Distribution of emphysema in heavy smokers: Impact on pulmonary function

SummaryPurposeTo investigate impact of distribution of computed tomography (CT) emphysema on severity of airflow limitation and gas exchange impairment in current and former heavy smokers participating in a lung cancer screening trial.Materials and MethodsIn total 875 current and former heavy smokers underwent baseline low-dose CT (30mAs) in our center and spirometry and diffusion capacity testing on the same day as part of the Dutch–Belgian Lung Cancer Screening Trial (NELSON). Emphysema was quantified for 872 subjects as the number of voxels with an apparent lowered X-ray attenuation coefficient. Voxels attenuated <−950HU were categorized as representing severe emphysema (ES950), while voxels attenuated between −910HU and −950HU represented moderate emphysema (ES910). Impact of distribution on severity of pulmonary function impairment was investigated with logistic regression, adjusted for total amount of emphysema.ResultsFor ES910 an apical distribution was associated with more airflow obstruction and gas exchange impairment than a basal distribution (both p<0.01). The FEV1/FVC ratio was 1.6% (95% CI 0.42% to 2.8%) lower for apical predominance than for basal predominance, for Tlco/VA the difference was 0.12% (95% CI 0.076–0.15%). Distribution of ES950 had no impact on FEV1/FVC ratio, while an apical distribution was associated with a 0.076% (95% CI 0.038–0.11%) lower Tlco/VA (p<0.001).ConclusionIn a heavy smoking population, an apical distribution is associated with more severe gas exchange impairment than a basal distribution; for moderate emphysema it is also associated with a lower FEV1/FVC ratio. However, differences are small, and likely clinically irrelevant

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction: Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC. Methods: Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [18F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC. Results: In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively). Conclusion: CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients

Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC)

Objectives: Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC. Materials and Methods: Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for ‘small cell-like’ or ‘non-small cell-like’ appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients. Results: Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like). Conclusion: LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease

Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15-20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40-50%

Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15–20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40–50%

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

peer reviewedDetection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours

Pulmonary nodules: Interscan variability of semiautomated volume measurements with multisection CT -- influence of inspiration level, nodule size, and segmentation performance

Purpose: To prospectively assess the precision of semiautomated volume measurements of pulmonary nodules at low-dose multi-detector row computed tomography (CT) and to investigate the influence of nodule size, segmentation algorithm, and inspiration level. Materials and Methods: This study had institutional review board approval; written informed consent was obtained from all patients. Between June 2004 and March 2005, 20 patients (15 men, five women; age range, 40-84 years; mean age, 57 years) referred for chest CT for known lung metastases under-went two additional low-dose chest CT examinations without contrast material (collimation, 16 x 0.75 mm). Between these examinations, patients got off and on the table to simulate the conditions for a follow-up examination. Noncalcified solid pulmonary nodules between 15 and 500 mm(3) that did not abut vessel or pleura were measured in both studies by using widely applied commercial semiautomated software. Interscan variability was established with the Bland and Altman approach. The impact of nodule shape (spherical or nonspherical) on measurement variability was assessed by using one-way analysis of variance, while the contributions of mean nodule volume and change in lung volume were investigated with univariate linear regression for completely (group A) and incompletely (group B) segmented nodules. Results: Two hundred eighteen eligible nodules (volume range, 16.4-472.7 mm(3); 106 spherical, 112 nonspherical) were evaluated. The 95% confidence interval for difference in measured volumes was -21.2%, 23.8% ( mean difference, 1.3%). The precision of nodule segmentation was highly dependent on nodule shape (P <.001) and was weakly related to inspiration level for completely segmented nodules (r = -0.20; P <.047), while mean nodule volume did not show any effect (P = .15 and P = .81 for group A and B nodules, respectively). Conclusion: Variation of semiautomated volume measurements of pulmonary nodules can be substantial. Segmentation represents the most important factor contributing to measurement variability, while change in inspiration level has only a weak effect for completely segmented nodule

Quantifying the Extent of Emphysema:Factors Associated with Radiologists' Estimations and Quantitative Indices of Emphysema Severity Using the ECLIPSE Cohort

Rationale and Objectives: This study investigated what factors radiologists take into account when estimating emphysema severity and assessed quantitative computed tomography (CT) measurements of low attenuation areas. Materials and Methods: CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 smoker controls, and 184 nonsmoker controls from the Evaluation of COPD Longitudinally to Indentify Surrogate Endpoints (ECLIPSE) study. CT scans were analyzed using the threshold technique (% Results: The percent low attenuation area (%LAA) and visual scores of emphysema severity correlated well (r=0.77, P Conclusions: Visual estimates of emphysema are not only determined by the extent of LAA, but also by lesion size, predominant type, and distribution of emphysema and presence/absence of areas of small airways disease. A computer analysis of low attenuation cluster size helps quantitative algorithms discriminate low attenuation areas from gas trapping, image noise, and emphysema