Menstrual cycle impacts lung structure measures derived from quantitative computed tomography.

OBJECTIVE: Quantitative computed tomography (qCT) is being increasingly incorporated in research studies and clinical trials aimed at understanding lung disease risk, progression, exacerbations, and intervention response. Menstrual cycle-based changes in lung function are recognized; however, the impact on qCT measures is currently unknown. We hypothesize that the menstrual cycle impacts qCT-derived measures of lung structure in healthy women and that the degree of measurement change may be mitigated in subjects on cyclic hormonal birth control. METHODS: Thirty-one non-smoking, healthy women with regular menstrual cycles (16 of which were on cyclic hormonal birth control) underwent pulmonary function testing and qCT imaging at both menses and early luteal phase time points. Data were evaluated to identify lung measurements which changed significantly across the two key time points and to compare degree of change across metrics for the sub-cohort with versus without birth control. RESULTS: The segmental airway measurements were larger and mean lung density was higher at menses compared to the early luteal phase. The sub-cohort with cyclic hormonal birth control did not have less evidence of measurement difference over the menstrual cycle compared to the sub-cohort without hormonal birth control. CONCLUSIONS: This study provides evidence that qCT-derived measures from the lung are impacted by the female menstrual cycle. This indicates studies seeking to use qCT as a more sensitive measure of cross-sectional differences or longitudinal changes in these derived lung measurements should consider acquiring data at a consistent time in the menstrual cycle for pre-menopausal women and warrants further exploration. KEY POINTS: • Lung measurements from chest computed tomography are used in multicenter studies exploring lung disease progression and treatment response. • The menstrual cycle impacts lung structure measurements. • Cyclic variability should be considered when evaluating longitudinal change with CT in menstruating women.

Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

OBJECTIVES: Post-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally. MATERIALS AND METHODS: A common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally. RESULTS: Using calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points. CONCLUSIONS: Calibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time. KEY POINTS: • Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.

High-energy external defibrillation and transcutaneous pacing during MRI: feasibility and safety.

BACKGROUND: Rapid application of external defibrillation, a crucial first-line therapy for ventricular fibrillation and cardiac arrest, is currently unavailable in the setting of magnetic resonance imaging (MRI), raising concerns about patient safety during MRI tests and MRI-guided procedures, particularly in patients with cardiovascular diseases. The objective of this study was to examine the feasibility and safety of defibrillation/pacing for the entire range of clinically useful shock energies inside the MRI bore and during scans, using defibrillation/pacing outside the magnet as a control. METHODS: Experiments were conducted using a commercial defibrillator (LIFEPAK 20, Physio-Control, Redmond, Washington, USA) with a custom high-voltage, twisted-pair cable with two mounted resonant floating radiofrequency traps to reduce emission from the defibrillator and the MRI scanner. A total of 18 high-energy (200-360 J) defibrillation experiments were conducted in six swine on a 1.5 T MRI scanner outside the magnet bore, inside the bore, and during scanning, using adult and pediatric defibrillation pads. Defibrillation was followed by cardiac pacing (with capture) in a subset of two animals. Monitored signals included: high-fidelity temperature (0.01 °C, 10 samples/sec) under the pads and 12-lead electrocardiogram (ECG) using an MRI-compatible ECG system. RESULTS: Defibrillation/pacing was successful in all experiments. Temperature was higher during defibrillation inside the bore and during scanning compared with outside the bore, but the differences were small (ΔT: 0.5 and 0.7 °C, p = 0.01 and 0.04, respectively). During scans, temperature after defibrillation tended to be higher for pediatric vs. adult pads (p = 0.08). MR-image quality (signal-to-noise ratio) decreased by ~ 10% when the defibrillator was turned on. CONCLUSIONS: Our study demonstrates the feasibility and safety of in-bore defibrillation for the full range of defibrillation energies used in clinical practice, as well as of transcutaneous cardiac pacing inside the MRI bore. Methods for Improving MR-image quality in the presence of a working defibrillator require further study.

Computed Tomography Measure of Lung at Risk and Lung Function Decline in Chronic Obstructive Pulmonary Disease.

