ArticleJournal of imaging informatics in medicine2026
AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The purpose of the study is to investigate the potential of artificial intelligence (AI)-driven analysis of preoperative chest radiograph (CXR) for predicting postoperative outcome in patients with early-stage non-small cell lung cancer (NSCLC). We retrospectively enrolled 416 consecutive patients (mean age, 65.6 years ± 9.9; 197 men) who underwent curative surgical resection for pathological stage 1 NSCLC at two referral hospitals between March 2020 and February 2021. AI-driven preoperative CXR analysis was performed for the detection of four abnormalities. The lesion detectability on CXR by AI analysis (abnormality score threshold of ≥ 15) was assessed. Cox proportional hazards regression analyses were performed to determine predictors of recurrence-free survival (RFS), and the performance of prognostic models based on clinical variables and AI results was compared to models based on clinical variables and pathologic tumor size/preoperative CT parameters. AI-based abnormality score was median 39.1% (interquartile range, 4.0-82.0). During a follow-up period of 1060 ± 200.7 days, 34 patients (8.2%) experienced recurrence. Both AI detectability and AI abnormality score were significant independent risk factors for poor RFS (hazard ratio 7.201 [95% CI 2.533-20.470] and 1.029 [95% CI 1.016-1.042] per 1% increase in score, p < 0.001). A multivariable prognostic model based on AI abnormality score showed comparable performance (c-index 0.795) to the models based on pathologic tumor size or CT parameters (c-index 0.794). Adding AI abnormality score to CT-derived tumor size significantly improved discrimination performance compared with the model using CT parameters (c-index 0.837 vs. 0.821, p < 0.001). AI-driven analysis of preoperative CXR can enhance the preoperative prediction of postoperative prognosis in patients undergoing surgical resection for early-stage NSCLC.
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