Objectives: Clinical data and cost-effectiveness analyses from several countries support the use of low-dose computed tomography (LDCT) to screen patients with high risk of lung cancer (LC). This study aimed to explore the economic value of screening LC with LDCT in Hungary.
Methods: Cohorts of screened and nonscreened subjects were simulated in a decision analytic model over their lifetime. Five steps in the patient trajectory were distinguished: no LC, nondiagnosed LC, screening, diagnosed LC, and post-treatment. Patient pathways were populated based on the Hungarian pilot study of screening, the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) LC screening trial, and local incidence and prevalence data. Healthcare costs were obtained from the National Health Insurance Fund. Utility data were obtained from international sources and adjusted to local tariffs. Scenarios according to screening frequency, age bands (50-74, 55-74 years), and smoking status were analyzed.
Results: Annual LDCT-based screening compared with no screening for 55- to 74-year-old current smokers showed 0.031 quality-adjusted life-year (QALY) gains for an additional €137, which yields €5707 per QALY. Biennial screening for the same target population showed that purchasing 1 QALY would cost €10 203. The least cost-effective case was biennial screening of the general population aged 50 to 74 years, which yielded €37 931 per QALY.
Conclusions: Screening LC with LDCT for a high-risk population could be cost-effective in Hungary. For the introduction of screening with LDCT, targeting the most vulnerable groups while having a long-term approach on costs and benefits is essential.
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http://dx.doi.org/10.1016/j.vhri.2022.10.002 | DOI Listing |
J Health Econ Outcomes Res
September 2024
Avalon Health Economics, Coral Gables, Florida, USA.
Early detection of lung cancer is crucial for improving patient outcomes. Although advances in diagnostic technologies have significantly enhanced the ability to identify lung cancer in earlier stages, there are still limitations. The alarming rate of false positives has resulted in unnecessary utilization of medical resources and increased risk of adverse events from invasive procedures.
View Article and Find Full Text PDFBMJ Open
December 2024
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Introduction: Early lung cancer screening (LCS) through low-dose CT (LDCT) is crucial but underused due to various barriers, including incomplete or inaccurate patient smoking data in the electronic health record and limited time for shared decision-making. The objective of this trial is to investigate a patient-centred intervention, MyLungHealth, delivered through the patient portal. The intervention is designed to improve LCS rates through increased identification of eligible patients and informed decision-making.
View Article and Find Full Text PDFCureus
December 2024
Family Health Unit New Directions, Unidade Local de Saúde do Alto Ave, Vizela, PRT.
Lung cancer is highly prevalent worldwide and is the leading cause of cancer-related death in Portugal. There is increasing evidence that low-dose computed tomography (LDCT) screening reduces mortality; however, few countries have implemented screening strategies. This review aims to gather the best evidence to assess the relevance of implementing lung cancer screening.
View Article and Find Full Text PDFJ Thorac Oncol
January 2025
Department of General Internal Medicine and Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Hypothesis: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed-tomography (LDCT).
Methods: We developed a comorbidity index (PLCO-ci) using LCS-eligible participants' data from the Prostate Lung Colorectal and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts 5-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated by the area under the ROC curve (ROC).
Eur Radiol
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Objectives: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.
Materials And Methods: A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.
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