RATIONALE: The rate of decline of lung function is greater than age-related change in a substantial proportion of patients with chronic obstructive pulmonary disease, even after smoking cessation. Regions of the lung adjacent to emphysematous areas are subject to abnormal stretch during respiration, and this biomechanical stress likely influences emphysema initiation and progression. OBJECTIVES: To assess whether quantifying this penumbra of lung at risk would predict FEV1 decline. METHODS: We analyzed paired inspiratory-expiratory computed tomography images at baseline of 680 subjects participating in a large multicenter study (COPDGene) over approximately 5 years. By matching inspiratory and expiratory images voxel by voxel using image registration, we calculated the Jacobian determinant, a measure of local lung expansion and contraction with respiration. We measured the distance between each normal voxel to the nearest emphysematous voxel, and quantified the percentage of normal voxels within each millimeter distance from emphysematous voxels as mechanically affected lung (MAL). Multivariable regression analyses were performed to assess the relationship between the Jacobian determinant, MAL, and FEV1 decline. MEASUREMENTS AND MAIN RESULTS: The mean (SD) rate of decline in FEV1 was 39.0 (58.6) ml/yr. There was a progressive decrease in the mean Jacobian determinant of both emphysematous and normal voxels with increasing disease stage (P < 0.001). On multivariable analyses, the mean Jacobian determinant of normal voxels within 2 mm of emphysematous voxels (MAL2) was significantly associated with FEV1 decline. In mild-moderate disease, for participants at or above the median MAL2 (threshold, 36.9%), the mean decline in FEV1 was 56.4 (68.0) ml/yr versus 43.2 (59.9) ml/yr for those below the median (P = 0.044). CONCLUSIONS: Areas of normal-appearing lung are mechanically influenced by emphysematous areas and this lung at risk is associated with lung function decline. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

Approaches to Evaluate Lung Inflammation in Translational Research.

Inflammation is a common feature in several types of lung disease and is a frequent end point to validate lung disease models, evaluate genetic or environmental impact on disease severity, or test the efficacy of new therapies. Questions relevant to a study should be defined during experimental design and techniques selected to specifically address these scientific queries. In this review, the authors focus primarily on the breadth of techniques to evaluate lung inflammation that have both clinical and preclinical applications. Stratification of approaches to assess lung inflammation can diminish weaknesses inherent to each technique, provide data validation, and increase the reproducibility of a study. Specialized techniques (eg, imaging, pathology) often require experienced personnel to collect, evaluate, and interpret the data; these experts should be active contributors to the research team through reporting of the data. Scoring of tissue lesions is a useful method to transform observational pathologic data into semiquantitative or quantitative data for statistical analysis and enhanced rigor. Each technique to evaluate lung inflammation has advantages and limitations; understanding these parameters can help identify approaches that best complement one another to increase the rigor and translational significance of data.

Protein Kinase Cζ Inhibitor Promotes Resolution of Bleomycin-Induced Acute Lung Injury.

Protein kinase Cζ (PKCζ) is highly expressed in the lung, where it plays several regulating roles in the pathogenesis of acute lung injury (ALI). Proliferation and differentiation of integrin ß4+ distal lung epithelial progenitor cells seem to play a key role in proper lung regeneration. We investigated the effects of a myristoylated PKCζ inhibitor (PKCζi) in a murine model of bleomycin-induced ALI. After intratracheal injury, we treated mice three times a week with PKCζi or its vehicle, DMSO. We found that mice injured with bleomycin and then treated with PKCζi for one week showed decreased activation of PKCζ, improved lung compliance, and decreased lung protein permeability compared to injured mice treated with DMSO. Mice treated continuously with PKCζi for 6 weeks showed reduced evidence of lung fibrosis by computed tomographic images, decreased lung collagen deposition, and decreased active transforming growth factor-ß in the bronchoalveolar lavage fluid. In addition, we found an increased number of lung ß4+ cells compared to DMSO at Week 6. Therefore, we grew isolated integrin ß4+ lung progenitor cells in the presence of PKCζi or DMSO and found that ß4+ cells treated with PKCζi proliferated more in vitro compared to DMSO. We conclude that the use of a PKCζi promotes resolution of lung fibrosis in a bleomycin ALI model and increases the number of ß4+ progenitor cells with regenerative potential in the lung.

Computed Tomography and Magnetic Resonance Imaging for Longitudinal Characterization of Lung Structure Changes in a Yucatan Miniature Pig Silicosis Model.

Medical imaging is a rapidly advancing field enabling the repeated, noninvasive assessment of physiological structure and function. These beneficial characteristics can supplement studies in swine by mirroring the clinical functions of detection, diagnosis, and monitoring in humans. In addition, swine may serve as a human surrogate, facilitating the development and comparison of new imaging protocols for translation to humans. This study presents methods for pulmonary imaging developed for monitoring pulmonary disease initiation and progression in a pig exposure model with computed tomography and magnetic resonance imaging. In particular, a focus was placed on systematic processes, including positioning, image acquisition, and structured reporting to monitor longitudinal change. The image-based monitoring procedure was applied to 6 Yucatan miniature pigs. A subset of animals (n= 3) were injected with crystalline silica into the apical bronchial tree to induce silicosis. The methodology provided longitudinal monitoring and evidence of progressive lung disease while simultaneously allowing for a cross-modality comparative study highlighting the practical application of medical image data collection in swine. The integration of multimodality imaging with structured reporting allows for cross comparison of modalities, refinement of CT and MRI protocols, and consistently monitors potential areas of interest for guided biopsy and/or necropsy.

Comparison of spirometric thresholds in diagnosing smoking-related airflow obstruction.

BACKGROUND: Diagnosis of chronic obstructive pulmonary disease is based on detection of airflow obstruction on spirometry. There is no consensus regarding using a fixed threshold to define airflow obstruction versus using the lower limit of normal (LLN) adjusted for age. We compared the accuracy and discrimination of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommended fixed ratio of forced expiratory volume in the first second/forced vital capacity<0.70 with LLN in diagnosing smoking-related airflow obstruction using CT-defined emphysema and gas trapping as the disease gold standard. METHODS: Data from a large multicentre study (COPDGene), which included current and former smokers (age range 45-80 years) with and without airflow obstruction, were analysed. Concordance between spirometric thresholds was measured. The accuracy of the thresholds in diagnosing emphysema and gas trapping was assessed using quantitative CT as gold standard. RESULTS: 7743 subjects were included. There was very good agreement between the two spirometric cutoffs (κ=0.85; 95% CI 0.83 to 0.86, p<0.001). 7.3% were discordant. Subjects with airflow obstruction by fixed ratio only had a greater degree of emphysema (4.1% versus 1.2%, p<0.001) and gas trapping (19.8% vs 7.5%, p<0.001) than those positive by LLN only, and also smoking controls without airflow obstruction (4.1% vs 1.9% and 19.8% vs 10.9%, respectively, p<0.001). On follow-up, the fixed ratio only group had more exacerbations than smoking controls. CONCLUSIONS: Compared with the fixed ratio, the use of LLN fails to identify a number of patients with significant pulmonary pathology and respiratory morbidity.

Mitochondrial Rac1 GTPase import and electron transfer from cytochrome c are required for pulmonary fibrosis.

The generation of reactive oxygen species, particularly H(2)O(2), from alveolar macrophages is causally related to the development of pulmonary fibrosis. Rac1, a small GTPase, is known to increase mitochondrial H(2)O(2) generation in macrophages; however, the mechanism by which this occurs is not known. This study shows that Rac1 is localized in the mitochondria of alveolar macrophages from asbestosis patients, and mitochondrial import requires the C-terminal cysteine of Rac1 (Cys-189), which is post-translationally modified by geranylgeranylation. Furthermore, H(2)O(2) generation mediated by mitochondrial Rac1 requires electron transfer from cytochrome c to a cysteine residue on Rac1 (Cys-178). Asbestos-exposed mice harboring a conditional deletion of Rac1 in macrophages demonstrated decreased oxidative stress and were significantly protected from developing pulmonary fibrosis. These observations demonstrate that mitochondrial import and direct electron transfer from cytochrome c to Rac1 modulates mitochondrial H(2)O(2) production in alveolar macrophages pulmonary fibrosis.

Radiomic biomarkers from chest computed tomography are assistive in immunotherapy response prediction for non-small cell lung cancer.

