Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD

PurposeTo 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 MethodsIn 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 ([&ge;]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. ResultsPRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV (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). ConclusionPRMD 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. Key PointsO_LIThis study introduces combined wavelet scattering and subspace learning for medical image segmentation, enabling accurate, interpretable voxel-level classification of emphysema and functional small airways disease on paired CT scans. C_LIO_LIThe proposed Deep Parametric Response Mapping method demonstrated 95% voxel-wise agreement with standard Parametric Response Mapping and stronger correlations with spirometric measures, enhancing the clinical relevance of CT-based phenotyping for Chronic Obstructive Pulmonary Disease. C_LIO_LIDeep Parametric Response Mapping significantly improved robustness to image noise--reducing overestimation of emphysema and functional small airways disease from [~]15% to <5% (P < 0.001)--and benefits from reduced data requirements due to the fixed, mathematically defined filters used in wavelet scattering. C_LI Summary StatementDeep Parametric Response Mapping improves the accuracy and noise robustness of CT-based classification of emphysema and functional small airways disease using feature-based representations, enhancing the reliability of COPD phenotyping.