深層学習モデルによる結節のインデックス付けと早期肺がん検出への応用

To evaluate whether a deep learning-based AI system with suspected nodule indexing and malignancy risk stratification improves radiologist performance in detecting pulmonary nodules on CT, using a dataset enriched with challenging early-stage lung cancers.

The study comprised a standalone AI sensitivity-specificity analysis and a two-arm crossover reader study with 16 American board-certified radiologists. Each reader interpreted 340 CT scans with and without AI, separated by a one-month washout. The dataset included 209 screening and 131 non-screening cases: 133 with lung cancer, 61 with benign non-calcified nodules ≥4 mm, and 146 normal. To enrich subtle lesions, 64 of 91 (70.3%) small cancer cases were drawn from early-round NLST CT scans. Localization-specific ROC (LROC) analysis was used to assess radiologist performance.

Standalone AI achieved a sensitivity of 0.804 at 1.37 false positives per case. With AI assistance, radiologists' LROC AUC improved cancer detection (0.761 vs. 0.652; ΔAUC = 0.109, 95% CI: 0.067, 0.152) and for all nodules (0.830 vs. 0.734; ΔAUC = 0.096, 95% CI: 0.059, 0.133). Mean sensitivity increased from 0.585 to 0.727, while specificity remained essentially unchanged (0.918 vs. 0.913). Interpretation time decreased by 12.9%, from a mean of 133 to 115.9 seconds (Difference = -17.1 seconds (95% CI: -26.7, -9.0)). AI alerts enabled detection of early-stage cancer detection previously missed in NLST interpretations.

The AI system significantly improved radiologist's performance in pulmonary nodule detection, with consistent benefits across nodule types, screening contexts, and experience levels; supporting its integration into routine chest CT interpretation workflows.