Risk of Malignancy in Patients with Asthma-COPD Overlap Compared to Patients with COPD without Asthma.

Chronic inflammation such as asthma may lead to higher risks of malignancy, which may be inhibited by anti-inflammatory medicine such as inhaled corticosteroids (ICS). The aim of this study was to evaluate if patients with asthma-Chronic Obstructive Pulmonary Disease (COPD) overlap have a higher risk of malignancy than patients with COPD without asthma, and, secondarily, if inhaled corticosteroids modify such a risk in a nationwide multi-center retrospective cohort study of Danish COPD-outpatients with or without asthma. Patients with asthma-COPD overlap were propensity score matched (PSM) 1:2 to patients with COPD without asthma. The endpoint was cancer diagnosis within 2 years. Patients were stratified depending on prior malignancy within 5 years. ICS was explored as a possible risk modifier. We included 50,897 outpatients with COPD; 88% without prior malignancy and 20% with asthma. In the PSM cohorts, 26,003 patients without prior malignancy and 3331 patients with prior malignancy were analyzed. There was no association between asthma-COPD overlap and cancer with hazard ratio (HR) = 0.92, CI = 0.78-1.08, p = 0.31 (no prior malignancy) and HR = 1.04, CI = 0.85-1.26, and p = 0.74 (prior malignancy) as compared to patients with COPD without asthma. ICS did not seem to modify the risk of cancer. In conclusion, in our study, asthma-COPD overlap was not associated with an increased risk of cancer events.

Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers.

PURPOSE: To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans. METHODS: A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p < 0.004. RESULTS: Performances of the model and observers were equivalent (AUC 0.932 versus 0.910, p = 0.184) for risk-assessment of malignant and benign nodules of all sizes. However, human readers performed superior to the computer model for differentiating malignant nodules from size-matched benign nodules (AUC 0.819 versus 0.706, p < 0.001). Large variations between observers were seen for ROC areas and ranges of risk scores. Morphological findings indicative of malignancy referred to border characteristics (spiculation, p < 0.001) and perinodular architectural deformation (distortion of surrounding lung parenchyma architecture, p < 0.001; pleural retraction, p = 0.002). CONCLUSIONS: Computer model and human observers perform equivalent for differentiating malignant from randomly selected benign nodules, confirming the high potential of computer models for nodule risk estimation in population based screening studies. However, computer models highly rely on size as discriminator. Incorporation of other morphological criteria used by human observers to superiorly discriminate size-matched malignant from benign nodules, will further improve computer performance.

Computer tomography guided lung biopsy using interactive breath-hold control: a randomized study.

BACKGROUND: Interactive breath-hold control (IBC) may improve the accuracy and decrease the complication rate of computed tomography (CT)-guided lung biopsy, but this presumption has not been proven in a randomized study. METHODS: Patients admitted for CT-guided lung biopsy were randomized to biopsy either with (N=201) or without (N=206) IBC. Biopsy accuracy, procedure time, radiation, and complications were compared in the two groups. Predictors for pneumothorax were analyzed. RESULTS: Procedures performed with the use of IBC (N=130) did not show higher biopsy accuracy (P=0.979) but were associated with a higher risk of pneumothorax (P=0.022) compared to procedures without the use of IBC (N=171). Overall, 50% of the biopsies were malignant, 13% were benign, and 33% were inconclusive (4% missing). Long needle time (P=0.037) and small nodule size (P=0.001) were predictors of pneumothorax. CONCLUSIONS: The use of IBC for CT-guided lung biopsy was not an advantage for unselected patients in our care, since it did not improve the biopsy accuracy and the risk of pneumothorax was increased.

Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines.

OBJECTIVES: To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances. METHODS: From the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: Dlongest-C (PanCan), DmeanAxial (NCCN), both obtained from axial sections, and Dmean3D (Lung-RADS). Subsequently all diameter definitions were applied uniformly to all systems. Areas under the ROC curves (AUC) were used to evaluate risk discrimination. RESULTS: PanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying Dlongest-C, Dmean3D and DmeanAxial, PanCan remained superior to Lung-RADS (p < 0.001 - p = 0.001) and NCCN (p < 0.001 - p = 0.016). Diameter definition significantly influenced NCCN's performance with Dlongest-C being the worst (Dlongest-C vs. Dmean3D, p = 0.005; Dlongest-C vs. DmeanAxial, p = 0.016). CONCLUSIONS: Without follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition. KEY POINTS: • PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules. • Nodule size definition had no significant impact on Lung-RADS and PanCan model. • 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter. • Longest diameter achieved lowest performance for all models. • Mean diameter performed equivalently when derived from axial sections and from volumetry.

Ultrasound-guided Lung Biopsy in the Hands of Respiratory Physicians: Diagnostic Yield and Complications in 215 Consecutive Patients in 3 Centers.

BACKGROUND: The aim of the study was to determine the diagnostic yield and prevalence of complications of ultrasound-guided transthoracic needle aspiration biopsies (US-TTNAB) performed by respiratory physicians after implementation of the procedure in an everyday clinical setting at 3 different centers. METHODS: Patients were included if they during the period from January 2012 to August 2014 had a registered US-TTNAB procedure code or if a US biopsy registration form had been filled out at either of the participating centers. Histology or cytology results were used as a reference test for diagnoses that could be made based on these results. Reference test for the remaining diagnoses was clinical follow-up. The diagnostic yield of US-TTNAB was defined as the proportion of patients in which the result of the US-TTNAB was consistent with the reference test. RESULTS: A total of 215 patients in which a primary US-TTNAB had been performed were identified. The most common biopsy sites were lungs and pleurae with a total of 164 (76.3%) patients and 31 patients (14.4%), respectively. US-TTNAB diagnostic yield was 76.9% (95% CI, 70.3%-83.4%) for malignant diagnoses and 47.6% (95% CI, 31.9%-63.4%) for nonmalignant diagnoses. The most common complications of US-TTNAB were pneumothorax (2.5%; 95% CI, 0.03%-4.6%) and pain at the biopsy site (2%; 95% CI, 0.04%-3.9%). No fatalities related to US-TTNAB were observed. CONCLUSION: US-TTNAB performed by respiratory physicians is a safe procedure with a low risk of complications and the diagnostic yield to establish a malignant diagnosis is acceptable.

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.