Objective To develop an automated method for the joint and consistent evaluation of emphysema and mortality risk that provides quantification of data and model uncertainty. Materials and methods Participants from the prospective COPDGene study who underwent both full radiation dose (FD) and reduced radiation dose (RD) chest CT scans at 5-year follow-up were included and divided into training (80%), validation (10%), and testing (10%) datasets. We trained a multi-task Bayesian neural network (BNN) to estimate the FD volume-adjusted lung density (ALD) regardless of acquisition protocol, in addition to the 5-year mortality risk. The data and model uncertainty were quantified in the testing dataset. Our deep learning ALD (DL-ALD) was compared to the conventional ALD. Results In total, 1350 participants (mean age 64.4 years ± 8.7; 659 female) were included. Compared to conventional ALD, DL-ALD was more consistent between FD and RD CT images (mean difference: 1 g/L ± 3.1 versus 14.8 g/L ± 5.3, p < 0.001). The predicted 5-year mortality was similar between image protocols (mean difference: 0.0007 ± 0.02, p = 0.76). The uncertainty associated with image variability when quantifying DL-ALD was lower in participants with severe emphysema (Pearson’s rho = 0.79, p < 0.001), and the model uncertainty for mortality risk was lower both for severe and early-stage participants compared to other participants (p < 0.001). Conclusion The presented multi-task BNN provides an increased robustness to imaging protocol compared to conventional methods for CT evaluation of emphysema. Additionally, it provides direct measurements of uncertainty for its generalization to diverse imaging protocols and patient populations. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Quantitative CT evaluation of emphysema is highly sensitive to CT protocol, which increases uncertainty in disease evaluation and impacts the clinical utility of traditional metrics. <Emphasis Type="BoldItalic">Findings</Emphasis> Uncertainty-aware deep learning improved consistency in emphysema quantification between fixed and reduced dose CT scans compared to traditional histogram analysis. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> CT evaluation of emphysema severity and mortality risk using uncertainty-aware deep learning methods is more consistent across variable radiation dose protocols compared to conventional methods while also providing measurement reliability metrics, improving the evaluation of COPD using CT. Graphical Abstract

Uncertainty-aware quantitative CT evaluation of emphysema and mortality risk from variable radiation dose images

期刊论文
发表日期：

2025年4月4日

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David Baraghoshi,
Matthew J. Strand,
Stephen M. Humphries,
David A. Lynch,
Alexander M. Kaizer,
Antonio R. Porras

摘要

Objective

To develop an automated method for the joint and consistent evaluation of emphysema and mortality risk that provides quantification of data and model uncertainty.

Materials and methods

Participants from the prospective COPDGene study who underwent both full radiation dose (FD) and reduced radiation dose (RD) chest CT scans at 5-year follow-up were included and divided into training (80%), validation (10%), and testing (10%) datasets. We trained a multi-task Bayesian neural network (BNN) to estimate the FD volume-adjusted lung density (ALD) regardless of acquisition protocol, in addition to the 5-year mortality risk. The data and model uncertainty were quantified in the testing dataset. Our deep learning ALD (DL-ALD) was compared to the conventional ALD.

Results

In total, 1350 participants (mean age 64.4 years ± 8.7; 659 female) were included. Compared to conventional ALD, DL-ALD was more consistent between FD and RD CT images (mean difference: 1 g/L ± 3.1 versus 14.8 g/L ± 5.3, p < 0.001). The predicted 5-year mortality was similar between image protocols (mean difference: 0.0007 ± 0.02, p = 0.76). The uncertainty associated with image variability when quantifying DL-ALD was lower in participants with severe emphysema (Pearson’s rho = 0.79, p < 0.001), and the model uncertainty for mortality risk was lower both for severe and early-stage participants compared to other participants (p < 0.001).

Conclusion

The presented multi-task BNN provides an increased robustness to imaging protocol compared to conventional methods for CT evaluation of emphysema. Additionally, it provides direct measurements of uncertainty for its generalization to diverse imaging protocols and patient populations.

Key Points

Question Quantitative CT evaluation of emphysema is highly sensitive to CT protocol, which increases uncertainty in disease evaluation and impacts the clinical utility of traditional metrics.

Findings Uncertainty-aware deep learning improved consistency in emphysema quantification between fixed and reduced dose CT scans compared to traditional histogram analysis.

Clinical relevance CT evaluation of emphysema severity and mortality risk using uncertainty-aware deep learning methods is more consistent across variable radiation dose protocols compared to conventional methods while also providing measurement reliability metrics, improving the evaluation of COPD using CT.

Graphical Abstract