Background: Lung cancer (LC) causes the most cancer deaths in the US, but early detection improves outcomes. Uptake of Low-Dose CT screening (LDCT) has been low, contributing to race-based disparities. Programmatic management of incidentally detected pulmonary nodules (PN) provides a complementary avenue for early detection which could address disparities. In our PN cohort, Detecting Early LC in the Mississippi Delta (DELUGE), we provide broad access to early detection across a region fraught with geographic, socioeconomic, and race-based disparities. However, many detected PN will not result in LC. After successful implementation at the program level, we seek to evaluate individual-level factors important for early-detection. We evaluated how individuals with small PN identified in DELUGE differ between those who were and were not diagnosed with LC. Methods: DELUGE is a prospective cohort study where we systematically manage incidentally detected PN with trained navigators and evidence-based risk stratification. We constructed DELUGE in a community-based healthcare system across the mid-south with some of the highest per-capita LC incidence and mortality rates in the US. We evaluated all PN ≤30mm in DELUGE from 2015-2022. We compared clinical and demographic characteristics between individuals who were or were not diagnosed with LC within 24 months. Primary analysis used logistic regression, reporting odds ratios (OR) with 95% confidence intervals. Results: From 2015-2022, 17,945 individuals with PN ≤30 mm (71% of the total cohort of 25,339) were identified and enrolled in DELUGE. They had median age of 65 years, were 56% female, and 27% Black / 69% White. 853 (4.8%) patients were diagnosed with LC within two years. PN size significantly predicted LC diagnosis. For every 1 mm increase in nodule size the odds of LC increased by 15% (OR: 1.15 [1.14-1.16]); PN in an upper lobe (OR: 2.0 [1.7-2.2]) and those with cavitation also increased the odds of LC (OR: 2.9[1.9-4.2]). Sex, rural/urban residence, and number of PN were not associated with LC. The most predictive individual level characteristics were age, prior cancer history (OR: 1.3 [1.1-1.5], family cancer history (OR: 1.7 [1.5-2.0], COPD (OR: 3.3 [2.9-3.8]), and smoking history (OR: 6.6 [5.3-8.4]). In the multiple variable model, the most significant associations with LC diagnosis included PN size, age, insurance, family cancer history, COPD, and smoking history (all p<0.0001). This model could provide estimated sensitivity 0.81 and specificity 0.81 for predicting LC (AUC= 0.88). Conclusions: Factors measurable at baseline have predictive ability, and predictive modeling may be useful in management of small PN. More focused follow-up could improve programmatic efficiency, targeting the alleviation of geographic, socioeconomic, and race-based health disparities in this high poverty region of the US.