Retrospective analysis shows machine learning improves CT-based airway and emphysema mapping

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medRxiv Published April 27, 2026 Medically reviewed May 27, 2026 Study authors: Namvar, A.; Shan, B.; Hoff, B.; Labaki, W. W.; Murray, S.; Bell, A. J.; Galban, S.; Kazerooni, E. A.… DOI ↗ By Dr. Lars van Dijk, PhD · Surgical, Procedural & Diagnostic

Key Takeaway

Note that PRMD may offer more reliable emphysema and small airways disease quantification than standard PRM in noisy imaging.

This retrospective analysis investigated the performance of Feature-Based Parametric Response Mapping (PRMD) using wavelet scattering convolution networks and machine learning on paired inspiratory-expiratory CT scans. The study compared this approach to conventional density-based parametric response mapping (PRM) in 8,972 tobacco-exposed participants, including those with normal spirometry, PRISm, and GOLD 1-4 COPD.

The researchers found that PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98). Notably, PRMD demonstrated stronger correlations with FEV1 for both emphysema (r = -0.54, P < 0.0001) and functional small airways disease (r = -0.51, P < 0.0001) compared to standard PRM (r = -0.42 for both, P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by approximately 15% (P < 0.001), whereas PRMD limited error to < 5% (P < 0.001).

While the results suggest PRMD provides a noise-resilient alternative for classifying emphysema and fSAD, which may enhance reliability for low-dose imaging or multi-center studies, the study limitations were not reported. The findings are based on a retrospective analysis of the association between imaging features and pulmonary function.

Study Details

Sample sizen = 3,872

EvidenceLevel 5

PublishedApr 2026

View Original Abstract ↓

Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution networks and machine learning to spatially detect and quantify functional small airways disease (fSAD) and emphysema on paired inspiratory-expiratory CT scans, with enhanced noise robustness. Materials and Methods: In this retrospective analysis of prospectively acquired data (2007-2017), we developed and validated a deep learning-based PRM approach using paired CT scans from 8,972 tobacco-exposed COPDGene participants ([≥]10 pack-years; mean age 60.1 {+/-} 8.8 years; 46.5% women), including controls with normal spirometry (n = 3,872; controls), PRISm (n = 1,089), GOLD 1-4 COPD (n = 4,011). Data were stratified into training, validation, and testing sets (24:6:70). PRMD extracts translation-invariant image features using a wavelet scattering network and applies a subspace learning classifier to classify voxels as emphysema or non-emphysematous air trapping (fSAD). PRMD was compared with conventional density-based PRM for voxel-wise agreement, correlation with pulmonary function, robustness to noise, and sensitivity to misregistration using Pearson correlation, Bland-Altman analysis, and paired t tests. Results: PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV1; (emphysema: r = - 0.54; fSAD: r = - 0.51; P < 0.0001) than standard PRM (r = - 0.42 for both; P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by ~15%, whereas PRMD limited error to < 5% (P < 0.001). Conclusion: PRMD provides an interpretable, feature-driven and noise-resilient alternative to traditional PRM for emphysema and fSAD classification, enhancing the reliability of CT-based COPD phenotyping for multi-center studies and low-dose imaging applications.