Purpose: Interstitial lung diseases (ILD) comprise over 200 parenchymal lung disorders. Among them, fibrosing ILDs, especially idiopathic pulmonary fibrosis (IPF) in particular are associated with a poor prognosis, while some others ILDs like sarcoidosis have a much better prognosis. A high proportion of ILD manifests as fibrotic ILD (fILD). Lung cancer (LC) is a frequent complication of fILD. Activated fibroblasts are crucial for fibrotic processes in fILD. The aim of this exploratory study was to evaluate the imaging properties of static and dynamic FAPI-PET/CT in various types of fILD and to confirm FAP expression of fILD lesions by FAP immunohistochemistry of human fILD biopsy samples and of lung sections of genetically engineered (Nedd4-2 -/- ) mice with an idiopathic pulmonary fibrosis (IPF) -like lung disease. Patients and Methods: PET-Scans of 15 patients with fILD and suspected LC were acquired 10, 60 and 180 minutes after the administration of 150-250 MBq of a 68Ga labelled FAPI tracer (FAPI-46). In three patients, dynamic scans over 40 mins were performed instead of imaging after 10 minutes. Standardized uptake values (SUVmax and SUVmean) of fibrotic lesions and LC were measured and CT-density-corrected. Target-to-background ratios (TBR) were calculated. PET imaging was correlated with CT-based fibrosis scores. Time-activity curves derived from dynamic imaging were analyzed. FAP immunohistochemistry of 4 human fILD biopsy samples and of fibrotic lungs of Nedd4-2-/- mice was carried out. Results: FILD lesions as well as LC showed markedly elevated FAPI-uptake (density corrected SUVmax / mean values 60 minutes post injection: 11,12 +/- 6,71 and 4,29 +/- 1,61 for fILD lesions and 16,69 +/- 9,35 and 6,44 +/- 3,29 for LC) and high TBR (TBR of density corrected SUVmax/SUVmean values 60 minutes post injection: 2,30 +/- 1,47 and 1,67 +/- 0,79 for fILD and 3,90 +/- 2,36 and 2,37 +/- 1,14 for LC). SUVmax and SUVmean values decreased over time with stable TBR of fILD and increasing TBR in LC on trend. Dynamic imaging showed differing time activity curves of fILD and LC. FAPI uptake showed a positive correlation with the CT-based fibrosis index (FIBI). Immunohistochemistry of human biopsy samples and lungs of Nedd4-2-/- mice showed a patchy expression of FAP in fibrotic lesions, preferentially in the transition zone to healthy lung parenchyma. Conclusion: FAPI-PET/CT imaging is a promising new imaging modality for fILD and LC. Its potential clinical value for monitoring and therapy evaluation of fILD should be investigated in future studies.

Fibroblast Activation Protein specific PET/CT imaging in fibrotic interstitial lung diseases and lung cancer: a translational exploratory study.

This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning–based kernel conversion technique in normalizing kernels for emphysema quantification. A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman’s test and Bland-Altman plots. All values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p   0.05). The deep learning–based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification. • Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT.
 • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema.
 • Deep learning–based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.

Emphysema quantification using low-dose computed tomography with deep learning–based kernel conversion comparison

Objectives To evaluate the prognostic value of fully automatic lung quantification based on spectral computed tomography (CT) and laboratory parameters for combined outcome prediction in COVID-19 pneumonia. Methods CT images of 53 hospitalized COVID-19 patients including virtual monochromatic reconstructions at 40-140keV were analyzed using a fully automated software system. Quantitative CT (QCT) parameters including mean and percentiles of lung density, fibrosis index (FIBI-700, defined as the percentage of segmented lung voxels ≥-700 HU), quantification of ground-glass opacities and well-aerated lung areas were analyzed. QCT parameters were correlated to laboratory and patient outcome parameters (hospitalization, days on intensive care unit, invasive and non-invasive ventilation). Results Best correlations were found for laboratory parameters LDH (r = 0.54), CRP (r = 0.49), Procalcitonin (r = 0.37) and partial pressure of oxygen (r = 0.35) with the QCT parameter 75th percentile of lung density. LDH, Procalcitonin, 75th percentile of lung density and FIBI-700 were the strongest independent predictors of patients’ outcome in terms of days of invasive ventilation. The combination of LDH and Procalcitonin with either 75th percentile of lung density or FIBI-700 achieved a r2 of 0.84 and 1.0 as well as an area under the receiver operating characteristic curve (AUC) of 0.99 and 1.0 for the prediction of the need of invasive ventilation. Conclusions QCT parameters in combination with laboratory parameters could deliver a feasible prognostic tool for the prediction of invasive ventilation in patients with COVID-19 pneumonia.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0271787&type=printable

COVID-19 pneumonia: Prediction of patient outcome by CT-based quantitative lung parenchyma analysis combined with laboratory parameters

