR imaging can be effectively shortened with a targeted project, but a more comprehensive intervention is needed to shorten the time from the radiological diagnosis, through the decisions of the oncoteam, to the start of the oncological treatment.
Purpose To evaluate morphological and metabolic findings in novel coronavirus 19 disease (COVID-19) with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (FDG-PET/CT). Materials and methods This was a single-centre, prospective clinical trial enrolling consecutive patients who required hospitalisation due to COVID-19 infection. All patients underwent routine chest CT on admission and a follow-up FDG-PET/CT scan on the 7th day of hospitalisation. COVID-19 related lung alterations, such as ground-glass opacity (GGO) and consolidation were quantified with semi-automated software using deep learning (DL) and metabolic parameters were expressed with PET-based metabolic inflammatory volume (MIV) and total inflammatory activity (TIA). The primary outcome was defined as increased inflammatory state on PET scan, with the median MIV and TIA being the cut-off value. Results Forty-four patients were enrolled (25 men; median [IQR] age: 52 [49-61] years). The median [IQR] MIV and TIA were 209 [73-517] ml and 499 [155-1429], respectively. The percentage of GGO and total lung CT severity scores at baseline CT showed weak correlation with MIV and TIA (r=0.33-0.39; p=0.13-0.34). At follow-up, we detected a strong correlation between all chest CT abnormalities and MIV and TIA (r=0.77; p<0.01 and r=0.75; p<0.01, respectively), as well as between CT severity scores and MIV and TIA (r=0.77; p<0.01 and r=0.75; p<0.01, respectively). Logistic regression analysis adjusted for demographics revealed that the extent of chest CT abnormalities on follow-up was an independent predictor of high inflammatory state (OR [by 1% change] =1.11 for both MIV and TIA; p=0.018 for MIV and p=0.021 for TIA). Also, a model encompassing CT abnormalities, interleukin-6 and lactate-dehydrogenase levels at follow-up showed high predictive values for inflammatory state, with an area-under-the-curve (AUC) on receiver operating characteristics analysis of 0.88. Conclusion The metabolic inflammatory volume and activity of COVID-19-pneumonia showed good correlation with morphological changes on CT imaging performed 7 days after patient hospitalization. Combining CT and laboratory data (lactate dehydrogenase and interleukin-6 levels), FDG-PET-based lung inflammatory status could effectively be predicted. Trial registration: www.clinicaltrials.gov (ID: NCT05009563). Registered 17 August 2021 (retrospectively registered), first patient enrolled: 13 January 2021.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.