Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.

OBJECTIVE: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. METHODS: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). RESULTS: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. CONCLUSION: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. CRITICAL RELEVANCE STATEMENT: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging. KEY POINTS: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Comparative sensitivity of early cystic fibrosis lung disease detection tools in school aged children.

BACKGROUND: Effective detection of early lung disease in cystic fibrosis (CF) is critical to understanding early pathogenesis and evaluating early intervention strategies. We aimed to compare ability of several proposed sensitive functional tools to detect early CF lung disease as defined by CT structural disease in school aged children. METHODS: 50 CF subjects (mean±SD 11.2 ± 3.5y, range 5-18y) with early lung disease (FEV1≥70 % predicted: 95.7 ± 11.8 %) performed spirometry, Multiple breath washout (MBW, including trapped gas assessment), oscillometry, cardiopulmonary exercise testing (CPET) and simultaneous spirometer-directed low-dose CT imaging. CT data were analysed using well-evaluated fully quantitative software for bronchiectasis and air trapping (AT). RESULTS: CT bronchiectasis and AT occurred in 24 % and 58 % of patients, respectively. Of the functional tools, MBW detected the highest rates of abnormality: Scond 82 %, MBWTG RV 78 %, LCI 74 %, MBWTG IC 68 % and Sacin 51 %. CPET VO2peak detected slightly higher rates of abnormality (9 %) than spirometry-based FEV1 (2 %). For oscillometry AX (14 %) performed better than Rrs (2 %) whereas Xrs and R5-19 failed to detect any abnormality. LCI and Scond correlated with bronchiectasis (r = 0.55-0.64, p < 0.001) and AT (r = 0.73-0.74, p < 0.001). MBW-assessed trapped gas was detectable in 92 % of subjects and concordant with CT-assessed AT in 74 %. CONCLUSIONS: Significant structural and functional deficits occur in early CF lung disease, as detected by CT and MBW. For MBW, additional utility, beyond that offered by LCI, was suggested for Scond and MBW-assessed gas trapping. Our study reinforces the complementary nature of these tools and the limited utility of conventional oscillometry and CPET in this setting.

Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.

Background: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Methods: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Results: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Conclusion: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.

Qualitative and quantitative evaluation of computed tomography changes in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor: a retrospective observational study.

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p < 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p < 0.001), and MLD decreased (p < 0.001) through a reduction of ground glass opacity areas (p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials.

MDCT-based longitudinal automated airway and air trapping analysis in school-age children with mild cystic fibrosis lung disease.

Objectives: Quantitative computed tomography (QCT) offers some promising markers to quantify cystic fibrosis (CF)-lung disease. Air trapping may precede irreversible bronchiectasis; therefore, the temporal interdependencies of functional and structural lung disease need to be further investigated. We aim to quantify airway dimensions and air trapping on chest CT of school-age children with mild CF-lung disease over two years. Methods: Fully-automatic software analyzed 144 serial spirometer-controlled chest CT scans of 36 children (median 12.1 (10.2-13.8) years) with mild CF-lung disease (median ppFEV1 98.5 (90.8-103.3) %) at baseline, 3, 12 and 24 months. The airway wall percentage (WP5-10), bronchiectasis index (BEI), as well as severe air trapping (A3) were calculated for the total lung and separately for all lobes. Mixed linear models were calculated, considering the lobar distribution of WP5-10, BEI and A3 cross-sectionally and longitudinally. Results: WP5-10 remained stable (P = 0.248), and BEI changed from 0.41 (0.28-0.7) to 0.54 (0.36-0.88) (P = 0.156) and A3 from 2.26% to 4.35% (P = 0.086) showing variability over two years. ppFEV1 was also stable (P = 0.276). A robust mixed linear model showed a cross-sectional, regional association between WP5-10 and A3 at each timepoint (P < 0.001). Further, BEI showed no cross-sectional, but another mixed model showed short-term longitudinal interdependencies with air trapping (P = 0.003). Conclusions: Robust linear/beta mixed models can still reveal interdependencies in medical data with high variability that remain hidden with simpler statistical methods. We could demonstrate cross-sectional, regional interdependencies between wall thickening and air trapping. Further, we show short-term regional interdependencies between air trapping and an increase in bronchiectasis. The data indicate that regional air trapping may precede the development of bronchiectasis. Quantitative CT may capture subtle disease progression and identify regional and temporal interdependencies of distinct manifestations of CF-lung disease.

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (-766 vs. -790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62-0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules.

Reproducibility of pulmonary magnetic resonance angiography in adults with muco-obstructive pulmonary disease.

