Controlled Self-Assembly of Nanoscale Superstructures in Phase-Change Ge-Sb-Te Nanowires.

Controlled growth of semiconductor nanowires with atomic precision offers the potential to tune the material properties for integration into scalable functional devices. Despite significant progress in understanding the nanowire growth mechanism, definitive control over atomic positions of its constituents, structure, and morphology via self-assembly remains challenging. Here, we demonstrate an exquisite control over synthesis of cation-ordered nanoscale superstructures in Ge-Sb-Te nanowires with the ability to deterministically vary the nanowire growth direction, crystal facets, and periodicity of cation ordering by tuning the relative precursor flux during synthesis. Furthermore, the role of anisotropy on material properties in cation-ordered nanowire superstructures is illustrated by fabricating phase-change memory (PCM) devices, which show significantly different growth direction dependent amorphization current density. This level of control in synthesizing chemically ordered nanoscale superstructures holds potential to precisely modulate fundamental material properties such as the electronic and thermal transport, which may have implications for PCM, thermoelectrics, and other nanoelectronic devices.

Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA.

While CT lung cancer screening reduces lung cancer-specific mortality, there are remaining challenges. Radiomic tools promiss to address these challenges, however, they are subject to interobserver variability if semi-automated segmentation techniques are used. Herein we report interobserver variability for two validated radiomic tools, BRODERS (Benign versus aggRessive nODule Evaluation using Radiomic Stratification) and CANARY (Computer-Aided Nodule Assessment and Risk Yield). We retrospectively analyzed the CT images of 95 malignant lung nodules of the adenocarcinoma spectrum using BRODERS and CANARY. Cases were identified at Mayo Clinic (n = 45) and Vanderbilt University Medical Center and Nashville/Veteran Administration Tennessee Valley Health Care System (n = 50). Three observers with different training levels (medical student, internal medicine resident and thoracic radiology fellow) each performed lung nodule segmentation. All methods were carried out in accordance with relevant guidelines and regulations. Interclass correlation coefficients (ICC) of 0.77, 0.98 and 0.97 for the average nodule volume, BRODERS cancer probability and Score Indicative of Lesion Aggression (SILA) which summarizes the distribution of the CANARY exemplars indicated good to excellent reliability, respectively. The dice similarity coefficient was 0.79 and 0.81 for the data sets from the two institutions. BRODERS and CANARY are robust radiomics tools with excellent interobserver variability. These tools are simple and reliable regardless the observer/operator's level of training.

Bronchoscopic Lung Volume Reduction: Highlighting the Patient Selection Process.

Patients with advanced emphysema frequently experience severe dyspnea that is inadequately treated with medical therapy alone. Over the past 4 years, we have seen increased usage of bronchoscopic lung volume reduction (BLVR) with endobronchial valves. Success of the procedure is dependent on patient selection because it is not necessarily appropriate for all patients with severe emphysema. (Table 1) The purpose of this review is to highlight the selection process at a single institution. We also discuss the influence of this process on outcomes. Between March 1, 2019, and October 12, 2021, 2402 patients were referred to a dedicated chronic obstructive pulmonary disease clinic at Mayo Clinic - Rochester, whereas 161 patients were subsequently referred for multidisciplinary BLVR review. Of those patients, 53 (32.9%) were selected to receive valves and 35 (21.7%) ultimately underwent the procedure. The main reasons for exclusion included an incompatible quantitative computed tomography analysis (n=37, 34.3%), presence of pulmonary nodule or active malignancy (n=14, 13.0%), or need for completion of pulmonary rehabilitation (n=9, 8.3%). Full or partial (>70%) target lobe collapse was observed in 58.6% of patients who received valves. Those with collapse experienced statistically significant improvements in spirometric measures. Twelve patients experienced a pneumothorax (34.3%), with 10 patients requiring thoracostomy tube placement and prolonged hospitalization (median, 11 days; range, 4-39 days). Nineteen patients required a secondary procedure within the first year. The study highlights how a multidisciplinary approach to the BLVR selection process enables individualization of a complex procedure and enhances the exclusion of inappropriate candidates.

Decarbonization of the Indian electricity sector: Technology choices and policy trade-offs.

