Use of chest radiographs in epidemiological investigations of pneumoconioses

Bayesian inference of dependent kappa for binary ratings

In medical and social science research, reliability of testing methods measured through inter- and intraobserver agreement is critical in disease diagnosis. Often comparison of agreement across multiple testing methods is sought in situations where testing is carried out on the same experimental units rendering the outcomes to be correlated. In this article, we first developed a Bayesian method for comparing dependent agreement measures under a grouped data setting. Simulation studies showed that the proposed methodology outperforms the competing methods in terms of power, while maintaining a decent type I error rate. We further developed a Bayesian joint model for comparing dependent agreement measures adjusting for subject and rater-level heterogeneity. Simulation studies indicate that our model outperforms a competing method that is used in this context. The developed methodology was implemented on a key measure on a dichotomous rating scale from a study with six raters evaluating three classification methods for chest radiographs for pneumoconiosis developed by the International Labor Office.

Novel Logistic Regression Model of Chest CT Attenuation Coefficient Distributions for the Automated Detection of Abnormal (Emphysema or ILD) Versus Normal Lung

RATIONALE AND OBJECTIVES

We evaluated the role of automated quantitative computed tomography (CT) scan interpretation algorithm in detecting interstitial lung disease (ILD) and/or emphysema in a sample of elderly subjects with mild lung disease. We hypothesized that the quantification and distributions of CT attenuation values on lung CT, over a subset of Hounsfield units (HUs) range (-1000 HU, 0 HU), can differentiate early or mild disease from normal lung.

MATERIALS AND METHODS

We compared the results of quantitative spiral rapid end-exhalation (functional residual capacity, FRC) and end-inhalation (total lung capacity, TLC) CT scan analyses of 52 subjects with radiographic evidence of mild fibrotic lung disease to the results of 17 normal subjects. Several CT value distributions were explored, including (1) that from the peripheral lung taken at TLC (with peels at 15 or 65 mm), (2) the ratio of (1) to that from the core of lung, and (3) the ratio of (2) to its FRC counterpart. We developed a fused-lasso logistic regression model that can automatically identify sub-intervals of -1000 HU and 0 HU over which a CT value distribution provides optimal discrimination between abnormal and normal scans.

RESULTS

The fused-lasso logistic regression model based on (2) with 15-mm peel identified the relative frequency of CT values of over -1000 HU and -900 and those over -450 HU and -200 HU as a means of discriminating abnormal versus normal lung, resulting in a zero out-sample false-positive rate, and 15% false-negative rate of that was lowered to 12% by pooling information.

CONCLUSIONS

We demonstrated the potential usefulness of this novel quantitative imaging analysis method in discriminating ILD and/or emphysema from normal lungs.