Abstract 2764: Maximizing high-risk incidental pulmonary nodule referrals using artificial intelligence.

Abstract Background: Effective lung cancer screening and pulmonary nodule (PN) management programs require timely detection and robust care coordination. We report the initial outcomes of an artificial intelligence (AI) tool for obtaining referrals to pulmonary following the detection of high-risk PN findings. Methods: JPS Health Network created the Pulmonary Nodule Risk Score (PNRS) classification system using nationally accepted PN guidelines. AI-driven Natural Language Processing (NLP) enabled navigators to identify patients meeting the PNRS 4B criteria (Table 1), the highest risk PN category. PNRS 4B detection and referral rates were compared over 22 months, spanning 11 months pre- and post-NLP implementation. Statistical analysis used Fisher’s Exact for categorical variables and Mann-Whitney U or t tests for continuous variables. Results: From June 1, 2023-April 25, 2024 (pre-NLP), 26,393 chest CTs were performed; 23,396 were obtained post-NLP (April 26, 2024-March 20, 2025). The pre- and post-NLP periods included 76 and 106 PNRS 4B patients, respectively. Post-NLP workflow significantly improved referral within 90 days rates from 55 (72%) to 98 (93%) (p<0.001). Appointment completion improved from 68% to 78% (p=0.17). Pre- and post-NLP comparisons demonstrated no significant differences in biopsy completion (65% vs 60%), Malignancy was noted in 28 (37%) vs 38 (37%). If pre-NLP referrals had reached 93% with the same 37% malignancy rate, an estimated 6 additional patients would have been diagnosed. Conclusion: The AI tool significantly increased referral rates and expedited malignancy detection, potentially reducing missed or delayed diagnoses in high-risk nodules. Citation Format: Ivan M. Marrufo, Raymond Johnson, Samuel Newman, Paras Patel, Kalyani Narra. Maximizing high-risk incidental pulmonary nodule referrals using artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2764.