WITHDRAWN: Oncogenic KRAS G12D extrinsically induces an immunosuppressive microenvironment in lung adenocarcinoma.

This manuscript has been withdrawn by the authors due to a dispute over co-first authorship that is currently being arbitrated by the medical school at our institution. Therefore, the authors do not wish this work to be cited as reference for the project. Upon completion of the arbitration process, we will take steps to revert the current withdrawn status. If you have any questions, please contact the corresponding author.

Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis-Associated Lung Cancer in Mice.

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive, often fatal loss of lung function due to overactive collagen production and tissue scarring. Patients with IPF have a sevenfold-increased risk of developing lung cancer. The COVID-19 pandemic has increased the number of patients with lung diseases, and infection can worsen prognoses for those with chronic lung diseases and disease-associated cancer. Understanding the molecular pathogenesis of IPF-associated lung cancer is imperative for identifying diagnostic biomarkers and targeted therapies that will facilitate prevention of IPF and progression to lung cancer. To understand how IPF-associated fibroblast activation, matrix remodeling, epithelial-to-mesenchymal transition (EMT), and immune modulation influences lung cancer predisposition, we developed a mouse model to recapitulate the molecular pathogenesis of pulmonary fibrosis-associated lung cancer using the bleomycin and Lewis lung carcinoma models. We demonstrate that development of pulmonary fibrosis-associated lung cancer is likely linked to increased abundance of tumor-associated macrophages and a unique gene signature that supports an immune-suppressive microenvironment through secreted factors. Not surprisingly, preexisting fibrosis provides a pre-metastatic niche and results in augmented tumor growth, and tumors associated with bleomycin-induced fibrosis are characterized by a dramatic loss of cytokeratin expression, indicative of EMT. IMPLICATIONS: This characterization of tumors associated with lung diseases provides new therapeutic targets that may aid in the development of treatment paradigms for lung cancer patients with preexisting pulmonary diseases.

Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fß-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS.

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1-2) COPD. We trained multiple models to predict rapid FEV1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0-2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV1% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV1% predicted (FEV1.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRMlower lobes fSAD. In the validation cohort, GOLD 0 and GOLD 1-2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV1 decline than those with lower scores. Conclusion: Predicting FEV1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts.

The adapter protein FADD provides an alternate pathway for entry into the cell cycle by regulating APC/C-Cdh1 E3 ubiquitin ligase activity.

The E3 ubiquitin ligase APC/C-Cdh1 maintains the G0/G1 state, and its inactivation is required for cell cycle entry. We reveal a novel role for Fas-associated protein with death domain (FADD) in the cell cycle through its function as an inhibitor of APC/C-Cdh1. Using real-time, single-cell imaging of live cells combined with biochemical analysis, we demonstrate that APC/C-Cdh1 hyperactivity in FADD-deficient cells leads to a G1 arrest despite persistent mitogenic signaling through oncogenic EGFR/KRAS. We further show that FADDWT interacts with Cdh1, while a mutant lacking a consensus KEN-box motif (FADDKEN) fails to interact with Cdh1 and results in a G1 arrest due to its inability to inhibit APC/C-Cdh1. Additionally, enhanced expression of FADDWT but not FADDKEN, in cells arrested in G1 upon CDK4/6 inhibition, leads to APC/C-Cdh1 inactivation and entry into the cell cycle in the absence of retinoblastoma protein phosphorylation. FADD's function in the cell cycle requires its phosphorylation by CK1α at Ser-194 which promotes its nuclear translocation. Overall, FADD provides a CDK4/6-Rb-E2F-independent "bypass" mechanism for cell cycle entry and thus a therapeutic opportunity for CDK4/6 inhibitor resistance.

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

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

Feasibility of function-guided lung treatment planning with parametric response mapping.

PURPOSE: Recent advancements in functional lung imaging have been developed to improve clinicians' knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)-based functional lung imaging method that utilizes a voxel-wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. METHODS AND MATERIALS: A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric-modulated arc therapy and PRM-guided treatment plans were designed for each patient. RESULTS: PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM-guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity. CONCLUSIONS: PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM-guided treatment planning.

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

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

An assessment of the correlation between robust CT-derived ventilation and pulmonary function test in a cohort with no respiratory symptoms.

