Quantitative texture-based analysis of pulmonary parenchymal features on chest CT: comparison with densitometric indices and short-term effect of changes in smoking habit.

PURPOSE: To investigate the correlations between densitometric and Computer Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER)-derived indices of pulmonary emphysema and their change in the short-term period for groups of patients with different smoking habits. METHOD: This retrospective study included 284 subjects from the ITALUNG trial (198 men and 86 women; mean±sd age 60±4 years) who underwent low-dose chest computed tomography at baseline and 2-year follow-up. Subjects were divided into four groups (persistent smokers, restarters, quitters and former smokers) according to their smoking habit at baseline and follow-up. Densitometric and texture analyses were performed, using CALIPER software. A correlation analysis was conducted between CALIPER-derived low-attenuation areas (LAAs) and densitometric indices, including the 15th percentile of the whole-lung attenuation histogram (Perc15) and the relative areas with density ≤-950 HU (RA950). Densitometric indices and LAAs were evaluated at baseline and variation assessed longitudinally with comparisons between groups with different smoking habit. Further analysis of parenchymal changes per pulmonary zone was performed. RESULTS: LAAs were strongly correlated with Perc15 (rs=0.81; p<0.001) and RA950 (rs=0.905; p<0.001). At baseline, the group of smokers showed higher Perc15, lower RA950, lower LAAs (particularly mild sub-class of LAAs) than the group of ex-smokers (p<0.001). At 2-year follow-up, densitometric indices and LAAs increased in persistent smokers, former smokers and quitters (p<0.05). The progression was larger and statistically more significant in quitters (p<0.001). CONCLUSION: CALIPER texture analysis provides an objective measure comparable to traditional density/histogram features to assess the lung parenchymal changes in relation to different smoking habits.

Quantitative CT and machine learning classification of fibrotic interstitial lung diseases.

OBJECTIVES: To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models' performance. METHODS: We retrospectively identified 1085 patients with pathologically proven usual interstitial pneumonitis (UIP), nonspecific interstitial pneumonitis (NSIP), and chronic hypersensitivity pneumonitis (CHP) who underwent peri-biopsy chest CT. Kruskal-Wallis test evaluated QCT feature associations with each ILD. QCT features, patient demographics, and pulmonary function test (PFT) results trained eXtreme Gradient Boosting (training/validation set n = 911) yielding 3 models: M1 = QCT features only; M2 = M1 plus age and sex; M3 = M2 plus PFT results. A DL model was also developed. ML and DL model areas under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were compared for multiclass (UIP vs. NSIP vs. CHP) and binary (UIP vs. non-UIP) classification performances. RESULTS: The majority (69/78 [88%]) of QCT features successfully differentiated the 3 ILDs (adjusted p ≤ 0.05). All QCT-ML models achieved higher AUC than the DL model (multiclass AUC micro-averages 0.910, 0.910, 0.925, and 0.798 and macro-averages 0.895, 0.893, 0.925, and 0.779 for M1, M2, M3, and DL respectively; binary AUC 0.880, 0.899, 0.898, and 0.869 for M1, M2, M3, and DL respectively). M3 demonstrated statistically significant better performance compared to M2 (∆AUC: 0.015, CI: [0.002, 0.029]) for multiclass prediction. CONCLUSIONS: QCT features successfully differentiated pathologically proven UIP, NSIP, and CHP. While QCT-based ML models outperformed a DL model for classifying ILDs, further investigations are warranted to determine if QCT-ML, DL, or a combination will be superior in ILD classification. KEY POINTS: • Quantitative CT features successfully differentiated pathologically proven UIP, NSIP, and CHP. • Our quantitative CT-based machine learning models demonstrated high performance in classifying UIP, NSIP, and CHP histopathology, outperforming a deep learning model. • While our quantitative CT-based machine learning models performed better than a DL model, additional investigations are needed to determine whether either or a combination of both approaches delivers superior diagnostic performance.

Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis.

