Lung cancer is the leading cause of mortality among oncological diseases worldwide. According to GLOBOCAN 2022 data, approximately 2.5 million new cases were recorded globally in 2022, of which 1.8 million were fatal. This accounts for almost one-fifth of all cancer-related deaths. Unfortunately, Georgia is not free from this burden; moreover, lung cancer represents one of the most critical oncological challenges in the country. According to GLOBOCAN 2022, it accounts for 13% of all new cancer cases in Georgia, while the age-standardized incidence rate in men reaches 35.7 per 100,000 population, exceeding the Eastern European average.
Data from the National Center for Disease Control and Public Health (NCDC) shows that about 90% of patients are already at stages III–IV at the time of diagnosis; 60% of them are diagnosed at stage IV, and the disease is detected at an early stage in only 10% of cases. An analysis published in the Journal of Thoracic Oncology (2020) defines the implementation of lung cancer screening in Georgia in this very context as one of the main priorities for reducing mortality. The first step toward this has already been taken: starting May 1, a free lung cancer screening program was launched in Tbilisi.
Why Wasn’t Chest Radiography Enough?
For decades, chest radiography (X-ray) was considered the primary method for lung cancer screening. However, large-scale studies, including the PLCO trial (with over 154,000 participants), failed to show a significant reduction in lung cancer mortality. The main problem was the low sensitivity of the method. Small nodules often went unnoticed, and the disease was revealed at a late stage.
This limitation was overcome by Low-Dose Computed Tomography (LDCT). The method uses a radiation dose of approximately 1.5 mSv, which is 75-90% lower than a standard CT scan, and provides a high-resolution image of the lungs. As a result, it is possible to detect small nodules years before symptoms develop. The effectiveness of LDCT has been proven by large-scale randomized trials. The US National Lung Screening Trial (NLST), which involved over 53,000 people, showed a 20% reduction in lung cancer mortality. The NELSON trial recorded a 24% reduction, and the MILD trial showed a reduction of up to 39%. These data have made LDCT the modern standard for screening high-risk populations.
The Main Challenge: Engaging the High-Risk Population
Knowing the effectiveness of LDCT and actually delivering it to high-risk groups are two different challenges. Smokers, the elderly, and socially vulnerable individuals are often the least likely to participate in screening. Barriers include distance, costs, fear, and distrust in the medical system.
To address this problem, the Manchester Lung Health Check program was launched in Manchester in 2016, becoming one of the first practical models of lung cancer screening within the NHS. Its main innovation was bringing the service directly to the patient. Mobile LDCT scanners—trucks—were placed in the parking lots of shopping centers in socially vulnerable neighborhoods. Individuals aged 55–74 with a history of smoking underwent a “lung health check,” which included a clinical assessment, spirometry, BMI determination, a consultation, and a 6-year risk assessment using the PLCOm2012 model. In cases of a ≥1.51% risk, an LDCT was performed during the same visit. According to data published in Thorax in 2019, 75% of the 2,541 participants belonged to a low socioeconomic status group. Out of 1,384 screened participants, lung cancer was detected in 3%; 80% of these were at an early stage, and 65% underwent surgical treatment.
Scale and Artificial Intelligence
The Manchester model laid the foundation for the NHS targeted national lung health check program, the large-scale implementation of which began in 2019. By January 2025, there were 43 active centers operating in England. Within the program, more than 712,000 LDCTs were performed. Over 6,200 lung cancer cases were detected, 75% of which were at stages I–II. An analysis published in Nature Medicine confirmed that early-stage detection of lung cancer in high-risk groups significantly improved.
Artificial intelligence (AI) has become a crucial tool in this process. The Annalise.AI system implemented at Wythenshawe Hospital automatically analyzes images and returns results in less than a minute. According to NHS England North West data, the system has prevented more than 1,400 unnecessary CT scans and over 1,000 urgent referrals. Currently, it processes more than 40,000 chest images monthly.
What Does This Mean for Countries Without Screening?
The UK’s experience, from mobile scanners to a national program, represents a practical model for countries where LDCT screening is just being introduced or is in the development phase. Its key components are validated risk models for identifying high-risk groups, community-oriented delivery systems, standardized protocols for nodule management, and the use of AI for massive image analysis.
For Georgia, this experience is especially important. Against the backdrop of a high risk of lung cancer and high cigarette consumption, an LDCT-based screening program has already been introduced in the country, which is a significant step toward developing early diagnostics. Today, the main task is no longer just having the evidence, but the effective implementation of the program, the full engagement of high-risk groups, and ensuring quality control. Manchester’s experience shows that successful screening is the prerequisite for detecting lung cancer at an early stage.
Bray F, et al. GLOBOCAN 2022, CA Cancer J Clin 2024; Pataraia A, et al. Lung Cancer in Georgia, J Thorac Oncol 2020; Crosbie PA, Balata H, et al. Thorax 2019; Crosbie PA, Balata H, et al. Thorax 2019;Balata H, et al. Lung Cancer 2021; Goodley P, Balata H, et al. BMJ Oncology 2024; Goodley P, Balata H, et al. J Thorac Oncol 2025; Nature Medicine 2026; NHS England Lung Cancer Screening Programme National Data,NHS England North West, 2025; NLST Research Team, NEJM 2011; de Koning HJ, et al. NEJM 2020; Pastorino U, et al. Annals of Oncology 2019.
Author: Anna Jghamadze, MD, Radiation Oncology Resident