OBJECTIVE: To evaluate CT-ventilation imaging (CTVI) within a well-characterized, healthy cohort with no respiratory symptoms and examine the correlation between CTVI and concurrent pulmonary function test (PFT). METHODS: CT scans and PFTs from 77 Caucasian participants in the NORM dataset (clinicaltrials.gov NCT00848406) were analyzed. CTVI was generated using the robust Integrated Jacobian Formulation (IJF) method. IJF estimated total lung capacity (TLC) was computed from CTVI. Bias-adjusted Pearson's correlation between PFT and IJF-based TLC was computed. RESULTS: IJF- and PFT-measured TLC showed a good correlation for both males and females [males: 0.657, 95% CI (0.438-0.797); females: 0.667, 95% CI (0.416-0.817)]. When adjusting for age, height, smoking, and abnormal CT scan, correlation moderated [males: 0.432, 95% CI (0.129-0.655); females: 0.540, 95% CI (0.207-0.753)]. Visual inspection of CTVI revealed participants who had functional defects, despite the fact that all participant had normal high-resolution CT scan. CONCLUSION: In this study, we demonstrate that IJF computed CTVI has good correlation with concurrent PFT in a well-validated patient cohort with no respiratory symptoms. ADVANCES IN KNOWLEDGE: IJF-computed CTVI's overall numerical robustness and consistency with PFT support its potential as a method for providing spatiotemporal assessment of high and low function areas on volumetric non-contrast CT scan.

Quantitative Emphysema on Low-Dose CT Imaging of the Chest and Risk of Lung Cancer and Airflow Obstruction: An Analysis of the National Lung Screening Trial.

BACKGROUND: Lung cancer risk prediction models do not routinely incorporate imaging metrics available on low-dose CT (LDCT) imaging of the chest ordered for lung cancer screening. RESEARCH QUESTION: What is the association between quantitative emphysema measured on LDCT imaging and lung cancer incidence and mortality, all-cause mortality, and airflow obstruction in individuals who currently or formerly smoked and are undergoing lung cancer screening? STUDY DESIGN AND METHODS: In 7,262 participants in the CT arm of the National Lung Screening Trial, percent low attenuation area (%LAA) was defined as the percentage of lung volume with voxels less than -950 Hounsfield units on the baseline examination. Multivariable Cox proportional hazards models, adjusting for competing risks where appropriate, were built to test for association between %LAA and lung cancer incidence, lung cancer mortality, and all-cause mortality with censoring at 6 years. In addition, multivariable logistic regression models were built to test the cross-sectional association between %LAA and airflow obstruction on spirometry, which was available in 2,700 participants. RESULTS: The median %LAA was 0.8% (interquartile range, 0.2%-2.7%). Every 1% increase in %LAA was independently associated with higher hazards of lung cancer incidence (hazard ratio [HR], 1.02; 95% CI, 1.01-1.03; P = .004), lung cancer mortality (HR, 1.02; 95% CI, 1.00-1.05; P = .045), and all-cause mortality (HR, 1.01; 95% CI, 1.00-1.03; P = .042). Among participants with spirometry, 892 had airflow obstruction. The likelihood of airflow obstruction increased with every 1% increase in %LAA (odds ratio, 1.07; 95% CI, 1.06-1.09; P < .001). A %LAA cutoff of 1% had the best discriminative accuracy for airflow obstruction in participants aged > 65 years. INTERPRETATION: Quantitative emphysema measured on LDCT imaging of the chest can be leveraged to improve lung cancer risk prediction and help diagnose COPD in individuals who currently or formerly smoked and are undergoing lung cancer screening.

Progression of Emphysema and Small Airways Disease in Cigarette Smokers.

BACKGROUND: Little is known about factors associated with emphysema progression in cigarette smokers. We evaluated factors associated with change in emphysema and forced expiratory volume in 1 second (FEV1) in participants with and without chronic obstructive pulmonary disease (COPD). METHODS: This retrospective study included individuals participating in the COPD Genetic Epidemiology study who completed the 5-year follow-up, including inspiratory and expiratory computed tomography (CT) and spirometry. All paired CT scans were analyzed using micro-mapping, which classifies individual voxels as emphysema or functional small airway disease (fSAD). Presence and progression of emphysema and FEV1 were determined based on comparison to nonsmoker values. Logistic regression analyses were used to identify clinical parameters associated with disease progression. RESULTS: A total of 3088 participants were included with a mean ± SD age of 60.7±8.9 years, including 72 nonsmokers. In all Global initiative for chronic Obstructive Lung Disease (GOLD) stages, the presence of emphysema at baseline was associated with emphysema progression (odds ratio [OR]: GOLD 0: 4.32; preserved ratio-impaired spirometry [PRISm]; 5.73; GOLD 1: 5.16; GOLD 2: 5.69; GOLD 3/4: 5.55; all p ≤0.01). If there was no emphysema at baseline, the amount of fSAD at baseline was associated with emphysema progression (OR for 1% increase: GOLD 0: 1.06; PRISm: 1.20; GOLD 1: 1.7; GOLD 3/4: 1.08; all p ≤ 0.03).In 1735 participants without spirometric COPD, progression in emphysema occurred in 105 (6.1%) participants and only 21 (1.2%) had progression in both emphysema and FEV1. CONCLUSIONS: The presence of emphysema is an important predictor of emphysema progression. In patients without emphysema, fSAD is associated with the development of emphysema. In participants without spirometric COPD, emphysema progression occurred independently of FEV1 decline.

