Evaluating Tissue Heterogeneity in the Radiologically Normal-Appearing Tissue in IPF Compared to Healthy Controls.

RATIONALE AND OBJECTIVES: Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease characterised by heterogeneously distributed fibrotic lesions. The inter- and intra-patient heterogeneity of the disease has meant that useful biomarkers of severity and progression have been elusive. Previous quantitative computed tomography (CT) based studies have focussed on characterising the pathological tissue. However, we hypothesised that the remaining lung tissue, which appears radiologically normal, may show important differences from controls in tissue characteristics. MATERIALS AND METHODS: Quantitative metrics were derived from CT scans in IPF patients (N = 20) and healthy controls with a similar age (N = 59). An automated quantitative software (CALIPER, Computer-Aided Lung Informatics for Pathology Evaluation and Rating) was used to classify tissue as normal-appearing, fibrosis, or low attenuation area. Densitometry metrics were calculated for all lung tissue and for only the normal-appearing tissue. Heterogeneity of lung tissue density was quantified as coefficient of variation and by quadtree. Associations between measured lung function and quantitative metrics were assessed and compared between the two cohorts. RESULTS: All metrics were significantly different between controls and IPF (p < 0.05), including when only the normal tissue was evaluated (p < 0.04). Density in the normal tissue was 14% higher in the IPF participants than controls (p < 0.001). The normal-appearing tissue in IPF had heterogeneity metrics that exhibited significant positive relationships with the percent predicted diffusion capacity for carbon monoxide. CONCLUSION: We provide quantitative assessment of IPF lung tissue characteristics compared to a healthy control group of similar age. Tissue that appears visually normal in IPF exhibits subtle but quantifiable differences that are associated with lung function and gas exchange.

Voxel-Wise Comparison of Co-Registered Quantitative CT and Hyperpolarised Gas Diffusion-Weighted MRI Measurements in IPF.

The patterns of idiopathic pulmonary fibrosis (IPF) lung disease that directly correspond to elevated hyperpolarised gas diffusion-weighted (DW) MRI metrics are currently unknown. This study aims to develop a spatial co-registration framework for a voxel-wise comparison of hyperpolarised gas DW-MRI and CALIPER quantitative CT patterns. Sixteen IPF patients underwent 3He DW-MRI and CT at baseline, and eleven patients had a 1-year follow-up DW-MRI. Six healthy volunteers underwent 129Xe DW-MRI at baseline only. Moreover, 3He DW-MRI was indirectly co-registered to CT via spatially aligned 3He ventilation and structural 1H MRI. A voxel-wise comparison of the overlapping 3He apparent diffusion coefficient (ADC) and mean acinar dimension (LmD) maps with CALIPER CT patterns was performed at baseline and after 1 year. The abnormal lung percentage classified with the LmD value, based on a healthy volunteer 129Xe LmD, and CALIPER was compared with a Bland-Altman analysis. The largest DW-MRI metrics were found in the regions classified as honeycombing, and longitudinal DW-MRI changes were observed in the baseline-classified reticular changes and ground-glass opacities regions. A mean bias of -15.3% (95% interval -56.8% to 26.2%) towards CALIPER was observed for the abnormal lung percentage. This suggests DW-MRI may detect microstructural changes in areas of the lung that are determined visibly and quantitatively normal by CT.

Pulmonary vessel volume in idiopathic pulmonary fibrosis compared with healthy controls aged > 50 years.

Idiopathic pulmonary fibrosis (IPF) is characterised by progressive fibrosing interstitial pneumonia with an associated irreversible decline in lung function and quality of life. IPF prevalence increases with age, appearing most frequently in patients aged > 50 years. Pulmonary vessel-like volume (PVV) has been found to be an independent predictor of mortality in IPF and other interstitial lung diseases, however its estimation can be impacted by artefacts associated with image segmentation methods and can be confounded by adjacent fibrosis. This study compares PVV in IPF patients (N = 21) with PVV from a healthy cohort aged > 50 years (N = 59). The analysis includes a connected graph-based approach that aims to minimise artefacts contributing to calculation of PVV. We show that despite a relatively low extent of fibrosis in the IPF cohort (20% of the lung volume), PVV is 2-3 times higher than in controls. This suggests that a standardised method to calculate PVV that accounts for tree connectivity could provide a promising tool to provide early diagnostic or prognostic information in IPF patients and other interstitial lung disease.

