Incidence and resource burden for the management of computed tomography-detected ground glass opacities at a tertiary lung cancer service in the United Kingdom.
Abdullah AlShammari, Muhammad Ashraf, Paulo De Sousa, Vinci Naruka, Laura Tincknell, Anant Patel, Asonitis Athanasios, Sarah Booth, Jessica Wallen, Chiara Proli, Catherine Docherty, Eric Lim
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引用次数: 0
Abstract
Objectives: The increased use of computed tomography (CT) for lung cancer screening has significantly raised awareness of pure ground glass opacity (pGGO) lesions. GGOs pose diagnostic challenges, ranging from benign findings to invasive adenocarcinomas. We aim to evaluate the incidence of pGGOs identified on CT at a tertiary UK lung cancer service and quantify their impact on time and healthcare resources.
Methods: We conducted a retrospective review of patients with pGGOs discussed at multidisciplinary team (MDT) meetings between 2017 and 2019 at the Royal Free and Royal Brompton Hospitals. Demographics were recorded, and annual incidence along with management data was analyzed.
Results: A total of 3,731 patients were discussed at MDTs during the specified timeframe. On initial review 401 cases (11%) discussed were identified on CT as exhibiting areas of ground glass change, with the annual incidence increasing from 8% in 2017 to 13% in 2019. Following a second stage detailed assessment of the cases and application of exclusion criteria-such as the presence of a part solid component-259 patients were confirmed to have pure GGOs and were included for subsequent analysis in this study. The median number of MDT meetings per GGO was 2 (range: 1-3), with 3 CT scans (range: 2-4) over a median follow-up of 8.7 months (range: 2.0-24.1). PET scans were performed in 74 (27%) patients, 40 (15%) underwent biopsies (62.5% confirming malignancy), and 24 (9%) proceeded to surgery.
Conclusions: The rising incidence of GGOs presents a growing resource burden, with repeated MDT discussions, imaging, and interventions. Revised guidelines with improved risk stratification models are essential to optimize management, reduce unnecessary follow-up, and improve resource utilization in lung cancer services.