INTRODUCTION: Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) software has already been widely used in the evaluation of interstitial lung diseases (ILD) but has not yet been tested in patients affected by COVID-19. Our aim was to use it to describe the relationship between Coronavirus Disease 2019 (COVID-19) outcome and the CALIPER-detected pulmonary vascular-related structures (VRS). MATERIALS AND METHODS: We performed a multicentric retrospective study enrolling 570 COVID-19 patients who performed a chest CT in emergency settings in two different institutions. Fifty-three age- and sex-matched healthy controls were also identified. Chest CTs were analyzed with CALIPER identifying the percentage of VRS over the total lung parenchyma. Patients were followed for up to 72 days recording mortality and required intensity of care. RESULTS: There was a statistically significant difference in VRS between COVID-19-positive patients and controls (median (iqr) 4.05 (3.74) and 1.57 (0.40) respectively, p = 0.0001). VRS showed an increasing trend with the severity of care, p < 0.0001. The univariate Cox regression model showed that VRS increase is a risk factor for mortality (HR 1.17, p < 0.0001). The multivariate analysis demonstrated that VRS is an independent explanatory factor of mortality along with age (HR 1.13, p < 0.0001). CONCLUSION: Our study suggests that VRS increases with the required intensity of care, and it is an independent explanatory factor for mortality. KEY POINTS: • The percentage of vascular-related structure volume (VRS) in the lung is significatively increased in COVID-19 patients. • VRS showed an increasing trend with the required intensity of care, test for trend p< 0.0001. • Univariate and multivariate Cox models showed that VRS is a significant and independent explanatory factor of mortality.

Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules.

INTRODUCTION: Implementation of low-dose chest computed tomography (CT) lung cancer screening and the ever-increasing use of cross-sectional imaging are resulting in the identification of many screen- and incidentally detected indeterminate pulmonary nodules. While the management of nodules with low or high pre-test probability of malignancy is relatively straightforward, those with intermediate pre-test probability commonly require advanced imaging or biopsy. Noninvasive risk stratification tools are highly desirable. METHODS: We previously developed the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a conventional predictive radiomic model based on eight imaging features capturing nodule location, shape, size, texture and surface characteristics. Herein we report its external validation using a dataset of incidentally identified lung nodules (Vanderbilt University Lung Nodule Registry) in comparison to the Brock model. Area under the curve (AUC), as well as sensitivity, specificity, negative and positive predictive values were calculated. RESULTS: For the entire Vanderbilt validation set (n=170, 54% malignant), the AUC was 0.87 (95% CI 0.81-0.92) for the Brock model and 0.90 (95% CI 0.85-0.94) for the BRODERS model. Using the optimal cut-off determined by Youden's index, the sensitivity was 92.3%, the specificity was 62.0%, the positive (PPV) and negative predictive values (NPV) were 73.7% and 87.5%, respectively. For nodules with intermediate pre-test probability of malignancy, Brock score of 5-65% (n=97), the sensitivity and specificity were 94% and 46%, respectively, the PPV was 78.4% and the NPV was 79.2%. CONCLUSIONS: The BRODERS radiomic predictive model performs well on an independent dataset and may facilitate the management of indeterminate pulmonary nodules.

Vessel-related structures predict UIP pathology in those with a non-IPF pattern on CT.