Background: Immunotherapies, such as programmed death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies have been shown to improve overall and progression-free survival (PFS) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). However, not all patients derive a meaningful clinical benefit. Additionally, patients receiving anti-PD-1/PD-L1 therapy can experience immune-related adverse events (irAEs). Clinically significant irAEs may require temporary pause or discontinuation of treatment. Having a tool to identify patients who may not benefit and/or are at risk for developing severe irAEs from immunotherapy will aid in an informed decision-making process for the patients and their physicians. Methods: Computed tomography (CT) scans and clinical data were retrospectively collected for this study to develop three prediction models using (I) radiomic features, (II) clinical features, and (III) radiomic and clinical features combined. Each subject had 6 clinical features and 849 radiomic features extracted. Selected features were run through an artificial neural network (NN) trained on 70% of the cohort, maintaining the case and control ratio. The NN was assessed by calculating the area-under-the-receiver-operating-characteristic curve (AUC-ROC), area-under-the-precision-recall curve (AUC-PR), sensitivity, and specificity. Results: A cohort of 132 subjects, of which 43 (33%) had a PFS ≤90 days and 89 (67%) of which had a PFS >90 days was used to develop the prediction models. The radiomic model was able to predict progression-free survival with a training AUC-ROC of 87% and testing AUC-ROC, sensitivity, and specificity of 83%, 75%, and 81%, respectively. In this cohort, the clinical and radiomic combined features did add a slight increase in the specificity (85%) but with a decrease in sensitivity (75%) and AUC-ROC (81%). Conclusions: Whole lung segmentation and feature extraction can identify those that would see a benefit from anti-PD-1/PD-L1 therapy.

Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model.

Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (- 9.06 cm3), gray matter (- 4.37% 95 CI - 7.41; - 1.83), caudate (- 0.16%, 95 CI - 0.24; - 0.08) and putamen (- 0.11% 95 CI - 0.23; - 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ 3.42%, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (- 8.27%, 95 CI - 10.1; - 5.56) and cerebrospinal fluid (+ 6.88%, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation.

CDK4/6-MEK Inhibition in MPNSTs Causes Plasma Cell Infiltration, Sensitization to PD-L1 Blockade, and Tumor Regression.

PURPOSE: Malignant peripheral nerve sheath tumors (MPNST) are lethal, Ras-driven sarcomas that lack effective therapies. We investigated effects of targeting cyclin-dependent kinases 4 and 6 (CDK4/6), MEK, and/or programmed death-ligand 1 (PD-L1) in preclinical MPNST models. EXPERIMENTAL DESIGN: Patient-matched MPNSTs and precursor lesions were examined by FISH, RNA sequencing, IHC, and Connectivity-Map analyses. Antitumor activity of CDK4/6 and MEK inhibitors was measured in MPNST cell lines, patient-derived xenografts (PDX), and de novo mouse MPNSTs, with the latter used to determine anti-PD-L1 response. RESULTS: Patient tumor analyses identified CDK4/6 and MEK as actionable targets for MPNST therapy. Low-dose combinations of CDK4/6 and MEK inhibitors synergistically reactivated the retinoblastoma (RB1) tumor suppressor, induced cell death, and decreased clonogenic survival of MPNST cells. In immune-deficient mice, dual CDK4/6-MEK inhibition slowed tumor growth in 4 of 5 MPNST PDXs. In immunocompetent mice, combination therapy of de novo MPNSTs caused tumor regression, delayed resistant tumor outgrowth, and improved survival relative to monotherapies. Drug-sensitive tumors that regressed contained plasma cells and increased cytotoxic T cells, whereas drug-resistant tumors adopted an immunosuppressive microenvironment with elevated MHC II-low macrophages and increased tumor cell PD-L1 expression. Excitingly, CDK4/6-MEK inhibition sensitized MPNSTs to anti-PD-L1 immune checkpoint blockade (ICB) with some mice showing complete tumor regression. CONCLUSIONS: CDK4/6-MEK inhibition induces a novel plasma cell-associated immune response and extended antitumor activity in MPNSTs, which dramatically enhances anti-PD-L1 therapy. These preclinical findings provide strong rationale for clinical translation of CDK4/6-MEK-ICB targeted therapies in MPNST as they may yield sustained antitumor responses and improved patient outcomes.

