Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial
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Published on: September 22, 2023
Federica Sabia1, Maurizio Balbi1,2, Roberta E Ledda1,2
1Department of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Insights
Fully automated coronary artery calcium (CAC) scoring effectively predicts 12-year all-cause mortality in lung cancer screening (LCS) participants. Higher CAC scores correlate with increased mortality risk, highlighting CAC
Area of Science:
- Cardiovascular Imaging and Diagnostics
- Thoracic Oncology and Screening
- Artificial Intelligence in Medicine
Background:
- Coronary artery calcium (CAC) is a recognized risk factor for cardiovascular events and mortality.
- CAC is not routinely assessed during low-dose computed tomography (LDCT)-based lung cancer screening (LCS).
- The MILD LCS trial provides a valuable cohort for investigating CAC's prognostic capabilities.
Purpose of the Study:
- To evaluate the efficacy of fully automated CAC scoring in predicting 12-year all-cause mortality.
- To assess the association between CAC levels and mortality risk within an LCS population.
- To explore the utility of AI-driven CAC scoring in a clinical screening setting.
Main Methods:
- Analysis of 2239 participants from the MILD LCS trial with baseline LDCT scans.
- Automated CAC scoring using artificial intelligence (AI) software, stratified into five risk groups.
- 12-year follow-up for all-cause and non-cancer mortality using Cox and Fine-Gray models.
Main Results:
- A significant increase in 12-year all-cause mortality was observed with rising CAC scores (3.2% for CAC=0 to 17% for CAC > 400).
- CAC > 400 was independently associated with a 3.80-fold increased risk of 12-year all-cause mortality (HR, 3.80 [95%CI, 1.35-10.74]).
- While CAC > 400 showed a univariate association with non-cancer mortality, this did not remain significant after adjustment.
Conclusions:
- Fully automated CAC scoring is a valuable tool for predicting 12-year all-cause mortality in LCS participants.
- Integrating CAC assessment into LCS could enhance risk stratification and patient management.
- AI-driven CAC analysis offers a feasible approach for routine cardiovascular risk assessment in screening programs.
Abstract:
Coronary artery calcium (CAC) is a known risk factor for cardiovascular (CV) events and mortality but is not yet routinely evaluated in low-dose computed tomography (LDCT)-based lung cancer screening (LCS). The present analysis explored the capacity of a fully automated CAC scoring to predict 12-year mortality in the Multicentric Italian Lung Detection (MILD) LCS trial. The study included 2239 volunteers of the MILD trial who underwent a baseline LDCT from September 2005 to January 2011, with a median follow-up of 190 months. The CAC score was measured by a commercially available fully automated artificial intelligence (AI) software and stratified into five strata: 0, 1-10, 11-100, 101-400, and > 400. Twelve-year all-cause mortality was 8.5% (191/2239) overall, 3.2% with CAC = 0, 4.9% with CAC = 1-10, 8.0% with CAC = 11-100, 11.5% with CAC = 101-400, and 17% with CAC > 400. In Cox proportional hazards regression analysis, CAC > 400 was associated with a higher 12-year all-cause mortality both in a univariate model (hazard ratio, HR, 5.75 [95% confidence interval, CI, 2.08-15.92] compared to CAC = 0) and after adjustment for baseline confounders (HR, 3.80 [95%CI, 1.35-10.74] compared to CAC = 0). All-cause mortality significantly increased with increasing CAC (7% in CAC ≤ 400 vs. 17% in CAC > 400, Log-Rank p-value <0.001). Non-cancer at 12 years mortality was 3% (67/2239) overall, 0.8% with CAC = 0, 1.0% with CAC = 1-10, 2.9% with CAC = 11-100, 3.6% with CAC = 101-400, and 8.2% with CAC > 400 (Grey's test p < 0.001). In Fine and Gray's competing risk model, CAC > 400 predicted 12-year non-cancer mortality in a univariate model (sub-distribution hazard ratio, SHR, 10.62 [95% confidence interval, CI, 1.43-78.98] compared to CAC = 0), but the association was no longer significant after adjustment for baseline confounders. In conclusion, fully automated CAC scoring was effective in predicting all-cause mortality at 12 years in a LCS setting.