OBJECTIVES: To determine if a quantitative imaging variable (vessel-related structures [VRS]) could identify subjects with a non-IPF diagnosis CT pattern who were highly likely to have UIP histologically. METHODS: Subjects with a multidisciplinary diagnosis of interstitial lung disease including surgical lung biopsy and chest CT within 1 year of each other were included in the study. Non-contrast CT scans were analyzed using the Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) program, which quantifies the amount of various abnormal CT patterns on chest CT. Quantitative data were analyzed relative to pathological diagnosis as well as the qualitative CT pattern. RESULTS: CALIPER-derived volumes of reticulation (p = 0.012), honeycombing (p = 0.017), and VRS (p < 0.001) were associated with a UIP pattern on pathology on univariate analysis but only VRS was associated with a UIP pathology on multivariable analysis (p = 0.013). Using a VRS cut-off of 173 cm3, the sensitivity and specificity for pathological UIP were similar to those for standard qualitative CT assessment (55.9% and 80.4% compared to 60.6% and 80.4%, respectively). VRS differentiated pathological UIP cases in those with a non-IPF diagnosis CT category (p < 0.001) but not in other qualitative CT patterns (typical UIP, probable UIP, and indeterminate for UIP). The rate of pathological UIP in those with VRS greater than 173 cm3 (84.2%) was nearly identical to those who had a qualitative CT pattern of probable UIP (88.9%). CONCLUSIONS: VRS may be an adjunct to CT in predicting pathology in patients with interstitial lung disease. KEY POINTS: • Volume of vessel-related structures (VRS) was associated with usual interstitial pneumonia (UIP) on pathology. • This differentiation arose from those with CT scans with a non-IPF diagnosis imaging pattern. • Higher VRS has similar diagnostic ramifications for UIP as probable UIP, transitively suggesting in patients with high VRS, pathology may be obviated.

Stratification of long-term outcome in stable idiopathic pulmonary fibrosis by combining longitudinal computed tomography and forced vital capacity.

OBJECTIVES: To test HRCT with either visual or quantitative analysis in both short-term and long-term follow-up of stable IPF against long-term (transplant-free) survival, beyond 2 years of disease stability. METHODS: Fifty-eight IPF patients had FVC measurements and HRCTs at baseline (HRCT0), 10-14 months (HRCT1) and 22-26 months (HRCT2). Visual scoring, CALIPER quantitative analysis of HRCT measures, and their deltas were evaluated against combined all-cause mortality and lung transplantation by adjusted Cox proportional hazard models at each time interval. RESULTS: At HRCT1, a ≥ 20% relative increase in CALIPER-total lung fibrosis yielded the highest radiological association with outcome (C-statistic 0.62). Moreover, the model combining FVC% drop ≥ 10% and ≥ 20% relative increase of CALIPER-total lung fibrosis improved the stratification of outcome (C-statistic 0.69, high-risk category HR 12.1; landmark analysis at HRCT1 C-statistic 0.66, HR 14.9 and at HRCT2 C-statistic 0.61, HR 21.8). Likewise, at HRCT2, the model combining FVC% decrease trend and ≥ 20% relative increase of CALIPER-pulmonary vessel-related volume (VRS) improved the stratification of outcome (C-statistic 0.65, HR 11.0; landmark analysis at HRCT1 C-statistic 0.62, HR 13.8 and at HRCT2 C-statistic 0.58, HR 12.6). A less robust stratification of outcome distinction was also demonstrated with the categorical visual scoring of disease change. CONCLUSIONS: Annual combined CALIPER -FVC changes showed the greatest stratification of long-term outcome in stable IPF patients, beyond 2 years. KEY POINTS: • Longitudinal high-resolution computed tomography (HRCT) data is more helpful than baseline HRCT alone for stratification of long-term outcome in IPF. • HRCT changes by visual or quantitative analysis can be added with benefit to the current spirometric reference standard to improve stratification of long-term outcome in IPF. • HRCT follow-up at 12-14 months is more helpful than HRCT follow-up at 23-26 months in clinically stable subjects with IPF.

Quantitative CT Analysis of Diffuse Lung Disease.

