2022 World Conference on Lung Cancer (ePosters)-EP01.05-006. Influenza Season Influence on Incidence and Outcome of Nodules in the NELSON Trial

EP01.05-006. Influenza Season Influence on Incidence and Outcome of Nodules in the NELSON Trial

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The study aimed to investigate the impact of the influenza season on the outcome of new nodules in the NELSON trial. Participants with 1 new nodule were included, and the winter season was compared to the summer season. The outcome of the new nodules and the number of new nodules matched to lung cancer were analyzed. Logistic regression analyses were conducted to examine the seasonal influence.<br /><br />A total of 820 new nodules were reported in 529 participants, with 59% reported in winter and 41% reported in summer. Of the new nodules reported in summer, 67% were resolving compared to 63% in winter. Among the 23 screen-detected cancers, 17 were reported in winter.<br /><br />However, logistic regression analyses did not show a significant association between the season of the year and the outcome of new nodules or the number of screen-detected cancers. Therefore, the study suggests that all new nodules should be treated with equal attention, regardless of the season.<br /><br />The predictor variables analyzed in the study included season (summer), age, smoking status (former), and gender (female). The results showed that only gender had a statistically significant association with nodule outcome.<br /><br />In conclusion, this study found no significant association between the influenza season and the outcome of new nodules in the lung cancer screening population. Therefore, the recommendation is to treat all new nodules with the same careful attention, irrespective of the season.

Asset Subtitle

Harriet Louise Lancaster

Meta Tag

Speaker Harriet Louise Lancaster

Topic Early Detection and Screening - Pulmonary Nodule

Keywords

influenza season

NELSON trial

new nodules

winter season

summer season

outcome

lung cancer

logistic regression analyses

screen-detected cancers

predictor variables