This study retrospectively analyzed the CT scans of 1524 patients who underwent lobectomy or pneumonectomy for non-small-cell lung cancer (NSCLC) from January 2015 to December 2019. Utilizing the AVIEW ILA software, a commercially available deep-learning-based automated quantification tool, these scans were evaluated for interstitial lung abnormality (ILA) as per the Fleischner Society's definition. Patients were divided into normal, ILA, and interstitial lung disease (ILD) groups. Results demonstrated that ILA and ILD were linked with poor recurrence-free survival (RFS) and overall survival (OS). Further, both fibrotic and non-fibrotic ILA components were correlated with poor RFS and OS in normal and ILA groups. The study thus underscores the prognostic value of automated CT quantification of ILA, providing valuable information for post-surgery surveillance, especially in patients with a significant extent of quantified ILA.