Quantitative analysis of thin-section CT of the chest has a growing role in the clinical evaluation and management of diffuse lung diseases. This heterogeneous group includes diseases with markedly different prognoses and treatment options. Quantitative tools can assist in both accurate diagnosis and longitudinal management by improving characterization and quantification of disease and increasing the reproducibility of disease severity assessment. Furthermore, a quantitative index of disease severity may serve as a useful tool or surrogate endpoint in evaluating treatment efficacy. The authors explore the role of quantitative imaging tools in the evaluation and management of diffuse lung diseases. Lung parenchymal features can be classified with threshold, histogram, morphologic, and texture-analysis-based methods. Quantitative CT analysis has been applied in obstructive, infiltrative, and restrictive pulmonary diseases including emphysema, cystic fibrosis, asthma, idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, connective tissue-related interstitial lung disease, and combined pulmonary fibrosis and emphysema. Some challenges limiting the development and practical application of current quantitative analysis tools include the quality of training data, lack of standard criteria to validate the accuracy of the results, and lack of real-world assessments of the impact on outcomes. Artifacts such as patient motion or metallic beam hardening, variation in inspiratory effort, differences in image acquisition and reconstruction techniques, or inaccurate preprocessing steps such as segmentation of anatomic structures may lead to inaccurate classification. Despite these challenges, as new techniques emerge, quantitative analysis is developing into a viable tool to supplement the traditional visual assessment of diffuse lung diseases and to provide decision support regarding diagnosis, prognosis, and longitudinal evaluation of disease. ©RSNA, 2019.

Predicting outcomes in rheumatoid arthritis related interstitial lung disease.

The aim of this study was to compare radiology-based prediction models in rheumatoid arthritis-related interstitial lung disease (RAILD) to identify patients with a progressive fibrosis phenotype.RAILD patients had computed tomography (CT) scans scored visually and using CALIPER and forced vital capacity (FVC) measurements. Outcomes were evaluated using three techniques, as follows. 1) Scleroderma system evaluating visual interstitial lung disease extent and FVC values; 2) Fleischner Society idiopathic pulmonary fibrosis (IPF) diagnostic guidelines applied to RAILD; and 3) CALIPER scores of vessel-related structures (VRS). Outcomes were compared to IPF patients.On univariable Cox analysis, all three staging systems strongly predicted outcome (scleroderma system hazard ratio (HR) 3.78, p=9×10-5; Fleischner system HR 1.98, p=2×10-3; and 4.4% VRS threshold HR 3.10, p=4×10-4). When the scleroderma and Fleischner systems were combined, termed the progressive fibrotic system (C-statistic 0.71), they identified a patient subset (n=36) with a progressive fibrotic phenotype and similar 4-year survival to IPF. On multivariable analysis, with adjustment for patient age, sex and smoking status, when analysed alongside the progressive fibrotic system, the VRS threshold of 4.4% independently predicted outcome (model C-statistic 0.77).The combination of two visual CT-based staging systems identified 23% of an RAILD cohort with an IPF-like progressive fibrotic phenotype. The addition of a computer-derived VRS threshold further improved outcome prediction and model fit, beyond that encompassed by RAILD measures of disease severity and extent.

Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD.

BACKGROUND: The mechanisms underlying airflow obstruction in COPD cannot be distinguished by standard spirometry. We ascertain whether mathematical modeling of airway biomechanical properties, as assessed from spirometry, could provide estimates of emphysema presence and severity, as quantified by computed tomography (CT) metrics and CT-based radiomics. METHODS: We quantified presence and severity of emphysema by standard CT metrics (VIDA) and co-registration analysis (ImbioLDA) of inspiratory-expiratory CT in 194 COPD patients who underwent pulmonary function testing. According to percentages of low attenuation area below - 950 Hounsfield Units (%LAA-950insp) patients were classified as having no emphysema (NE) with %LAA-950insp < 6, moderate emphysema (ME) with %LAA-950insp ≥ 6 and < 14, and severe emphysema (SE) with %LAA-950insp ≥ 14. We also obtained stratified clusters of emphysema CT features by an automated unsupervised radiomics approach (CALIPER). An emphysema severity index (ESI), derived from mathematical modeling of the maximum expiratory flow-volume curve descending limb, was compared with pulmonary function data and the three CT classifications of emphysema presence and severity as derived from CT metrics and radiomics. RESULTS: ESI mean values and pulmonary function data differed significantly in the subgroups with different emphysema degree classified by VIDA, ImbioLDA and CALIPER (p < 0.001 by ANOVA). ESI differentiated NE from ME/SE CT-classified patients (sensitivity 0.80, specificity 0.85, AUC 0.86) and SE from ME CT-classified patients (sensitivity 0.82, specificity 0.87, AUC 0.88). CONCLUSIONS: Presence and severity of emphysema in patients with COPD, as quantified by CT metrics and radiomics can be estimated by mathematical modeling of airway function as derived from standard spirometry.