BACKGROUND: Recent studies support magnetic resonance angiography (MRA) as a diagnostic tool for pulmonary arterial disease. PURPOSE: To determine MRA image quality and reproducibility, and the dependence of MRA image quality and reproducibility on disease severity in patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). MATERIAL AND METHODS: Twenty patients with COPD (mean age 66.5 ± 8.9 years; FEV1% = 42.0 ± 13.3%) and 15 with CF (mean age 29.3 ± 9.3 years; FEV1% = 66.6 ± 15.8%) underwent morpho-functional chest magnetic resonance imaging (MRI) including time-resolved MRA twice one month apart (MRI1, MRI2), and COPD patients underwent non-contrast computed tomography (CT). Image quality was assessed visually using standardized subjective 5-point scales. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were measured by regions of interest. Disease severity was determined by spirometry, a well-evaluated chest MRI score, and by computational CT emphysema index (EI) for COPD. RESULTS: Subjective image quality was diagnostic for all MRA at MRI1 and MRI2 (mean score = 4.7 ± 0.6). CNR and SNR were 4 43.8 ± 8.7 and 50.5 ± 8.7, respectively. Neither image quality score nor CNR or SNR correlated with FEV1% or chest MRI score for COPD and CF (r = 0.239-0.248). CNR and SNR did not change from MRI1 to MRI2 (P = 0.434-0.995). Further, insignificant differences in CNR and SNR between MRA at MRI1 and MRI2 did not correlate with FEV1% nor chest MRI score in COPD and CF (r = -0.238-0.183), nor with EI in COPD (r = 0.100-0.111). CONCLUSION: MRA achieved diagnostic quality in COPD and CF patients and was highly reproducible irrespective of disease severity. This supports MRA as a robust alternative to CT in patients with underlying muco-obstructive lung disease.

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

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.

Fibroblast Activation Protein-Specific PET/CT Imaging in Fibrotic Interstitial Lung Diseases and Lung Cancer: A Translational Exploratory Study.

Interstitial lung diseases (ILDs) comprise over 200 parenchymal lung disorders. Among them, fibrosing ILDs, especially idiopathic pulmonary fibrosis, are associated with a poor prognosis, whereas some other ILDs, such as sarcoidosis, have a much better prognosis. A high proportion 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 fibroblast activation protein (FAP) inhibitor (FAPI) PET/CT in various types of fILD and to confirm FAP expression in fILD lesions by FAP immunohistochemistry of human fILD biopsy samples and of lung sections of genetically engineered (Nedd4-2-/- ) mice with an idiopathic pulmonary fibrosislike lung disease. Methods: PET scans of 15 patients with fILD and suspected LC were acquired 10, 60, and 180 min after the administration of 150-250 MBq of a 68Ga-labeled FAPI tracer (FAPI-46). In 3 patients, dynamic scans over 40 min were performed instead of imaging after 10 min. The SUVmax and SUVmean of fibrotic lesions and LC were measured and CT-density-corrected. Target-to-background ratios (TBRs) 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 performed. Results: fILD lesions as well as LC showed markedly elevated 68Ga-FAPI uptake (density-corrected SUVmax and SUVmean 60 min after injection: 11.12 ± 6.71 and 4.29 ± 1.61, respectively, for fILD lesions and 16.69 ± 9.35 and 6.44 ± 3.29, respectively, for LC) and high TBR (TBR of density-corrected SUVmax and SUVmean 60 min after injection: 2.30 ± 1.47 and 1.67 ± 0.79, respectively, for fILD and 3.90 ± 2.36 and 2.37 ± 1.14, respectively, for LC). SUVmax and SUVmean decreased over time, with a stable TBR for fILD and a trend toward an increasing TBR in LC. Dynamic imaging showed differing time-activity curves for fILD and LC. 68Ga-FAPI uptake showed a positive correlation with the CT-based fibrosis index. Immunohistochemistry of human biopsy samples and the lungs of Nedd4-2-/- mice showed a patchy expression of FAP in fibrotic lesions, preferentially in the transition zone to healthy lung parenchyma. Conclusion:68Ga-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.

Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools.