Chronic obstructive pulmonary disease (COPD) is frequently associated with coronary artery disease (CAD). When considering computed tomography (CT) for COPD phenotyping, coronary vessel wall calcification would be a potential marker of cardiac disease. However, non-ECG gated scans as used in COPD monitoring do not comply with established quantitative approaches using ECG-triggered CT and the Agatston score. We studied the diagnostic potential of Agatston scores from non-triggered scans for cardiac disease. The study population was a sub-group of the COPD cohort COSYCONET that underwent CT scanning in addition to comprehensive clinical assessments, echocardiographic data and physician-based diagnoses of comorbidities. Agatston scores from non-contrast enhanced, non-triggered CT were used to identify a cut-off value for CAD via ROC analysis. 399 patients were included (152 female, mean age 66.0 ± 8.2 y). In terms of CAD, an Agatston score ≥1500 AU performed best (AUC 0.765; 95% CI: 0.700, 0.831) and was superior to the conventional cut-off value (400 AU). Using this value for defining groups, there were differences (p &lt; 0.05) in lung function, left atrial diameter and left ventricular end-systolic diameter as well as CT-determined central airway wall thickness pointing towards a bronchitis phenotype. In multivariate analysis, BMI, hyperlipidemia, arterial hypertension, GOLD D (p &lt; 0.05) but particularly Agatston score ≥1500 AU (Odds ratio 10.5; 95% CI: 4.8; 22.6)) were predictors of CAD. We conclude that in COPD patients, Agatston scores derived from non-ECG gated CT showed a much higher cut-off value (1500 AU) for actionable coronary artery disease than the score derived from ECG-triggered CT in cardiology patients. 

Association of coronary artery calcification with clinical and physiological characteristics in patients with COPD: Results from COSYCONET.

Purpose Normalized emphysema score is a protocol-robust CT biomarker of mortality. We aimed to improve mortality prediction by including the emphysema score progression rate–its change over time–into the models. Method and materials CT scans from 6000 National Lung Screening Trial CT arm participants were included. Of these, 1810 died (445 lung cancer-specific). The remaining 4190 survivors were sampled with replacement up to 24432 to approximate the full cohort. Three overlapping subcohorts were formed which required participants to have images from specific screening rounds. Emphysema scores were obtained after resampling, normalization, and bullae cluster analysis of the original images. Base models contained solely the latest emphysema score. Progression models included emphysema score progression rate. Models were adjusted by including baseline age, sex, BMI, smoking status, smoking intensity, smoking duration, and previous COPD diagnosis. Cox proportional hazard models predicting all-cause and lung cancer mortality were compared by calculating the area under the curve per year follow-up. Results In the subcohort of participants with baseline and first annual follow-up scans, the analysis was performed on 4940 participants (23227 after resampling). Area under the curve for all-cause mortality predictions of the base and progression models 6 years after baseline were 0.564 (0.564 to 0.565) and 0.569 (0.568 to 0.569) when unadjusted, and 0.704 (0.703 to 0.704) to 0.705 (0.704 to 0.705) when adjusted. The respective performances predicting lung cancer mortality were 0.638 (0.637 to 0.639) and 0.643 (0.642 to 0.644) when unadjusted, and 0.724 (0.723 to 0.725) and 0.725 (0.725 to 0.726) when adjusted. Conclusion Including emphysema score progression rate into risk models shows no clinically relevant improvement in mortality risk prediction. This is because scan normalization does not adjust for an overall change in lung density. Adjusting for changes in smoking behavior is likely required to make this a clinically useful measure of emphysema progression.

/pdf/predicting-all-cause-and-lung-cancer-mortality-using-2u04rzcdm2.pdf

Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances.

To longitudinally evaluate effects of smoking cessation on quantitative CT in a lung cancer screening cohort of heavy smokers over 4 years. After 4 years, low-dose chest CT was available for 314 long-term ex-smokers (ES), 404 continuous smokers (CS) and 39 recent quitters (RQ) who quitted smoking within 2 years after baseline CT. CT acquired at baseline and after 3 and 4 years was subjected to well-evaluated densitometry software, computing mean lung density (MLD) and 15th percentile of the lung density histogram (15TH). At baseline, active smokers showed significantly higher MLD and 15TH (-822±35 and -936±25 HU, respectively) compared to ES (-831±31 and -947±22 HU, p<0.01–0.001). After 3 years, CS again had significantly higher MLD and 15TH (-801±29 and -896±23 HU) than ES (-808±27 and -906±20 HU, p<0.01–0.001) but also RQ (-813±20 and -909±15 HU, p<0.05-0.001). Quantitative CT parameters did not change significantly after 4 years. Importantly, smoking status independently predicted MLD at baseline and year 3 (p<0.001) in multivariate analysis. On quantitative CT, lung density is higher in active smokers than ex-smokers, and sustainably decreases after smoking cessation, reflecting smoking-induced inflammation. Interpretations of quantitative CT data within clinical trials should consider smoking status. • Lung density is higher in active smokers than ex-smokers.
 • Lung density sustainably decreases after smoking cessation.
 • Impact of smoking cessation on lung density is independent of potentially confounding factors.
 • Smoke-induced pulmonary inflammation and particle deposition influence lung density on CT.

Effect of smoking cessation on quantitative computed tomography in smokers at risk in a lung cancer screening population.

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Effect of smoking cessation on quantitative computed tomography in smokers at risk in a lung cancer screening population.