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.

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.

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.

Differentiation of Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Related Interstitial Lung Disease Using Quantitative Imaging.

A usual interstitial pneumonia (UIP) imaging pattern can be seen in both idiopathic pulmonary fibrosis (IPF) and connective tissue disease-related interstitial lung disease (CTD-ILD). The purpose of this multicenter study was to assess whether quantitative imaging data differ between IPF and CTD-ILD in the setting of UIP. Patients evaluated at two medical centers with CTD-ILD or IPF and a UIP pattern on CT or pathology served as derivation and validation cohorts. Chest CT data were quantitatively analyzed including total volumes of honeycombing, reticulation, ground-glass opacity, normal lung, and vessel related structures (VRS). VRS was compared with forced vital capacity percent predicted (FVC%) and percent predicted diffusing capacity of the lungs for carbon monoxide (DLCO%). There were 296 subjects in total, with 40 CTD-ILD and 85 IPF subjects in the derivation cohort, and 62 CTD-ILD and 109 IPF subjects in the validation cohort. VRS was greater in IPF across the cohorts on univariate (p < 0.001) and multivariable (p < 0.001-0.047) analyses. VRS was inversely correlated with DLCO% in both cohorts on univariate (p < 0.001) and in the derivation cohort on multivariable analysis (p = 0.003) but not FVC%. Total volume of normal lung was associated with DLCO% (p < 0.001) and FVC% (p < 0.001-0.009) on multivariable analysis in both cohorts. VRS appears to have promise in differentiating CTD-ILD from IPF. The underlying pathophysiological relationship between VRS and ILD is complex and is likely not explained solely by lung fibrosis.

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.

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.

Automated CT Analysis of Major Forms of Interstitial Lung Disease.

This study aimed to determine diagnostic and prognostic differences in major forms of interstitial lung disease using quantitative CT imaging. A retrospective study of 225 subjects with a multidisciplinary diagnosis of idiopathic pulmonary fibrosis (IPF), interstitial pneumonia with autoimmune features (IPAF), connective tissue disease (CTD), or chronic hypersensitivity pneumonitis (cHP) was conducted. Non-contrast CT scans were analyzed using the Computer Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) program. Resulting data were analyzed statistically using ANOVA and Student's t-test. Univariate, multivariable, and receiver operating characteristic analyses were conducted on patient mortality data. CALIPER analysis of axial distribution on CT scans in those with IPF demonstrated greater peripheral volumes of reticulation than either CTD (p = 0.033) or cHP (p = 0.007). CTD showed lower peripheral ground-glass opacity than IPF (p = 0.005) and IPAF (p = 0.004). Statistical analysis of zonal distributions revealed reduced lower zone ground-glass opacity in cHP than IPF (p = 0.044) or IPAF (p = 0.018). Analysis of pulmonary vascular-related structure (VRS) volume by diagnosis indicated greater VRS volume in IPF compared to CTD (p = 0.003) and cHP (p = 0.003) as well as in IPAF compared to CTD (p = 0.007) and cHP (p = 0.007). Increased reticulation (p = 0.043) and ground glass opacity (p = 0.032) were predictive of mortality on univariate analysis. Increased pulmonary VRS volume was predictive of mortality (p < 0.001) even after multivariate analysis (p = 0.041). Quantitative CT imaging revealed significant differences between ILD diagnoses in specific CT findings in axial and, to a lesser degree, zonal distributions. Increased pulmonary VRS volume seems to be associated with both diagnosis and survival.

Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification.