Inter- and intra-scan variability for lung imaging quantifications via CT.

CT imaging provides physicians valuable insights when diagnosing disease in a clinical setting. In order to provide an accurate diagnosis, is it important to have a high accuracy with controlled variability across CT scans from different scanners and imaging parameters. The purpose of this study was to analyze variability of lung imaging biomarkers across various scanners and parameters using a customized version of a commercially available anthropomorphic chest Phantom (Kyoto Kagaku) with several experimental sample inserts. The phantom was across 10 different CT scanners with a total of 209 imaging conditions. An algorithm was developed to compute different imaging biomarkers. Variability across images from the same scanner and from different scanners was analyzed by computing coefficients of variation (CV) and standard deviations of HU values. LAA -950 and LAA -856 biomarkers had the highest levels of variability, while the majority of other biomarkers had variability less than 10 HU or 10% CV in both inter and intra-scan measurements. There was no clear trend present between the biomarker measurements and CTDIvol. The results of this study demonstrates the existing variability in CT quantifications for lung imaging, which prompt further studies on how to reduce such variation.

Scanner-specific validation of a CT simulator using a COPD-emulated anthropomorphic phantom.

Traditional methods of quantitative analysis of CT images typically involve working with patient data, which is often expensive and limited in terms of ground truth. To counter these restrictions, quantitative assessments can instead be made through Virtual Imaging Trials (VITs) which simulate the CT imaging process. This study sought to validate DukeSim (a scanner-specific CT simulator) utilizing clinically relevant biomarkers for a customized anthropomorphic chest phantom. The physical phantom was imaged utilizing two commercial CT scanners (Siemens Somatom Force and Definition Flash) with varying imaging parameters. A computational version of the phantom was simulated utilizing DukeSim for each corresponding real acquisition. Biomarkers were computed and compared between the real and virtually acquired CT images to assess the validity of DukeSim. The simulated images closely matched the real images both qualitatively and quantitatively, with the average biomarker percent difference of 3.84% (range 0.19% to 18.27%). Results showed that DukeSim is reasonably well validated across various patient imaging conditions and scanners, which indicates the utility of DukeSim for further VIT studies where real patient data may not be feasible.

Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a known comorbidity for lung cancer independent of smoking history. Quantitative computed tomography (qCT) imaging features related to COPD have shown promise in the assessment of lung cancer risk. We hypothesize that qCT features from the lung, lobe, and airway tree related to the location of the pulmonary nodule can be used to provide informative malignancy risk assessment. METHODS: A total of 183 qCT features were extracted from 278 individuals with a solitary pulmonary nodule of known diagnosis (71 malignant, 207 benign). These included histogram and airway characteristics of the lungs, lobe, and segmental paths. Performances of the least absolute shrinkage and selection operator (LASSO) regression analysis and an ensemble of neural networks (ENN) were compared for feature set selection and classification on a testing cohort of 49 additional individuals (15 malignant, 34 benign). RESULTS: The LASSO and ENN methods produced different feature sets for classification with LASSO selecting fewer qCT features (7) than the ENN (17). The LASSO model with the highest performing training area under the curve (AUC) (0.80) incorporated automatically extracted features and reader-measured nodule diameter with a testing AUC of 0.62. The ENN model with the highest performing AUC (0.77) also incorporated qCT and reader diameter but maintained higher testing performance AUC (0.79). CONCLUSIONS: Automatically extracted qCT imaging features of the lung can be informative of the differentiation between individuals with malignant pulmonary nodules and those with benign pulmonary nodules, without requiring nodule segmentation and analysis.

Task-based validation and application of a scanner-specific CT simulator using an anthropomorphic phantom.

BACKGROUND: Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner-specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. PURPOSE: To validate a scanner-specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. METHODS: An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner-specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20-300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground-truth values modeled in the computational chest phantom. RESULTS: The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%-18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84-94 HU, lung volume of 797-3785 cm3 , and lung mass of -800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6-84 HU, lung volume of 66-3785 cm3 , and lung mass of 170-1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. CONCLUSION: We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

The improved success rate and reduced complications of a novel localization device vs. hookwire for thoracoscopic resection of small pulmonary nodules: a single-center, open-label, randomized clinical trial.