India is the third largest CO2 emitter worldwide, and its electricity demand, which is primarily supplied by coal-fired generation, is expected to increase almost threefold over the next twenty years. Here, we simulate 40 scenarios for the 2040 Indian electricity sector, considering uncertainty in future natural gas prices and costs for batteries and variable renewable energy (VRE) technologies, under different CO2 emissions limits and renewable portfolio standard (RPS) targets. We find a large-scale expansion of VRE, particularly, solar PV, in most scenarios. Furthermore, energy storage competes with natural gas and coal to provide flexibility to integrate VRE. Given a set of technology assumptions, policies that explicitly limit CO2 emissions are more cost-effective at reducing emissions than RPS policies. The former are also more effective at reducing air pollution than RPS policies by explicitly penalizing CO2 emissions, thereby reducing coal generation more substantially than RPS policies.

Pneumococcal vaccination coverage among adults aged 19 to 64 years with immuno-compromising conditions, cerebrospinal fluid (CSF) leaks, or cochlear implants in the US.

Background: Adults with immuno-compromising conditions, CSF leaks, or cochlear implants are at increased risk for pneumococcal disease (high-risk patients), yet pneumococcal vaccination rates in the US for this group are low.Methods: A retrospective cohort analysis was conducted from 2010 to 2018 using the Truven Health MarketScan database to estimate pneumococcal vaccination coverage among adults aged 19 to 64 years newly diagnosed with high-risk conditions, and to assess factors associated with receiving the recommended pneumococcal vaccines.Results: The study sample included 2,497,799 adults aged 19 to 64 years old with newly diagnosed high-risk conditions. Most of the study cohort had seven or more annual physician office (52%) and pharmacy (56%) visits. The proportion of high-risk adults who received at least one pneumococcal vaccination increased from 5.4% after 1 year of follow-up to 14.2% after 6 years of follow-up. Compared to those who received no pneumococcal vaccination, high-risk adults who received any pneumococcal vaccination were more likely to be older, female, enrolled in an HMO, had more healthcare encounters, and were treated by a primary care provider.Conclusion: Despite numerous healthcare encounters annually, very few high-risk adults received pneumococcal vaccines, highlighting the need for implementing targeted interventions to increase vaccine uptake in this vulnerable population.

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.

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.

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.

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.

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.

ODM-203, a Selective Inhibitor of FGFR and VEGFR, Shows Strong Antitumor Activity, and Induces Antitumor Immunity.

Alterations in the gene encoding for the FGFR and upregulation of the VEGFR are found often in cancer, which correlate with disease progression and unfavorable survival. In addition, FGFR and VEGFR signaling synergistically promote tumor angiogenesis, and activation of FGFR signaling has been described as functional compensatory angiogenic signal following development of resistance to VEGFR inhibition. Several selective small-molecule FGFR kinase inhibitors are currently in clinical development. ODM-203 is a novel, selective, and equipotent inhibitor of the FGFR and VEGFR families. In this report we show that ODM-203 inhibits FGFR and VEGFR family kinases selectively and with equal potency in the low nanomolar range (IC50 6-35 nmol/L) in biochemical assays. In cellular assays, ODM-203 inhibits VEGFR-induced tube formation (IC50 33 nmol/L) with similar potency as it inhibits proliferation in FGFR-dependent cell lines (IC50 50-150 nmol/L). In vivo, ODM-203 shows strong antitumor activity in both FGFR-dependent xenograft models and in an angiogenic xenograft model at similar well-tolerated doses. In addition, ODM-203 inhibits metastatic tumor growth in a highly angiogenesis-dependent kidney capsule syngenic model. Interestingly, potent antitumor activity in the subcutaneous syngenic model correlated well with immune modulation in the tumor microenvironment as indicated by marked decrease in the expression of immune check points PD-1 and PD-L1 on CD8 T cells and NK cells, and increased activation of CD8 T cells. In summary, ODM-203 shows equipotent activity for both FGFR and VEGFR kinase families and antitumor activity in both FGFR and angigogenesis models.

Correction: Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial.

[This corrects the article DOI: 10.1371/journal.pone.0196910.].

Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma: radiologic biopsy, risk stratification and future directions.

