Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial

Federica Sabia, Maurizio Balbi, Roberta E. Ledda, Gianluca Milanese, Margherita Ruggirello, Camilla Valsecchi, Alfonso Marchianò, Nicola Sverzellati, Ugo Pastorino
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The present analysis investigates the predictive capacity of a fully automated coronary artery calcium (CAC) scoring system using artificial intelligence (AI) software (AVIEW, Coreline Soft, Seoul, Korea) in a low-dose computed tomography (LDCT)-based lung cancer screening (LCS) trial. The study includes 2239 participants from the Multicentric Italian Lung Detection (MILD) trial who underwent a baseline LDCT between September 2005 and January 2011, with a median follow-up of 190 months. The CAC scores were categorized into five strata, and their association with 12-year all-cause mortality and non-cancer mortality was analyzed. The results show that higher CAC scores, particularly CAC > 400, are associated with increased all-cause mortality and non-cancer mortality rates. The fully automated CAC scoring system effectively predicts all-cause mortality at 12 years in the LCS setting. These findings highlight the potential of automated CAC scoring in enhancing risk assessment and mortality prediction in LDCT-based LCS.


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