OBJECTIVE: Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients. METHODS: 418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LUFIT on segmented lung volume and 80th (reflecting fibrosis) and 40th (reflecting ground-glass opacity) percentile of the lung density histogram was analyzed using Bland-Altman plots. The fibrosis and ground-glass opacity segmented by IMBIO (lung texture analysis software tool) were included in specific regression analyses. RESULTS: In the IPF-group, LUFIT outperformed YACTA by segmenting more lung volume (mean difference 242 mL, 95% limits of agreement -54 to 539 mL), as well as quantifying higher 80th (76 HU, -6 to 158 HU) and 40th percentiles (9 HU, -73 to 90 HU). No relevant differences were revealed in the control group. The 80th/40th percentile as quantified by LUFIT correlated positively with the percentage of fibrosis/ground-glass opacity calculated by IMBIO (r = 0.78/r = 0.92). CONCLUSIONS: In terms of segmentation of pulmonary fibrosis, LUFIT as a shape model-based segmentation software tool is superior to the threshold-based YACTA, tool, since the density of (severe) fibrosis is similar to that of the surrounding soft tissues. Therefore, shape modeling as used in LUFIT may serve as a valid tool in the quantification of IPF, since this mainly affects the subpleural space.

Improved detection of air trapping on expiratory computed tomography using deep learning.

BACKGROUND: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. MATERIALS AND METHODS: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. RESULTS: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. CONCLUSION: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Volume Vascular Pulmonar Estimado por Software Automatizado é um Preditor de Mortalidade após Embolia Pulmonar Aguda / Pulmonary Vascular Volume Estimated by Automated Software is a Mortality Predictor after Acute Pulmonary Embolism

Resumo Fundamento: A embolia pulmonar aguda (EPA) tem desfecho clínico variável. A angiotomografia computadorizada (angio-CT) é considerada o padrão-ouro para o diagnóstico. Objetivo: Avaliar se o volume vascular pulmonar (VVP) quantificado por software automatizado é um preditor de mortalidade após EPA. Métodos: Estudo de coorte retrospectivo no qual a imagem da angio-CT de 61 pacientes com EPA foi reanalisada. O VVP e o volume pulmonar (VP) foram estimados automaticamente pelo software Yacta. Calculamos o VVP ajustado pela razão: VVP(cm3)/VP(litros). Parâmetros prognósticos clássicos da angio-CT (carga embólica; razão do diâmetro do ventrículo direito/ventrículo esquerdo; razão do diâmetro da artéria pulmonar/aorta; desvio do septo interventricular; infarto pulmonar e refluxo de contraste na veia hepática) foram avaliados. A mortalidade em 1 mês foi o desfecho analisado. Consideramos um valor de p <0,05 como estatisticamente significativo. Resultados: Sete mortes (11%) ocorreram entre os 61 pacientes durante 1 mês de seguimento. O VVP ajustado <23cm3/L foi um preditor independente de mortalidade na análise univariada (odds ratio [OR]: 26; intervalo de confiança de 95% [IC95%]: 3-244; p=0,004) e na análise multivariada (OR ajustado: 19 [IC95%: 1,3-270]; p=0,03). Os parâmetros clássicos da angio-CT não foram associados à mortalidade em 1 mês nesta amostra. O VVP ajustado <23cm3/L apresentou sensibilidade de 86%, especificidade de 82%, valor preditivo negativo de 94% e valor preditivo positivo de 64% para identificação dos pacientes que morreram. Conclusão: VVP ajustado <23cm3/L foi um preditor independente de mortalidade após EPA. Esse parâmetro mostrou melhor desempenho prognóstico do que os outros achados clássicos da angio-CT. (Arq Bras Cardiol. 2020; 115(5):809-818)

Abstract Background: Acute pulmonary embolism (APE) has a variable clinical outcome. Computed tomography pulmonary angiography (CTPA) is the gold standard for this diagnosis. Objective: To evaluate if the pulmonary vascular volume (PVV) quantified by automated software is a mortality predictor after APE. Methods: Retrospective cohort study where the CTPA imaging of 61 patients with APE was reanalyzed. Pulmonary vascular volume (PVV) and pulmonary volume (PV) were automatically estimated using the Yacta software. We calculated the adjusted PVV by the ratio: PVV(cm3)/PV(liters). Classical prognostic CTPA parameters (clot load index, right ventricle/left ventricle diameter ratio, pulmonary artery/aorta diameter ratio, ventricular septal bowing, pulmonary infarction and reflux of contrast into the hepatic vein) were assessed. The outcome assessed was one-month mortality. We considered a p-value <0.05 as statistically significant. Results: Seven deaths (11%) occurred at one month among these 61 patients. PVV<23cm3/L was an independent predictor of one-month mortality in the univariate [odds ratio (OR): 26; 95% confidence interval (CI): 3-244; p=0.004] and multivariate analyses [adjusted OR: 19; 95%CI: 1.3-270; p=0.03]. The classical CTPA parameters were not associated with one-month mortality in this sample. The PVV<23cm3/L showed a sensitivity of 86%, a specificity of 82%, a negative predictive value of 94% and a positive predictive value of 64% to identify the patients who died. Conclusion: PVV<23cm3/L was an independent predictor of one-month mortality after APE. This parameter showed better prognostic performance than other classical CTPA findings. (Arq Bras Cardiol. 2020; 115(5):809-818)

Influence of acquisition settings and radiation exposure on CT lung densitometry-An anthropomorphic ex vivo phantom study.