Predicting Outcomes in Idiopathic Pulmonary Fibrosis Using Automated Computed Tomographic Analysis.

RATIONALE: Quantitative computed tomographic (CT) measures of baseline disease severity might identify patients with idiopathic pulmonary fibrosis (IPF) with an increased mortality risk. We evaluated whether quantitative CT variables could act as a cohort enrichment tool in future IPF drug trials. OBJECTIVES: To determine whether computer-derived CT measures, specifically measures of pulmonary vessel-related structures (VRSs), can better predict functional decline and survival in IPF and reduce requisite sample sizes in drug trial populations. METHODS: Patients with IPF undergoing volumetric noncontrast CT imaging at the Royal Brompton Hospital, London, and St. Antonius Hospital, Utrecht, were examined to identify pulmonary function measures (including FVC) and visual and computer-derived (CALIPER [Computer-Aided Lung Informatics for Pathology Evaluation and Rating] software) CT features predictive of mortality and FVC decline. The discovery cohort comprised 247 consecutive patients, with validation of results conducted in a separate cohort of 284 patients, all fulfilling drug trial entry criteria. MEASUREMENTS AND MAIN RESULTS: In the discovery and validation cohorts, CALIPER-derived features, particularly VRS scores, were among the strongest predictors of survival and FVC decline. CALIPER results were accentuated in patients with less extensive disease, outperforming pulmonary function measures. When used as a cohort enrichment tool, a CALIPER VRS score greater than 4.4% of the lung was able to reduce the requisite sample size of an IPF drug trial by 26%. CONCLUSIONS: Our study has validated a new quantitative CT measure in patients with IPF fulfilling drug trial entry criteria-the VRS score-that outperformed current gold standard measures of outcome. When used for cohort enrichment in an IPF drug trial setting, VRS threshold scores can reduce a required IPF drug trial population size by 25%, thereby limiting prohibitive trial costs. Importantly, VRS scores identify patients in whom antifibrotic medication prolongs life and reduces FVC decline.

Serial automated quantitative CT analysis in idiopathic pulmonary fibrosis: functional correlations and comparison with changes in visual CT scores.

OBJECTIVES: To determine whether computer-based CT quantitation of change can improve on visual change quantification of parenchymal features in IPF. METHODS: Sixty-six IPF patients with serial CT imaging (6-24 months apart) had CT features scored visually and with a computer software tool: ground glass opacity, reticulation and honeycombing (all three variables summed as interstitial lung disease extent [ILD]) and emphysema. Pulmonary vessel volume (PVV) was estimated by computer only. Relationships between changes in CT features and forced vital capacity (FVC) were examined using univariate and multivariate linear regression analyses. RESULTS: On univariate analysis, changes in computer variables demonstrated stronger linkages to FVC change than changes in visual scores (CALIPER ILD:R2=0.53, p<0.0001; Visual ILD:R2=0.16, p=0.001). PVV increase correlated most strongly with relative FVC change (R2=0.57). When PVV constituents (vessel size and location) were examined, an increase in middle zone vessels linked most strongly to FVC decline (R2=0.57) and was independent of baseline disease severity (characterised by CT fibrosis extent, FVC, or DLco). CONCLUSIONS: An increase in PVV, specifically an increase in middle zone lung vessels, was the strongest CT determinant of FVC decline in IPF and was independent of baseline disease severity. KEY POINTS: • Computer analysis improves on visual CT scoring in evaluating deterioration on CT • Increasing pulmonary vessel volume is the strongest CT predictor of functional deterioration • Increasing pulmonary vessel volume predicts functional decline independent of baseline disease severity.

Likelihood of pulmonary hypertension in patients with idiopathic pulmonary fibrosis and emphysema.

