Comparison of Two Deep Learning Models and Their Combination for 1-year Lung Cancer Detection in Two Screening Cohorts.

Published in Radiology. Cardiothoracic imaging. Abstract: Purpose To evaluate the performance of Sybil, a Medical Open Network for Artificial Intelligence (MONAI)-based nodule model, and their combination for 1-year lung cancer detection in two screening cohorts: the National Lung Screening Trial (NLST) and the Pamplona International Early Lung Cancer Action Program (P-IELCAP). Materials and Methods Baseline low-dose CT (LDCT) examinations from the NLST and the P-IELCAP were analyzed. A prespecified OR-stacked composite model combining Sybil, a MONAI-based nodule model, and a safety-net rule (MONAI-detected nodule > 6 mm with detection confidence > 0.95) was developed and evaluated for 1-year lung cancer detection. Model discrimination was assessed by using area under the receiver operating characteristic curve (AUC), and operating-point performance metrics were calculated. Results A total of 5110 baseline LDCT examinations were analyzed, including 2343 from NLST and 2767 from P-IELCAP, with 65 lung cancers diagnosed within 1 year. Mean age, mean pack-years, and percentage of female participants were 61.6 years ± 5.1 (SD), 57.3 pack-years ± 24.8, and 39.4% (923 of 2343), respectively, in NLST and 56.0 years ± 9.5, 34.4 pack-years ± 21.5, and 28.6% (791 of 2767), respectively, in P-IELCAP. Lung cancer incidence (per 1000 person-years) was higher in NLST than in P-IELCAP (17.7 vs 8.7; P = .006). Mean Sybil 1-year lung cancer probabilities (scaled by 1000 for presentation) were also higher in NLST than in P-IELCAP (16.4 vs 9.9; P < .01...