The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Nodule Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment of tumor characteristics. This analysis may obviate the need for tissue biopsy and facilitate the risk stratification of adenocarcinoma of the lung. CANARY was developed by unsupervised machine learning techniques using CT data of histopathologically-characterized adenocarcinomas of the lung. This technique identified 9 distinct exemplars that constitute the spectrum of CT features found in adenocarcinoma of the lung. The distributions of these features in a nodule correlate with histopathology. Further automated clustering of CANARY nodules defined three distinct groups that have distinctly different post-resection disease free survival (DFS). CANARY has been validated within the NLST cohort and multiple other cohorts. Using semi-automated segmentation as input to CANARY, there is excellent repeatability and interoperator correlation of results. Confirmation and longitudinal tracking of indolent adenocarcinoma with CANARY may ultimately add decision support in nuanced cases where surgery may not be in the best interest of the patient due to competing comorbidity. Currently under investigation is CANARY's role in detecting differing driver mutations and tumor response to targeted chemotherapeutics. Combining the results from CANARY analysis with clinical information and other quantitative techniques such as analysis of the tumor-free surrounding lung may aid in building more powerful predictive models. The next step in CANARY investigation will be its prospective application, both in selecting low-risk stage 1 adenocarcinoma for active surveillance and investigation in selecting high-risk early stage adenocarcinoma for adjuvant therapy.

Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas.

Lung adenocarcinoma (ADC), the most common lung cancer type, is recognized increasingly as a disease spectrum. To guide individualized patient care, a non-invasive means of distinguishing indolent from aggressive ADC subtypes is needed urgently. Computer-Aided Nodule Assessment and Risk Yield (CANARY) is a novel computed tomography (CT) tool that characterizes early ADCs by detecting nine distinct CT voxel classes, representing a spectrum of lepidic to invasive growth, within an ADC. CANARY characterization has been shown to correlate with ADC histology and patient outcomes. This study evaluated the inter-observer variability of CANARY analysis. Three novice observers segmented and analyzed independently 95 biopsy-confirmed lung ADCs from Vanderbilt University Medical Center/Nashville Veterans Administration Tennessee Valley Healthcare system (VUMC/TVHS) and the Mayo Clinic (Mayo). Inter-observer variability was measured using intra-class correlation coefficient (ICC). The average ICC for all CANARY classes was 0.828 (95% CI 0.76, 0.895) for the VUMC/TVHS cohort, and 0.852 (95% CI 0.804, 0.901) for the Mayo cohort. The most invasive voxel classes had the highest ICC values. To determine whether nodule size influenced inter-observer variability, an additional cohort of 49 sub-centimeter nodules from Mayo were also segmented by three observers, with similar ICC results. Our study demonstrates that CANARY ADC classification between novice CANARY users has an acceptably low degree of variability, and supports the further development of CANARY for clinical application.

Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial.

PURPOSE: Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules. MATERIAL AND METHODS: Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model. RESULTS: Eight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939. CONCLUSIONS: Our novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed.

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.

High intensity lifestyle intervention and long-term impact on weight and clinical outcomes.

BACKGROUND: Obesity increases the risk for diabetes and cardiovascular events, with a corresponding growth in medical costs. High intensity lifestyle intervention (HILI) is the cornerstone for weight management. We assessed the effectiveness of clinic-based HILI on weight loss and associated clinical outcomes by duration of program participation and comorbid conditions. METHODS: This was a retrospective cohort study of patients who enrolled in HILI weight management programs at Health Management Resources (HMR) clinics located across the U.S. Patients completed health risk assessments (HRA) and were enrolled for up to 24 months at the time of follow-up HRA. HMR programs provide weekly group coaching to achieve reduced calorie intake, increased fruit/vegetable intake, and physical activity ≥2,000 kcal/wk. A Markov model predicted avoidance of diabetes and cardiovascular events and projected cost savings due to weight loss. RESULTS: Of the 500 patients included in the analysis, 67% were female and mean age was 54.1 years (s.d. 11.6). The baseline weight and BMI were 243.5 lbs (range 144.0-545.0) and 38.8 kg/m2 (range 25.4-85.0), respectively. Overall, patients lost an average of 47.4 lbs (18.9% of initial body weight [IBW]); the amount of weight loss was consistent among those with diabetes/pre-diabetes (50%), high/moderate risk for dyslipidemia (60%), hypertension/pre-hypertension (86%), and severe obesity (37%). The mean IBW lost was 16.4%, 19.3%, 20.7% for ≤6 months (n = 165), 7-12 months (n = 140), 13-24 months (n = 195) of program participation, respectively. The simulation model estimated 22 diabetes and 30 cardiovascular events and $1,992,370 medical costs avoided over 5 years in the 500 patients evaluated. CONCLUSION: Patients in the HMR clinic-based HILI program achieved substantial weight loss regardless of duration of program participation, risk profile and comorbid status. The HMR program could be an effective strategy to prevent costly diabetes and cardiovascular events, particularly in high risk patients.

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.