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Background In patients with idiopathic pulmonary fibrosis (IPF) there is a need to identify biomarkers that 1) are associated with increased risk of adverse outcome and 2) can be used to monitor treatment response or identify disease progression over time. Methods Two consecutive cohorts of patients with IPF were accessed from the Open Source Imaging Consortium database. Automated computed tomography (CT) biomarkers of disease severity incorporating fibrotic and pulmonary vascular features (the reticulovascular score and weighted reticulovascular score (WRVS)) were studied. Relationships between imaging biomarkers, lung function and survival were analysed. Results In separate test and validation cohorts, 168 and 176 patients with IPF respectively (median survival 2.6 years) were studied. A threshold of WRVS ≥15% at baseline CT was most strongly associated with transplant-free survival (HR 3.00, 95% CI 1.47–6.10, p=0.002) when adjusted for baseline forced vital capacity (FVC) and age. In patients with 12-month follow-up CT and lung function tests (n=89) an increase in 3% of WRVS (the minimal clinically important difference) was also significantly associated with reduced survival independent of FVC, and outperformed visual evaluation of progressive fibrosis. Conclusions WRVS is an automated CT biomarker which can identify patients with IPF at increased risk of progression and is able to reliably capture disease progression over time.

Summary: Background: The proper management of suspicious radiologic findings is crucial to optimize the effectiveness of low-dose computed tomography (LDCT) lung cancer screening trials. In the BioMILD study, we evaluated the utility of combining a plasma 24-microRNA signature classifier (MSC) and LDCT to define the individual risk and personalize screening strategies. Here we aim to assess the utility of repeated MSC testing during annual screening rounds in 1024 participants with suspicious LDCT findings. Methods: The primary outcome was two-year lung cancer incidence in relation to MSC test results, reported as relative risk (RR) with 95% confidence interval (CI). Lung cancer incidence and mortality were estimated using extended Cox models for time-dependent covariates, yielding the respective hazard ratios (HR). Clinicaltrials.gov ID: NCT02247453. Findings: With a median follow-up of 8.5 years, the full study set included 1403 indeterminate LDCT (CTind) and 584 positive LDCT (CT+) results. A lung cancer RR increase in MSC+ compared to MSC- participants was observed in both the CTind (RR: 2.5; 95% CI: 1.4–4.32) and CT+ (RR: 2.6; 95% CI: 1.81–3.74) groups and was maintained when considering stage I or resectable tumors only. A 98% negative predictive value in CTind/MSC− and a 30% positive predictive value in CT+/MSC+ lesions were recorded. At seven years’ follow-up, MSC+ participants had a cumulative HR of 4.4 (95% CI: 3.0–6.4) for lung cancer incidence and of 8.1 (95% CI: 2.7–24.5) for lung cancer mortality. Interpretation: Our study shows that MSC can be reliably performed during LDCT screening rounds to increase the accuracy of lung cancer risk and mortality prediction and supports its clinical utility in the management of LDCT findings of uncertain malignancy. Funding: Italian Association for Cancer Research; Italian Ministry of Health; Horizon2020; National Cancer Institute (NCI); Gensignia LifeScience.

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.