Background: In our previous study, we developed a 4-hook claw-suture localization device for pulmonary nodule resection, which acheived satifisfactory results. Following this, we conducted this single-center, open-label, randomized clinical trial to compare the success rate and complication rate of this novel localization device and currently widely-used hookwire. Methods: Patients with small pulmonary nodules (0.4-1 cm) who received preoperative localization and thoracoscopic resection at Shanghai Chest Hospital were randomly assigned (1:2 ratio, via computer-generated randomized numbers) to undergo localization using either a novel claw-suture system (claw group) or classical (hookwire group) localization device. The primary endpoint of this study was localization success rate, and the secondary endpoints included complications, localization-related time, and pain. Results: A total of 411 patients were randomly assigned to the claw group (n=136) or the hookwire group (n=275) before thoracoscopic resection of small pulmonary nodules and analyzed. Compared with the hookwire group, the claw group had a significantly higher success rate (133/136, 97.8% vs. 254/275, 92.4%, P=0.027), less asymptomatic hemorrhage (16.9% vs. 37.5%, P=0.003) and pleural reaction (0% vs. 5.1%, P=0.017), as well as better pain alleviation 10 min after localization (measured using the difference between two visual analog scale scores, 0.84±0.98 vs. 0.35±0.79, P<0.001). In contrast, the hookwire group was associated with a shorter localization procedure duration than the claw group (7.2±2.9 vs. 14.4±6.6 min, P<0.001). In the multiple localization subgroup, the claw group compared to the hookwire group also achieved higher success (32/33, 97.0% vs. 70/86, 81.4%) and less pleural reaction (0% vs. 16.3%). Conclusions: The new claw-suture localization device is superior to traditional hookwire, with a higher success rate, fewer complications, and better patient tolerance for preoperative localization of small pulmonary nodules. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900027346.

A Novel Porcine Model of CLN2 Batten Disease that Recapitulates Patient Phenotypes.

CLN2 Batten disease is a lysosomal disorder in which pathogenic variants in CLN2 lead to reduced activity in the enzyme tripeptidyl peptidase 1. The disease typically manifests around 2 to 4 years of age with developmental delay, ataxia, seizures, inability to speak and walk, and fatality between 6 and 12 years of age. Multiple Cln2 mouse models exist to better understand the etiology of the disease; however, these models are unable to adequately recapitulate the disease due to differences in anatomy and physiology, limiting their utility for therapeutic testing. Here, we describe a new CLN2R208X/R208X porcine model of CLN2 disease. We present comprehensive characterization showing behavioral, pathological, and visual phenotypes that recapitulate those seen in CLN2 patients. CLN2R208X/R208X miniswine present with gait abnormalities at 6 months of age, ERG waveform declines at 6-9 months, vision loss at 11 months, cognitive declines at 12 months, seizures by 15 months, and early death at 18 months due to failure to thrive. CLN2R208X/R208X miniswine also showed classic storage material accumulation and glial activation in the brain at 6 months, and cortical atrophy at 12 months. Thus, the CLN2R208X/R208X miniswine model is a valuable resource for biomarker discovery and therapeutic development in CLN2 disease.

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning.

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.

Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging.

This review compares the emerging technologies and approaches in the application of magnetic resonance (MR) and computed tomography (CT) imaging for the assessment of pulmonary nodules and staging of malignant findings. Included in this review is a brief definition of pulmonary nodules and an introduction to the challenges faced. We have highlighted the current status of both MR and CT for the early detection of lung nodules. Developments are detailed in this review for the management of pulmonary nodules using advanced imaging, including: dynamic imaging studies, dual energy CT, computer aided detection and diagnosis, and imaging assisted nodule biopsy approaches which have improved lung nodule detection and diagnosis rates. Recent advancements linking in vivo imaging to corresponding histological pathology are also highlighted. In vivo imaging plays a pivotal role in the clinical staging of pulmonary nodules through TNM assessment. While CT and positron emission tomography (PET)/CT are currently the most commonly clinically employed modalities for pulmonary nodule staging, studies are presented that highlight the augmentative potential of MR.