Quantitative lung morphology: semi-automated measurement of mean linear intercept.

ABSTRACT: BACKGROUND:Quantifying morphologic changes is critical to our understanding of the pathophysiology of the lung. Mean linear intercept (MLI) measures are important in the assessment of clinically relevant pathology, such as emphysema. However, qualitative measures are prone to error and bias, while quantitative methods such as mean linear intercept (MLI) are manually time consuming. Furthermore, a fully automated, reliable method of assessment is nontrivial and resource-intensive. METHODS:We propose a semi-automated method to quantify MLI that does not require specialized computer knowledge and uses a free, open-source image-processor (Fiji). We tested the method with a computer-generated, idealized dataset, derived an MLI usage guide, and successfully applied this method to a murine model of particulate matter (PM) exposure. Fields of randomly placed, uniform-radius circles were analyzed. Optimal numbers of chords to assess based on MLI were found via receiver-operator-characteristic (ROC)-area under the curve (AUC) analysis. Intraclass correlation coefficient (ICC) measured reliability. RESULTS:We demonstrate high accuracy (AUCROC >?0.8 for MLIactual >?63.83 pixels) and excellent reliability (ICC?=?0.9998, p 

SUBMITTER: Crowley G 

PROVIDER: S-EPMC6842138 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature