Noninvasive Risk Stratification of Lung Adenocarcinoma using Quantitative Computed Tomography

IntroductionLung cancer remains the leading cause of cancer-related deaths in the United States and worldwide. Adenocarcinoma is the most common type of lung cancer and encompasses lesions with widely variable clinical outcomes. In the absence of noninvasive risk stratification, individualized patient management remains challenging. Consequently a subgroup of pulmonary nodules of the lung adenocarcinoma spectrum is likely treated more aggressively than necessary.MethodsConsecutive patients with surgically resected pulmonary nodules of the lung adenocarcinoma spectrum (lesion size ⩽3 cm, 2006–2009) and available presurgical high-resolution computed tomography (HRCT) imaging were identified at Mayo Clinic Rochester. All cases were classified using an unbiased Computer-Aided Nodule Assessment and Risk Yield (CANARY) approach based on the quantification of presurgical HRCT characteristics. CANARY-based classification was independently correlated to postsurgical progression-free survival.ResultsCANARY analysis of 264 consecutive patients identified three distinct subgroups. Independent comparisons of 5-year disease-free survival (DFS) between these subgroups demonstrated statistically significant differences in 5-year DFS, 100%, 72.7%, and 51.4%, respectively (p = 0.0005).ConclusionsNoninvasive CANARY-based risk stratification identifies subgroups of patients with pulmonary nodules of the adenocarcinoma spectrum characterized by distinct clinical outcomes. This technique may ultimately improve the current expert opinion-based approach to the management of these lesions by facilitating individualized patient management

Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

Abnormal airway dilatation, termed traction bronchiectasis, is a typical feature of idiopathic pulmonary fibrosis (IPF). Volumetric computed tomography (CT) imaging captures the loss of normal airway tapering in IPF. We postulated that automated quantification of airway abnormalities could provide estimates of IPF disease extent and severity. We propose AirQuant, an automated computational pipeline that systematically parcellates the airway tree into its lobes and generational branches from a deep learning based airway segmentation, deriving airway structural measures from chest CT. Importantly, AirQuant prevents the occurrence of spurious airway branches by thick wave propagation and removes loops in the airway-tree by graph search, overcoming limitations of existing airway skeletonisation algorithms. Tapering between airway segments (intertapering) and airway tortuosity computed by AirQuant were compared between 14 healthy participants and 14 IPF patients. Airway intertapering was significantly reduced in IPF patients, and airway tortuosity was significantly increased when compared to healthy controls. Differences were most marked in the lower lobes, conforming to the typical distribution of IPF-related damage. AirQuant is an open-source pipeline that avoids limitations of existing airway quantification algorithms and has clinical interpretability. Automated airway measurements may have potential as novel imaging biomarkers of IPF severity and disease extent

Predicting outcomes in rheumatoid arthritis related interstitial lung disease

Aims: To compare radiology-based prediction models in rheumatoid arthritis-related interstitial lung disease (RA-ILD) to identify patients with a progressive fibrosis phenotype.Methods: RAILD patients had CTs scored visually and by CALIPER and forced vital capacity (FVC) measurements. Outcomes were evaluated using three techniques: 1.Scleroderma system evaluating visual ILD extent and FVC values; 2.Fleischer Society IPF diagnostic guidelines applied to RAILD; 3.CALIPER scores of vessel-related structures (VRS). Outcomes were compared to IPF patients.Results: On univariable Cox analysis, all three staging systems strongly predicted outcome: Scleroderma System:HR=3.78, p=9×10-5; Fleischner System:HR=1.98, p=2×10-3; 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, gender and smoking status, when analysed alongside the Progressive Fibrotic System, the VRS threshold of 4.4% independently predicted outcome (Model C-statistic=0.77).Conclusions: 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

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.]

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Quantitative Stratification of Diffuse Parenchymal Lung Diseases

<div><p>Diffuse parenchymal lung diseases (DPLDs) are characterized by widespread pathological changes within the pulmonary tissue that impair the elasticity and gas exchange properties of the lungs. Clinical-radiological diagnosis of these diseases remains challenging and their clinical course is characterized by variable disease progression. These challenges have hindered the introduction of robust objective biomarkers for patient-specific prediction based on specific phenotypes in clinical practice for patients with DPLD. Therefore, strategies facilitating individualized clinical management, staging and identification of specific phenotypes linked to clinical disease outcomes or therapeutic responses are urgently needed. A classification schema consistently reflecting the radiological, clinical (lung function and clinical outcomes) and pathological features of a disease represents a critical need in modern pulmonary medicine. Herein, we report a quantitative stratification paradigm to identify subsets of DPLD patients with characteristic radiologic patterns in an unsupervised manner and demonstrate significant correlation of these self-organized disease groups with clinically accepted surrogate endpoints. The proposed consistent and reproducible technique could potentially transform diagnostic staging, clinical management and prognostication of DPLD patients as well as facilitate patient selection for clinical trials beyond the ability of current radiological tools. In addition, the sequential quantitative stratification of the type and extent of parenchymal process may allow standardized and objective monitoring of disease, early assessment of treatment response and mortality prediction for DPLD patients.</p></div

The cluster-specific mean physiologic measures (FEV1/FVC ratio, percentage predicted values of DLCO, TLC, FEV1, and FVC) for the ten stratified groups.

<p>The error bars indicate the standard errors of mean. The numbers within parenthesis in the horizontal axis represent the number of cluster-specific cases used in the mean, standard error computation. The post-ANOVA pairwise t-test for the indicated five variables is shown as the staircase diagram with green fill for significant differences after Bonferroni correction. At least one variable is statistically significant across the clusters except for between groups (1, 2) and (5, 8).</p

1322 LTRC patients represented as glyphs.

<p>The classified parenchymal patterns are represented in the indicated colors. The radius of the glyphs is proportional to the lung volumes; the COPD cases with extensive low attenuation areas likely due to emphysema are visually larger than more normal or fibrotic cases with considerable regions of reticular or honeycombing.</p