Despite multiple recent advances, the diagnosis and management of lung cancer remain challenging and it continues to be the deadliest malignancy. In 2011, the National Lung Screening Trial (NLST) reported 20% reduction in lung cancer related mortality using annual low dose chest computed tomography (CT). These results led to the approval and nationwide establishment of lung cancer CT-based lung cancer screening programs. These findings have been further validated by the recently published Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) and Multicentric Italian Lung Detection (MILD) trials, the latter showing benefit of screening even beyond the 5 years. However, the implementation of lung cancer screening has been impeded by several challenges, including the differentiation between benign and malignant nodules, the large number of false positive studies and the detection of indolent, potentially clinically insignificant lung cancers (overdiagnosis). Hence, the development of non-invasive strategies to accurately classify and risk stratify screen-detected pulmonary nodules in order to individualize clinical management remains a high priority area of research. Radiomics is a recently coined term which refers to the process of imaging feature extraction and quantitative analysis of clinical diagnostic images to characterize the nodule phenotype beyond what is possible with conventional radiologist assessment. Even though it is still in early phase, several studies have already demonstrated that radiomics approaches are potentially useful for lung nodule classification, risk stratification, individualized management and prediction of overall prognosis. The goal of this review is to summarize the current literature regarding the radiomics of screen-detected lung nodules, highlight potential challenges and discuss its clinical application along with future goals and challenges.

Automated Computed Tomography analysis in the assessment of Idiopathic Pulmonary Fibrosis severity and progression.

PURPOSE: To investigate the role of a quantitative analysis software (CALIPER) in identifying HRCT thresholds predicting IPF patients' survival and lung function decline and its role in detecting changes of HRCT abnormalities related to treatment and their correlation with Forced Vital Capacity (FVC). METHODS: This retrospective study included 105 patients with a multidisciplinary diagnosis of IPF for whom one HRCT at baseline and concomitant FVC were available. HRCTs were evaluated with CALIPER and the correlation between FVC and radiological features were assessed. Radiological thresholds for survival prediction and functional decline were calculated for all patients. Fifty-nine patients with at least 2 serial HRCTs were classified into two groups based on treatment. For patients for whom a FVC within 3 months of the HRCT was available (n = 44), the correlation of radiological and clinical progression was evaluated. RESULTS: The correlation between FVC and CALIPER-derived features at baseline was significant and strong. A baseline CALIPER-derived interstitial lung disease (ILD%) extent higher than 20 % and pulmonary vascular related structures (PVRS%) score greater than 5 % defined a worse prognosis. A significant progression of CALIPER-derived features in all patients was found with a faster increase in untreated patients. ILD% and PVRS% changes during follow-up demonstrated strong correlations with FVC changes. CONCLUSIONS: CALIPER quantification of fibrosis and vascular involvement could distinguish disease progression in treated versus untreated patients and predict the survival. The changes in CALIPER-derived variables over time were significantly correlated to changes in FVC.

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.

Computed Tomography-Based Score Indicative of Lung Cancer Aggression (SILA) Predicts the Degree of Histologic Tissue Invasion and Patient Survival in Lung Adenocarcinoma Spectrum.

OBJECTIVE: Most computed tomography (CT)-detected lung cancers are adenocarcinomas (ACs), representing lesions with variable tissue invasion, aggressiveness, and clinical outcome. Visual radiologic characterization of AC pulmonary nodules is both inconsistent and inadequate to confidently predict histopathologic classification or prognosis. Comprehensive pathologic interpretation requires full nodule resection. We have described a computerized scoring system for AC detected on CT scans that can noninvasively estimate the degree of histologic invasion and simultaneously predict patient survival. METHODS: The Computer-Aided Nodule Assessment and Risk Yield has been validated to characterize CT-detected nodules across the spectrum of AC. With the use of unsupervised clustering, nine natural exemplars were identified as basic radiographic features of AC nodules. We now introduce the Score Indicative of Lung Cancer Aggression (SILA), which is a cumulative aggregate of normalized distributions of ordered Computer-Aided Nodule Assessment and Risk Yield exemplars. The SILA values for each of 237 unique nodules in AC were compared with the histopathologically defined maximum linear extent of tumor invasion. With use of the SILA, Kaplan-Meier survival and Cox proportionality analysis were performed on patients with stage I AC, who comprised a subset of our cohort. RESULTS: The SILA discriminated between indolent and invasive AC (p < 0.0001). In addition, prediction of linear extent of histopathologic tumor invasion was possible. In stage I AC, three separate SILA prognosis groups were identified: indolent, intermediate, and poor, with 5-year survival rates of 100%, 79%, 58%, respectively. Cox proportionality hazard modeling predicted a 50% increase in mortality, for a 0.1 unit increase in the SILA over a median follow-up time of 3.6 years (p < 0.0002). CONCLUSIONS: The SILA is a computer-based analytic measure allowing noninvasive approximation of histologic invasion and prediction of patient survival in CT-detected AC nodules.

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.

Comparison of changes in rectal area and volume during MR evacuation proctography in healthy and constipated adults.

BACKGROUND: During proctography, rectal emptying is visually estimated by the reduction in rectal area. The correlation between changes in rectal area, which is a surrogate measure of volume, is unclear. Our aims were to compare the change in rectal area and volume during magnetic resonance (MR) proctography and to compare these parameters with rectal balloon expulsion time (BET). METHODS: In 49 healthy and 46 constipated participants, we measured BET and rectal area and volume with a software program before and after participants expelled rectal gel during proctography. KEY RESULTS: All participants completed both tests; six healthy and 17 constipated patients had a prolonged (>60 seconds) BET. During evacuation, the reduction in rectal area and volume was lower in participants with an abnormal than a normal BET (P < 0.01). The reduction in rectal area and volume were strongly correlated (r = 0.93, P < 0.001) and equivalent for identifying participants with abnormal BET. Among participants with less evacuation, the reduction in rectal area underestimated the reduction in rectal volume. A rectocele larger than 2 cm was observed in eight of 18 (44%) participants in whom the difference between change in volume and area was ˃10% but only 14 of 77 (18%) participants in whom the difference was ≤10% (P = 0.03). CONCLUSIONS: Measured with MR proctography, the rectal area is reasonably accurate for quantifying rectal emptying and equivalent to rectal volume for distinguishing between normal and abnormal BET. When evacuation is reduced, the change in rectal area may underestimate the change in rectal volume.

Assessment of Interstitial Lung Disease Using Lung Ultrasound Surface Wave Elastography: A Novel Technique With Clinicoradiologic Correlates.

PURPOSE: Optimal strategies to detect early interstitial lung disease (ILD) are unknown. ILD is frequently subpleural in distribution and affects lung elasticity. Lung ultrasound surface wave elastography (LUSWE) is a noninvasive method of quantifying superficial lung tissue elastic properties. In LUWSE a handheld device applied at the intercostal space vibrates the chest at a set frequency, and the lung surface wave velocity is measured by an ultrasound probe 5 mm away in the same intercostal space. We explored LUWSE's ability to detect ILD and correlated LUSWE velocity with physiological, quantitative, and visual radiologic features of subjects with known ILD and of healthy controls. MATERIALS AND METHODS: Seventy-seven subjects with ILD, mostly caused by connective tissue disease, and 19 healthy controls were recruited. LUSWE was performed on all subjects in 3 intercostal lung regions bilaterally. Comparison of LUSWE velocities pulmonary function testing, visual assessment, and quantitative analysis of recent computed tomographic imaging with Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) software. RESULTS: Sonographic velocities were higher in all lung regions for cases, with the greatest difference in the lateral lower lung. Median velocity in m/s was 5.84 versus 4.11 and 5.96 versus 4.27 (P<0.00001) for cases versus controls, left and right lateral lower lung zones, respectively. LUSWE velocity correlated negatively with vital capacity and positively with radiologist and CALIPER-detected interstitial abnormalities. CONCLUSIONS: LUSWE is a safe and noninvasive technique that shows high sensitivity to detect ILD and correlated with clinical, physiological, radiologic, and quantitative assessments of ILD. Prospective study in detecting ILD is indicated.