Targeted dose CT lung cancer screening does not have substantial effect on life-years saved among high risk groups.
Targeting low-dose computed tomography (LDCT) for lung cancer screening for people at highest risk for lung cancer mortality does not offer substantial gains in terms of life-years saved, quality-adjusted life-years (QALYs), or cost-effectiveness, according to a study published in Annals of Internal Medicine.
Researchers from the United States and the Netherlands sought to quantify the value of risk-targeted selection for lung cancer screening compared with National Lung Screening Trial eligibility criteria. The researchers analyzed incremental seven-year mortality, life expectancy, quality-adjusted life-years (QALYs), costs, and cost-effectiveness of screening with LDCT versus chest radiography at each decile of lung cancer mortality risk.
The results showed participants at greater risk for lung cancer mortality were older and had more comorbid conditions and higher screening-related costs. Lung cancer was found in more high-risk patients and targeted screening averted lung cancer deaths over the seven years of the trial, but those at higher risk were also older, had greater smoking exposure, and were more likely to have a preexisting diagnosis of chronic obstructive pulmonary disease. The incremental lung cancer mortality benefits during the first seven years ranged from 1.2 to 9.5 lung cancer deaths prevented per 10,000 person-years for the lowest to highest risk deciles, respectively.
The gradient of benefits across risk groups was attenuated in terms of life-years (extreme decile ratio, 3.6) and QALYs (extreme decile ratio, 2.4). The incremental cost-effectiveness ratios were similar across risk deciles ($75,000 per QALY in the lowest risk decile to $53,000 per QALY in the highest risk decile).
The researchers concluded that although risk targeting may improve screening efficiency in terms of early lung cancer mortality per person screened, the gains in efficiency are attenuated and modest in terms of life-years, QALYs, and cost-effectiveness.
Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?
June 19th 2025Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.
Can AI Predict Future Lung Cancer Risk from a Single CT Scan?
May 19th 2025In never-smokers, deep learning assessment of single baseline low-dose computed tomography (CT) scans demonstrated a 79 percent AUC for predicting lung cancer up to six years later, according to new research presented today at the American Thoracic Society (ATS) 2025 International Conference.