Lung T1 mapping magnetic resonance imaging in the assessment of pulmonary disease in children with cystic fibrosis: a pilot study.

BACKGROUND: Assessment tools for early cystic fibrosis (CF) lung disease are limited. Detecting early pulmonary disease is crucial to increasing life expectancy by starting interventions to slow the progression of the pulmonary disease with the many treatment options available. OBJECTIVE: To compare the utility of lung T1-mapping MRI with ultrashort echo time (UTE) MRI in children with cystic fibrosis in detecting early stage lung disease and monitoring pulmonary exacerbations. MATERIALS AND METHODS: We performed a prospective study in 16 children between September 2017 and January 2018. In Phase 1, we compared five CF patients with normal spirometry (mean 11.2 years) to five age- and gender-matched healthy volunteers. In Phase 2, we longitudinally evaluated six CF patients (median 11 years) in acute pulmonary exacerbation. All children had non-contrast lung T1-mapping and UTE MRI and spirometry testing. We compared the mean normalized T1 value and percentage lung volume without T1 value in CF patients and healthy subjects in Phase 1 and during treatment in Phase 2. We also performed cystic fibrosis MRI scoring. We evaluated differences in continuous variables using standard statistical tests. RESULTS: In Phase 1, mean normalized T1 values of the lung were significantly lower in CF patients in comparison to healthy controls (P=0.02) except in the right lower lobe (P=0.29). The percentage lung volume without T1 value was also significantly higher in CF patients (P=0.006). UTE MRI showed no significant differences between CF patients and healthy volunteers (P=0.11). In Phase 2, excluding one outlier case who developed systemic disease in the course of treatment, the whole-lung T1 value increased (P=0.001) and perfusion scoring improved (P=0.02) following therapy. We observed no other significant changes in the MRI scoring. CONCLUSION: Lung T1-mapping MRI can detect early regional pulmonary CF disease in children and might be helpful in the assessment of acute pulmonary exacerbations.

Small airways pathology in idiopathic pulmonary fibrosis: a retrospective cohort study.

BACKGROUND: The observation that patients with idiopathic pulmonary fibrosis (IPF) can have higher than normal expiratory flow rates at low lung volumes led to the conclusion that the airways are spared in IPF. This study aimed to re-examine the hypothesis that airways are spared in IPF using a multiresolution imaging protocol that combines multidetector CT (MDCT), with micro-CT and histology. METHODS: This was a retrospective cohort study comparing explanted lungs from patients with severe IPF treated by lung transplantation with a cohort of unused donor (control) lungs. The donor control lungs had no known lung disease, comorbidities, or structural lung injury, and were deemed appropriate for transplantation on review of the clinical files. The diagnosis of IPF in the lungs from patients was established by a multidisciplinary consensus committee according to existing guidelines, and was confirmed by video-assisted thoracic surgical biopsy or by pathological examination of the contralateral lung. The control and IPF groups were matched for age, sex, height, and bodyweight. Samples of lung tissue were compared using the multiresolution imaging approach: a cascade of clinical MDCT, micro-CT, and histological imaging. We did two experiments: in experiment 1, all the lungs were randomly sampled; in experiment 2, samples were selected from regions of minimal and established fibrosis. The patients and donors were recruited from the Katholieke Universiteit Leuven (Leuven, Belgium) and the University of Pennsylvania Hospital (Philadelphia, PA, USA). The study took place at the Katholieke Universiteit Leuven, and the University of British Columbia (Vancouver, BC, Canada). FINDINGS: Between Oct 5, 2009, and July 22, 2016, explanted lungs from patients with severe IPF (n=11), were compared with a cohort of unused donor (control) lungs (n=10), providing 240 samples of lung tissue for comparison using the multiresolution imaging approach. The MDCT specimen scans show that the number of visible airways located between the ninth generation (control 69 [SD 22] versus patients with IPF 105 [33], p=0·0023) and 14th generation (control 9 [6] versus patients with IPF 49 [28], p<0·0001) of airway branching are increased in patients with IPF, which we show by micro-CT is due to thickening of their walls and distortion of their lumens. The micro-CT analysis showed that compared with healthy (control) lung anatomy (mean 5·6 terminal bronchioles per mL [SD 1·6]), minimal fibrosis in IPF tissue was associated with a 57% loss of the terminal bronchioles (mean 2·4 terminal bronchioles per mL [SD 1·0]; p<0·0001), the appearance of fibroblastic foci, and infiltration of the tissue by inflammatory immune cells capable of forming lymphoid follicles. Established fibrosis in IPF tissue had a similar reduction (66%) in the number of terminal bronchioles (mean 1·9 terminal bronchioles per mL [SD 1·4]; p<0·0001) and was dominated by increased airspace size, Ashcroft fibrosis score, and volume fractions of tissue and collagen. INTERPRETATION: Small airways disease is a feature of IPF, with significant loss of terminal bronchioles occuring within regions of minimal fibrosis. On the basis of these findings, we postulate that the small airways could become a potential therapeutic target in IPF. FUNDING: Katholieke Universiteit Leuven, US National Institutes of Health, BC Lung Association, and Genentech.

Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation.

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.

Comparison of Voxel-Wise and Histogram Analyses of Glioma ADC Maps for Prediction of Early Therapeutic Change.

Noninvasive imaging methods are sought to objectively predict early response to therapy for high-grade glioma tumors. Quantitative metrics derived from diffusion-weighted imaging, such as apparent diffusion coefficient (ADC), have previously shown promise when used in combination with voxel-based analysis reflecting regional changes. The functional diffusion mapping (fDM) metric is hypothesized to be associated with volume of tumor exhibiting an increasing ADC owing to effective therapeutic action. In this work, the reference fDM-predicted survival (from previous study) for 3 weeks from treatment initiation (midtreatment) is compared to multiple histogram-based metrics using Kaplan-Meier estimator for 80 glioma patients stratified to responders and nonresponders based on the population median value for the given metric. The ADC histogram metric reflecting reduction in midtreatment volume of solid tumor (ADC < 1.25 × 10-3 mm2/s) by >8% population-median with respect to pretreatment is found to have the same predictive power as the reference fDM of increasing midtreatment ADC volume above 4%. This study establishes the level of correlation between fDM increase and low-ADC tumor volume shrinkage for prediction of early response to radiation therapy in patients with glioma malignancies.

Voxel-Wise Longitudinal Parametric Response Mapping Analysis of Chest Computed Tomography in Smokers.

RATIONALE AND OBJECTIVES: Chronic obstructive pulmonary disease is a heterogeneous disease characterized by small airway abnormality and emphysema. We hypothesized that a voxel-wise computed tomography analytic approach would identify patterns of disease progression in smokers. MATERIALS AND METHODS: We analyzed 725 smokers in spirometric GOLD stages 0-4 with two chest CTs 5 years apart. Baseline inspiration, follow-up inspiration and follow-up expiration images were spatially registered to baseline expiration so that each voxel had correspondences across all time points and respiratory phases. Voxel-wise Parametric Response Mapping (PRM) was then generated for the baseline and follow-up scans. PRM classifies lung as normal, functional small airway disease (PRMfSAD), and emphysema (PRMEMPH). RESULTS: Subjects with low baseline PRMfSAD and PRMEMPH predominantly had an increase in PRMfSAD on follow-up; those with higher baseline PRMfSAD and PRMEMPH mostly had increases in PRMEMPH. For GOLD 0 participants (n = 419), mean 5-year increases in PRMfSAD and PRMEMPH were 0.3% for both; for GOLD 1-4 participants (n = 306), they were 0.6% and 1.6%, respectively. Eighty GOLD 0 subjects (19.1%) had overall radiologic progression (30.0% to PRMfSAD, 52.5% to PRMEMPH, and 17.5% to both); 153 GOLD 1-4 subjects (50.0%) experienced progression (17.6% to PRMfSAD, 48.4% to PRMEMPH, and 34.0% to both). In a multivariable model, both baseline PRMfSAD and PRMEMPH were associated with development of PRMEMPH on follow-up, although this relationship was diminished at higher levels of baseline PRMEMPH. CONCLUSION: A voxel-wise longitudinal PRM analytic approach can identify patterns of disease progression in smokers with and without chronic obstructive pulmonary disease.

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

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

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

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