OBJECTIVES: To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom. MATERIALS AND METHODS: Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kVp and 80kVp, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kVp/120 mAs serving as the standard of reference. RESULTS: Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05). CONCLUSIONS: Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required.

Influence of Inspiratory/Expiratory CT Registration on Quantitative Air Trapping.

RATIONALE AND OBJECTIVES: The aim of this study was to assess variability in quantitative air trapping (QAT) measurements derived from spatially aligned expiration CT scans. MATERIALS AND METHODS: Sixty-four paired CT examinations, from 16 school-age cystic fibrosis subjects examined at four separate time intervals, were used in this study. For each pair, visually inspected lobe segmentation maps were generated and expiration CT data were registered to the inspiration CT frame. Measurements of QAT, the percentage of voxels on the expiration CT scan below a set threshold were calculated for each lobe and whole-lung from the registered expiration CT and compared to the true values from the unregistered data. RESULTS: A mathematical model, which simulates the effect of variable regions of lung deformation on QAT values calculated from aligned to those from unaligned data, showed the potential for large bias. Assessment of experimental QAT measurements using Bland-Altman plots corroborated the model simulations, demonstrating biases greater than 5% when QAT was approximately 40% of lung volume. These biases were removed when calculating QAT from aligned expiration CT data using the determinant of the Jacobian matrix. We found, by Dice coefficient analysis, good agreement between aligned expiration and inspiration segmentation maps for the whole-lung and all but one lobe (Dice coefficient > 0.9), with only the lingula generating a value below 0.9 (mean and standard deviation of 0.85 ± 0.06). CONCLUSION: The subtle and predictable variability in corrected QAT observed in this study suggests that image registration is reliable in preserving the accuracy of the quantitative metrics.

Longitudinal airway remodeling in active and past smokers in a lung cancer screening population.

OBJECTIVES: To longitudinally investigate smoking cessation-related changes of quantitative computed tomography (QCT)-based airway metrics in a group of heavy smokers. METHODS: CT scans were acquired in a lung cancer screening population over 4 years at 12-month intervals in 284 long-term ex-smokers (ES), 405 continuously active smokers (CS), and 31 subjects who quitted smoking within 2 years after baseline CT (recent quitters, RQ). Total diameter (TD), lumen area (LA), and wall percentage (WP) of 1st-8th generation airways were computed using airway analysis software. Inter-group comparison was performed using Mann-Whitney U test or Student's t test (two groups), and ANOVA or ANOVA on ranks with Dunn's multiple comparison test (more than two groups), while Fisher's exact test or chi-squared test was used for categorical data. Multiple linear regression was used for multivariable analysis. RESULTS: At any time, TD and LA were significantly higher in ES than CS, for example, in 5th-8th generation airways at baseline with 6.24 mm vs. 5.93 mm (p < 0.001) and 15.23 mm2 vs. 13.51 mm2 (p < 0.001), respectively. RQ showed higher TD (6.15 mm vs. 5.93 mm, n.s.) and significantly higher LA (14.77 mm2 vs. 13.51 mm2, p < 0.001) than CS after 3 years, and after 4 years. In multivariate analyses, smoking status independently predicted TD, LA, and WP at baseline, at 3 years and 4 years (p < 0.01-0.001), with stronger impact than pack years. CONCLUSIONS: Bronchial dimensions depend on the smoking status. Smoking-induced airway remodeling can be partially reversible after smoking cessation even in long-term heavy smokers. Therefore, QCT-based airway metrics in clinical trials should consider the current smoking status besides pack years. KEY POINTS: • Airway lumen and diameter are decreased in active smokers compared to ex-smokers, and there is a trend towards increased airway wall thickness in active smokers. • Smoking-related airway changes improve within 2 years after smoking cessation. • Smoking status is an independent predictor of airway dimensions.

Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8-14 year-old children with cystic fibrosis.

OBJECTIVES: Densitometry on paired inspiratory and expiratory multidetector computed tomography (MDCT) for the quantification of air trapping is an important approach to assess functional changes in airways diseases such as cystic fibrosis (CF). For a regional analysis of functional deficits, an accurate lobe segmentation algorithm applicable to inspiratory and expiratory scans is beneficial. MATERIALS AND METHODS: We developed a fully automated lobe segmentation algorithm, and subsequently validated automatically generated lobe masks (ALM) against manually corrected lobe masks (MLM). Paired inspiratory and expiratory CTs from 16 children with CF (mean age 11.1±2.4) acquired at 4 time-points (baseline, 3mon, 12mon, 24mon) with 2 kernels (B30f, B60f) were segmented, resulting in 256 ALM. After manual correction spatial overlap (Dice index) and mean differences in lung volume and air trapping were calculated for ALM vs. MLM. RESULTS: The mean overlap calculated with Dice index between ALM and MLM was 0.98±0.02 on inspiratory, and 0.86±0.07 on expiratory CT. If 6 lobes were segmented (lingula treated as separate lobe), the mean overlap was 0.97±0.02 on inspiratory, and 0.83±0.08 on expiratory CT. The mean differences in lobar volumes calculated in accordance with the approach of Bland and Altman were generally low, ranging on inspiratory CT from 5.7±52.23cm3 for the right upper lobe to 17.41±14.92cm3 for the right lower lobe. Higher differences were noted on expiratory CT. The mean differences for air trapping were even lower, ranging from 0±0.01 for the right upper lobe to 0.03±0.03 for the left lower lobe. CONCLUSIONS: Automatic lobe segmentation delivers excellent results for inspiratory and good results for expiratory CT. It may become an important component for lobe-based quantification of functional deficits in cystic fibrosis lung disease, reducing necessity for user-interaction in CT post-processing.

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

OBJECTIVE: To longitudinally evaluate effects of smoking cessation on quantitative CT in a lung cancer screening cohort of heavy smokers over 4 years. METHODS: 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). RESULTS: 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. CONCLUSION: 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. KEY POINTS: • 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.

Changes of Emphysema Parameters over the Respiratory Cycle During Free Breathing: Preliminary Results Using Respiratory Gated 4D-CT.

The purpose of this research was to evaluate respiratory gated CT of the lung in patients with COPD for analysis of parenchymal characteristics who were potential candidates for volume reduction surgery. Eleven patients with clinically known emphysematous disease underwent a respiratory gated, free-breathing 64-multislice-CT (Aquilion 64, Toshiba). Retrospective image reconstruction was performed similar to cardiac CT at every 10% of the respiratory loop, resulting in 10 complete volumetric datasets at 10 equidistant time points. All images were transferred onto a PC for calculation of the total lung volume, emphysema volume, emphysema index, and mean lung density. Complete datasets could be successfully reconstructed in all patients. The mean lung volume increased from 6.9 L to 7.5 L over the respiratory cycle. Emphysema volume increased from 1.6 L to 2.0 L and emphysema index from 22.6% to 26.5% from expiration to inspiration. In conclusion, respiratory gated chest 4D-CT allows for combined morphologic and functional image analysis, which can provide new insight into functional impairment and individual treatment planning.

Differences of airway dimensions between patients with and without bronchiolitis obliterans syndrome after lung transplantation-Computer-assisted quantification of computed tomography.

BACKGROUND: The aim of our retrospective study was to determine whether a dedicated software for assessment of airway morphology can detect differences in airway dimensions between patients with and without bronchiolitis obliterans syndrome (BOS), regarded as the clinical correlate of chronic lung allograft rejection. METHODS: 12 patients with and 14 patients without diagnosis of BOS were enrolled in the study. Evaluation of bronchial wall area percentage (WA%) and bronchial wall thickness (WT) in all follow-up CT scans was performed using a semiautomatic airway assessment tool. We assessed temporal changes (ΔWA%, ΔWT) and compared these morphological parameters with forced expiratory volume in one second (ΔFEV1). RESULTS: In patients with and without BOS, the temporal changes over the entire follow-up were 26.6% versus 16.2% for ΔFEV1 (p=0.034), 14.2% versus 5.4% for ΔWA% (p=0.003) and 0.212mm versus 0.064mm for ΔWT (p=0.011). CONCLUSIONS: We detected significant differences of the temporal changes of airway dimensions (ΔWA%, ΔWT) between lung transplant recipients with and without BOS. We conclude that computer-assisted bronchial wall measurements in CT scans might complement the information from pulmonary function tests and establish as a non-invasive method to confirm BOS in lung transplant recipients in the future.