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Coronary artery calcium (CAC) is a known risk factor for cardiovascular events, but not yet routinely evaluated in Low Dose Computed Tomography (LDCT) screening. The present analysis compared the accuracy of a new automated CAC quantification versus prior manual quantification on baseline LDCT screening images as predictors of all-cause mortality at 12 years.The study included 1129 volunteers of the Multicentric Italian Lung Detection (MILD) trial who underwent a baseline LDCT scan from September 2005 to September 2006, already analyzed in a previous paper on CAC scoring. The initial manual CAC (mCAC) had been scored by one operator using a dedicated software, while the new automated CAC (aCAC) score was measured by a fully automated artificial intelligence software. All CAC scores were stratified in four categories: 0, 0.1- 19.9, 20-399, and ≥ 400.The study showed a high correlation between aCAC and mCAC scores, with an Intraclass Correlation Coefficient of 0.887. Of 613 negative mCAC score, 87.6% had aCAC score >0, and 14.0% >20. A CAC score >20 revealed a higher risk of 12-year all-cause mortality both with mCAC and aCAC. Focusing on the 535 individuals with false negative mCAC score, aCAC identified a subset of volunteers with a significantly poorer survival of 86% (aCAC 20-399, p=0.0007).CAC quantification could be accurately and safely performed with a fully automated software on baseline LDCT screening images to predict all-cause mortality risk.

Cytisine, a partial agonist-binding nicotine acetylcholine receptor, is a promising cessation intervention. We conducted a single-center, randomized, controlled trial (RCT) in Italy to assess the efficacy and tolerability of cytisine as a smoking cessation therapy among lung cancer screening participants.From July 2019 to March 2020, the Screening and Multiple Intervention on Lung Epidemics RCT enrolled 869 current heavy tobacco users in a low-dose computed tomography screening program, with a randomized comparison of pharmacologic intervention with cytisine plus counseling (N = 470) versus counseling alone (N = 399). The primary outcome was continuous smoking abstinence at 12 months, biochemically verified through carbon monoxide measurement.At the 12-month follow-up, the quit rate was 32.1% (151 participants) in the intervention arm and 7.3% (29 participants) in the control arm. The adjusted OR of continuous abstinence was 7.2 (95% confidence interval: 4.6-11.2). Self-reported adverse events occurred more frequently in the intervention arm (399 events among 196 participants) than in the control arm (230 events among 133 participants, p0.01). The most common adverse events were gastrointestinal symptoms, comprising abdominal swelling, gastritis, and constipation.The efficacy and safety observed in the Screening and Multiple Intervention on Lung Epidemics RCT indicate that cytisine, a very low-cost medication, is a useful treatment option for smoking cessation and a feasible strategy to improve low-dose computed tomography screening outcomes with a potential benefit for all-cause mortality.

Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.

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 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.