A Novel Automated Algorithm to Identify Lung Cancer Screening from Free Text of Radiology Orders.

Rustagi, Vali, Graham, Lum, Slatore, Keyhani. A Novel Automated Algorithm to Identify Lung Cancer Screening from Free Text of Radiology Orders. Journal of general internal medicine. 2025 May 1; 40(6):1306-1314, DOI: 10.1007/s11606-025-09429-2.

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Abstract:

BACKGROUND: Lung cancer screening (LCS) is recommended for asymptomatic patients. Administrative codes for LCS may capture tests prompted by signs/symptoms. OBJECTIVE: To validate an automated algorithm that identifies LCS among asymptomatic patients. DESIGN: In this cross-sectional study, an algorithm was iteratively developed to identify outpatient low-dose chest CT scans via Current Procedural Terminology (CPT) codes, search free text of radiology orders for screening terms and signs/symptoms (e.g., cough), and classify scans as screening or not. PARTICIPANTS: National population-based sample of 4503 adults ages 65-80 in Veterans Health Affairs primary care, with detailed smoking history to identify LCS-eligible individuals (30 + pack-years, current tobacco use, or quit  <  15 years prior). MAIN MEASURES: Algorithm specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) relative to manual chart review (gold standard) on 100% of screening scans and  >  10% random sample of non-screening scans. KEY RESULTS: Chart review was conducted on n  =  335 scans. The final algorithm could not classify 22% of scans, of which 73% were non-screening; these were excluded from primary analyses. Among 842 LCS-eligible individuals, the algorithm demonstrated 97% sensitivity (95%CI 91-99%) and 79% specificity (58-93%). Only 69% (61-77%) of scans classified as LCS via administrative codes were truly screening, compared to 95% of those classified as screening via the algorithm (p  <  0.001). Algorithm performance was similar regardless of LCS eligibility, with 90% PPV (84-94%) and 93% NPV (86-97%) in the overall population regardless of tobacco cigarette history. CONCLUSIONS: An automated algorithm can accurately identify screening versus diagnostic chest imaging, a necessary step to unbiased analyses of LCS in non-randomized settings. Studies should assess the accuracy of administrative codes for LCS in other health systems.