BACKGROUND AND OBJECTIVE: This study evaluated whether patients with combined pulmonary fibrosis and emphysema (CPFE) have an increased likelihood of pulmonary hypertension (PHT) when compared with idiopathic pulmonary fibrosis (IPF) patients without emphysema. METHODS: Two consecutive IPF populations having undergone transthoracic echocardiography were examined (n = 223 and n = 162). Emphysema and interstitial lung disease (ILD) extent were quantified visually; ILD extent was also quantified by a software tool, CALIPER. Echocardiographic criteria categorized PHT risk. RESULTS: The prevalence of an increased PHT likelihood was 29% and 31% in each CPFE cohort. Survival at 12 months was 60% across both CPFE cohorts with no significantly worsened outcome identified when compared with IPF patients without emphysema. Using logistic regression models in both cohorts, total computed tomography (CT) disease extent (ILD and emphysema) predicted the likelihood of PHT. After adjustment for total disease extent, CPFE had no stronger association with PHT likelihood than IPF patients without emphysema. CONCLUSION: Our findings indicate that the reported association between CPFE and PHT is explained by the summed baseline CT extents of ILD and emphysema. Once baseline severity is taken into account, CPFE is not selectively associated with a malignant microvascular phenotype, when compared with IPF patients without emphysema.

Functional and prognostic effects when emphysema complicates idiopathic pulmonary fibrosis.

This study aimed to investigate whether the combination of fibrosis and emphysema has a greater effect than the sum of its parts on functional indices and outcome in idiopathic pulmonary fibrosis (IPF), using visual and computer-based (CALIPER) computed tomography (CT) analysis.Consecutive patients (n=272) with a multidisciplinary IPF diagnosis had the extent of interstitial lung disease (ILD) scored visually and by CALIPER. Visually scored emphysema was subcategorised as isolated or mixed with fibrotic lung. The CT scores were evaluated against functional indices forced vital capacity (FVC), diffusing capacity of the lungs for carbon monoxide (DLCO), transfer coefficient of the lung for carbon monoxide (KCO), composite physiologic index (CPI)) and mortality.The presence and extent of emphysema had no impact on survival. Results were maintained following correction for age, gender, smoking status and baseline severity using DLCO, and combined visual emphysema and ILD extent. Visual emphysema quantitation indicated that relative preservation of lung volumes (FVC) resulted from tractionally dilated airways within fibrotic lung, ventilating areas of admixed emphysema (p<0.0001), with no independent effect on FVC from isolated emphysema. Conversely, only isolated emphysema (p<0.0001) reduced gas transfer (DLCO).There is no prognostic impact of emphysema in IPF, beyond that explained by the additive extents of both fibrosis and emphysema. With respect to the location of pulmonary fibrosis, emphysema distribution determines the functional effects of emphysema.

Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures.

Computer-based computed tomography (CT) analysis can provide objective quantitation of disease in idiopathic pulmonary fibrosis (IPF). A computer algorithm, CALIPER, was compared with conventional CT and pulmonary function measures of disease severity for mortality prediction.CT and pulmonary function variables (forced expiratory volume in 1 s, forced vital capacity, diffusion capacity of the lung for carbon monoxide, transfer coefficient of the lung for carbon monoxide and composite physiologic index (CPI)) of 283 consecutive patients with a multidisciplinary diagnosis of IPF were evaluated against mortality. Visual and CALIPER CT features included total extent of interstitial lung disease, honeycombing, reticular pattern, ground glass opacities and emphysema. In addition, CALIPER scored pulmonary vessel volume (PVV) while traction bronchiectasis and consolidation were only scored visually. A combination of mortality predictors was compared with the Gender, Age, Physiology model.On univariate analyses, all visual and CALIPER-derived interstitial features and functional indices were predictive of mortality to a 0.01 level of significance. On multivariate analysis, visual CT parameters were discarded. Independent predictors of mortality were CPI (hazard ratio (95% CI) 1.05 (1.02-1.07), p<0.001) and two CALIPER parameters: PVV (1.23 (1.08-1.40), p=0.001) and honeycombing (1.18 (1.06-1.32), p=0.002). A three-group staging system derived from this model was powerfully predictive of mortality (2.23 (1.85-2.69), p<0.0001).CALIPER-derived parameters, in particular PVV, are more accurate prognostically than traditional visual CT scores. Quantitative tools such as CALIPER have the potential to improve staging systems in IPF.

