Chest x-ray findings in tuberculosis patients identified by passive and active case finding: A retrospective study

Background: Chest x-ray is central in screening and diagnosis of tuberculosis. However, sputum culture remains gold standard for diagnosis. Aim: To establish the rate of normal chest x-rays in tuberculosis patients found by spot sputum culture screening, and compare them to a group identified through passive case finding. Method: Chest x-rays from 39 culture-positive patients, identified by spot sputum culture screening in Copenhagen from 2012 to 2014, were included in the study (spot sputum culture group(SSC)). 39 normal chest x-rays from persons screened by mobile x-ray, and 39 chest x-rays from tuberculosis-patients identified through passive case finding(PCF) were anonymised and randomised. Two respiratory physicians and two radiologists assessed the chest x-rays. Results: The normal chest x-ray rate was higher in the non-tuberculosis control group (median = 32 (82.1%), range = 74.4% – 100%), compared to the SSC group (median = 7 (17.9%), range = 10.3% – 33.3%), and the PCF controls (median = 3(7.7%), range = 2.6% – 15.4%). In the SSC group 14 (35.9%) were categorized as normal by at least one study participant. Conclusion: A substantial minority of patients diagnosed with tuberculosis by spot sputum culture screening, and through passive case finding would not have been identified with chest x-ray alone, highlighting that a normal chest x-ray does not exclude pulmonary tuberculosis. Keywords: Tuberculosis, Chest x-ray changes, Passive case finding, Active case finding, Chest x-ray assessment, Normal chest x-ra

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

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.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.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).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

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

Observer agreement for malignancy probability score.

<p>Examples of nodules for which observers and the PanCan model uniformly scored a high or low malignancy probability. For the observers, a threshold of < 25% averaged over all observers was considered a 'low risk' score, and a threshold of > 60% was considered a 'high risk' score. For the PanCan model, a threshold of < 6% was considered as a 'low risk' score and a threshold of > 30% was considered as a 'high risk' score. Nodules are displayed in the axial plane. From left to right: A) Part-solid <i>malignant</i> nodule, 13 mm, observers scored 65%, PanCan 31.9% corresponding to a uniformly true positive score; B) Solid <i>malignant</i> nodule, 4 mm, observers scored 9.5%, PanCan 0.3%; corresponding to a uniformly false negative score; C) Solid <i>benign</i> perifissural nodule, 11 mm, observers scored 1.9%, PanCan 0%, corresponding to a uniformly true negative score; D) Part-solid <i>benign</i> nodule, 27 mm, observers scored 60%, PanCan 30.7%, corresponding to a uniformly false positive score.</p

Performance of observers and the various computer models (PanCan 1b and 2b) for the discrimination between malignant and benign nodules for random and size-matched benign nodules versus malignant nodules.

<p>Performance of observers and the various computer models (PanCan 1b and 2b) for the discrimination between malignant and benign nodules for random and size-matched benign nodules versus malignant nodules.</p

Observer disagreement for malignancy probability score.

<p>Examples of nodules for which the PanCan model and the observers showed conflicting malignancy probability scores. For the observers, a threshold of < 25% averaged over all observers was considered a 'low' score, and a threshold of > 60% was considered a 'high' score. For the PanCan model, a threshold of < 6% was considered a 'low' score and a threshold of > 30% was considered a 'high' score. Nodules are displayed in axial plane. From left to right: A) Solid <i>malignant</i> nodule, 15 mm, observers scored 24%, PanCan 35%; B) Pure ground-glass <i>malignant</i> nodule, 9 mm, observers scored 58%, PanCan 4%; C) Solid <i>benign</i> nodule, 16.5 mm, observers scored 14%, PanCan 37%; D) Part-solid <i>benign</i> nodule, 13 mm, observers scored 65%, PanCan 14%.</p

Performance of observers and PanCan model.

<p>ROC curves of the observers 1–11, the PanCan model 2b, the PanCan model 1b, and only nodule size as predictor for (A) discriminating randomly selected benign nodules from malignant nodules on the left, and (B) on the right discriminating size-matched benign nodules from malignant nodules. Note that in Fig 1A the PanCan model outperforms human observers at a specificity > 80%, while in Fig 1B all human observers perform better than the PanCan model.</p