Segmentation-based deep learning emphysema quantification using chest CT: improved accuracy and robustness vs LAA-950.

Published in European radiology. Abstract: To develop a deep learning segmentation algorithm to enable accurate, reliable emphysema quantification while improving agreement with radiologist assessments and pulmonary function tests. The model was developed using retrospective virtual and clinical datasets. Virtual data enabled pre-training using ground truth across controlled parameters, including scanners, doses, and reconstruction kernels, while clinical data enabled fine-tuning with expert-annotated emphysema masks. Segmentation accuracy was quantified using the Dice coefficient, and robustness was quantified using emphysema percentage consistency across imaging conditions. Model-based emphysema percentage was correlated with Fleischner visual scores (ordinal; 0-5) and pulmonary function tests (DLCO, FEV 1 pp, and FEV1/FVC) and compared to LAA-950. Statistical analysis included univariate/multivariate correlations. Quantitative assessment included Dice, bias, limits of agreement, and reproducibility coefficient. Virtual data included 20 human models (mean age: 43 years ± 11 [SD], 10 men), and clinical data included multi-center cohorts of 101 patients (C1; 57 years ± 8, 54 men), 23 patients (C2; 57 years ± 7, 14 men), and 1159 patients (C3; 65 years ± 9, 586 men). The model outperformed LAA-950 in segmentation accuracy, achieving higher Dice scores across virtual (76.6% ± 8.8 vs 51.5% ± 23.5), C1 (48.4% ± 24.2 vs 22.5% ± 20.2), and C2 (64.8% ± 9.3 vs 32.7% ± 16.8) cohorts. Furthermore, analysis showed improved bias ...