Evaluation of visual and computer-based CT analysis for the identification of functional patterns of obstruction and restriction in hypersensitivity pneumonitis.

BACKGROUND AND OBJECTIVE: To determine whether computer-based quantification (CALIPER software) is superior to visual computed tomography (CT) scoring in the identification of CT patterns indicative of restrictive and obstructive functional indices in hypersensitivity pneumonitis (HP). METHODS: A total of 135 consecutive HP patients had CT parenchymal patterns evaluated quantitatively by both visual scoring and CALIPER. Results were evaluated against: forced vital capacity (FVC), total lung capacity (TLC), diffusing capacity for carbon monoxide (DLCO ) and a composite physiological index (CPI) to identify which CT scoring method better correlated with functional indices. RESULTS: CALIPER-derived scores of total interstitial lung disease extent correlated more strongly than visual scores: FVC (CALIPER R = 0.73, visual R = 0.51); DLCO (CALIPER R = 0.61, visual R = 0.48); and CPI (CALIPER R = 0·70, visual R = 0·55). The CT variable that correlated most strongly with restrictive functional indices was CALIPER pulmonary vessel volume (PVV): FVC R = 0.75, DLCO R = 0.68 and CPI R = 0.76. Ground-glass opacity quantified by CALIPER alone demonstrated strong associations with restrictive functional indices: CALIPER FVC R = 0.65; DLCO R = 0.59; CPI R = 0.64; and visual = not significant. Decreased attenuation lung quantified by CALIPER was a better morphological measure of obstructive lung disease than equivalent visual scores as judged by relationships with TLC (CALIPER R = 0.63 and visual R = 0.12). All results were maintained on multivariate analysis. CONCLUSION: CALIPER improved on visual scoring in HP as judged by restrictive and obstructive functional correlations. Decreased attenuation regions of the lung quantified by CALIPER demonstrated better linkages to obstructive lung physiology than visually quantified CT scores. A novel CALIPER variable, the PVV, demonstrated the strongest linkages with restrictive functional indices and could represent a new automated index of disease severity in HP.

Chronic hypersensitivity pneumonitis: identification of key prognostic determinants using automated CT analysis.

BACKGROUND: Chronic hypersensitivity pneumonitis (CHP) has a variable disease course. Computer analysis of CT features was used to identify a subset of CHP patients with an outcome similar to patients with idiopathic pulmonary fibrosis (IPF). METHODS: Consecutive patients with a multi-disciplinary team diagnosis of CHP (n = 116) had pulmonary function tests (FEV1, FVC, DLco, Kco, and a composite physiologic index [CPI]) and CT variables predictive of mortality evaluated by analysing visual and computer-based (CALIPER) parenchymal features: total interstitial lung disease (ILD) extent, honeycombing, reticular pattern, ground glass opacities, pulmonary vessel volume (PVV), emphysema, and traction bronchiectasis. Mean survival was compared between both CHP and IPF patients (n = 185). RESULTS: In CHP, visual/CALIPER measures of reticular pattern, honeycombing, visual traction bronchiectasis, and CALIPER ILD extent were predictive of mortality (p < 0 · 05) on univariate analysis. PVV was strongly predictive of mortality on univariate (p < 0 · 0001) and multivariate analysis independent of age, gender and disease severity (represented by the CPI [p < 0 · 01]). CHP patients with a PVV threshold >6 · 5% of the lung had a mean survival (35 · 3 ± 6 · 1 months; n = 20/116 [17%]) and rate of disease progression that closely matched IPF patients (38 · 4 ± 2 · 2 months; n = 185). CONCLUSIONS: Pulmonary vessel volume can identify CHP patients at risk of aggressive disease and a poor IPF-like prognosis.

Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study.

BACKGROUND: To evaluate computer-based computer tomography (CT) analysis (CALIPER) against visual CT scoring and pulmonary function tests (PFTs) when predicting mortality in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). To identify outcome differences between distinct CTD-ILD groups derived following automated stratification of CALIPER variables. METHODS: A total of 203 consecutive patients with assorted CTD-ILDs had CT parenchymal patterns evaluated by CALIPER and visual CT scoring: honeycombing, reticular pattern, ground glass opacities, pulmonary vessel volume, emphysema, and traction bronchiectasis. CT scores were evaluated against pulmonary function tests: forced vital capacity, diffusing capacity for carbon monoxide, carbon monoxide transfer coefficient, and composite physiologic index for mortality analysis. Automated stratification of CALIPER-CT variables was evaluated in place of and alongside forced vital capacity and diffusing capacity for carbon monoxide in the ILD gender, age physiology (ILD-GAP) model using receiver operating characteristic curve analysis. RESULTS: Cox regression analyses identified four independent predictors of mortality: patient age (P < 0.0001), smoking history (P = 0.0003), carbon monoxide transfer coefficient (P = 0.003), and pulmonary vessel volume (P < 0.0001). Automated stratification of CALIPER variables identified three morphologically distinct groups which were stronger predictors of mortality than all CT and functional indices. The Stratified-CT model substituted automated stratified groups for functional indices in the ILD-GAP model and maintained model strength (area under curve (AUC) = 0.74, P < 0.0001), ILD-GAP (AUC = 0.72, P < 0.0001). Combining automated stratified groups with the ILD-GAP model (stratified CT-GAP model) strengthened predictions of 1- and 2-year mortality: ILD-GAP (AUC = 0.87 and 0.86, respectively); stratified CT-GAP (AUC = 0.89 and 0.88, respectively). CONCLUSIONS: CALIPER-derived pulmonary vessel volume is an independent predictor of mortality across all CTD-ILD patients. Furthermore, automated stratification of CALIPER CT variables represents a novel method of prognostication at least as robust as PFTs in CTD-ILD patients.

Noninvasive Computed Tomography-based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial.

RATIONALE: Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification. OBJECTIVES: To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes. METHODS: We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. MEASUREMENTS AND MAIN RESULTS: A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases. CONCLUSIONS: CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.

Comparison of manual and semiautomated techniques for analyzing gastric volumes with MRI in humans.

Gastric emptying, accommodation, and motility can be quantified with magnetic resonance imaging (MRI). The first step in image analysis entails segmenting the stomach from surrounding structures, usually by a time-consuming manual process. We have developed a semiautomated process to segment and measure gastric volumes with MRI. Gastric images were acquired with a three-dimensional gradient echo MRI sequence at 5, 10, 20, and 30 min after ingestion of a liquid nutrient (Ensure, 296 ml) labeled with gadolinium in 20 healthy volunteers and 29 patients with dyspeptic symptoms. The agreement between gastric volumes measured by manual segmentation and our new semiautomated algorithm was assessed with Lin's concordance correlation coefficient (CCC) and the Bland Altman test. At 5 min after a meal, food volumes measured by manual (352 ± 4 ml) and semiautomated (346 ± 4 ml) techniques were correlated {CCC[95% confidence interval (CI)] 0.70 (0.52, 0.81)}; air volumes measured by manual (88 ± 6 ml) and semiautomated (84 ± 6 ml) techniques were also correlated [CCC (95% CI) 0.89 (0.82, 0.94)]. Findings were similar at subsequent time points. The Bland Altman test was not significant. The time required for semiautomated segmentation ranged from an average of 204 s for the 5-min images to 233 s for the 20-min images. These times were appreciably smaller than the typical times of many tens of minutes, even hours, required for manual segmentation. To conclude, a semiautomated process can measure gastric food and air volume using MRI with comparable accuracy and far better efficiency than a manual process.