Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics. © 2019 Wolters Kluwer Health, Inc. All rights reserved

Quantitative CT-based structural alterations of segmental airways in cement dust-exposed subjects

Background Dust exposure has been reported as a risk factor of pulmonary disease, leading to alterations of segmental airways and parenchymal lungs. This study aims to investigate alterations of quantitative computed tomography (QCT)-based airway structural and functional metrics due to cement-dust exposure. Methods To reduce confounding factors, subjects with normal spirometry without fibrosis, asthma and pneumonia histories were only selected, and a propensity score matching was applied to match age, sex, height, smoking status, and pack-years. Thus, from a larger data set (N = 609), only 41 cement dust-exposed subjects were compared with 164 non-cement dust-exposed subjects. QCT imaging metrics of airway hydraulic diameter (Dh), wall thickness (WT), and bifurcation angle (θ) were extracted at total lung capacity (TLC) and functional residual capacity (FRC), along with their deformation ratios between TLC and FRC. Results In TLC scan, dust-exposed subjects showed a decrease of Dh (airway narrowing) especially at lower-lobes (p < 0.05), an increase of WT (wall thickening) at all segmental airways (p < 0.05), and an alteration of θ at most of the central airways (p < 0.001) compared with non-dust-exposed subjects. Furthermore, dust-exposed subjects had smaller deformation ratios of WT at the segmental airways (p < 0.05) and θ at the right main bronchi and left main bronchi (p < 0.01), indicating airway stiffness. Conclusions Dust-exposed subjects with normal spirometry demonstrated airway narrowing at lower-lobes, wall thickening at all segmental airways, a different bifurcation angle at central airways, and a loss of airway wall elasticity at lower-lobes. The airway structural alterations may indicate different airway pathophysiology due to cement dusts.This study was supported by the Korea Ministry of Environment (MOE) as The Environmental Health Action Program [RE201806039, and RE201806027], Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education [NRF2017R1D1A1B03034157]

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC. © The Author(s) 201

Clinical usability of 3D gradient-echo-based ultrashort echo time imaging: Is it enough to facilitate diagnostic decision in real-world practice?

BackgroundWith recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT).MethodsTwenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions.ResultsDepiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p ConclusionsOur study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules

Association between Long-Term Exposure to PM_2.5 and Lung Imaging Phenotype in CODA Cohort

Background and Aims: Ambient particulate matter (PM) is causing respiratory symptoms of individuals at all ages and reducing their lung functions. These individuals could develop chronic pulmonary disease. Recent studies have shown that short-term exposure to PM affects acute exacerbation of respiratory disease. However, evidence about the association between long-term exposure and progression of respiratory diseases remains insufficient. The purpose of this study was to examine the association between long-term exposure of air pollution (PM2.5) and the effect on lung imaging phenotype in dust-exposed Korean adults living near cement factories. Methods: We conducted a cross-sectional analysis on the Chronic Obstructive Pulmonary Disease (COPD) in Dusty Areas (CODA) cohort, which was recruited from 2012 to 2014. Emphysema index and mean wall area were measured using an in-house software program developed by the Korean obstructive lung disease study group based on chest CT scan. A satellite-based model was used to estimate the long-term PM2.5 concentration at each participant’s address. Results: Of 504 eligible participants, 400 participants were analyzed. Their mean age was 71.7 years. Most participants were men (N = 301, 75.3%). The emphysema index of the whole group was 6.63 ± 0.70, and the mean wall area was 68.8 ± 5.2. Image measurement and PM2.5 concentration showed no significant difference in the whole group; however, in the group of subjects with normal lung function, there were significant associations between long-term PM2.5 exposure and emphysema index measurement: 1-year (ß = 0.758, p = 0.021), 3-year (ß = 0.629, p = 0.038), and 5-year (ß = 0.544, p = 0.045). There was no significant association between long-term PM2.5 exposure and mean wall area measurement: 1-year (ß = −0.389, p = 0.832), 3-year (ß = −3.677, p = 0.170), and 5-year (ß = −3.769, p = 0.124). Conclusions: This study suggests that long-term exposure of PM2.5 may affect the emphysematous change in patients with normal lung functions

Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma

Purpose To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1

Correction: Predicting clinical outcome with phenotypic clusters using quantitative CT fibrosis and emphysema features in patients with idiopathic pulmonary fibrosis.

[This corrects the article DOI: 10.1371/journal.pone.0215303.]