Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction

CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions

ABSTRACT

Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.

Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study

INTRODUCTION/RATIONALE

Protein biomarkers may help enable the prediction of incident interstitial features on chest CT.

METHODS

We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.05). To predict incident interstitial features, we trained four random forest classifiers in a two-thirds random subset of COPDGene: (1) imaging and demographic information, (2) univariate screen biomarkers, (3) multivariable confirmation biomarkers and (4) multivariable confirmation biomarkers available in a separate testing cohort (Pittsburgh Lung Screening Study (PLuSS)). We evaluated classifier performance in the remaining one-third of COPDGene, and, for the final model, also in PLuSS.

RESULTS

In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features.

CONCLUSIONS

Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.

Development and Application of a Virtual Imaging Trial Framework for Longitudinal Quantification of Emphysema in CT

Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0-5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.