Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD

Abstract

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics

“…The substantial difference in the relative volume of residual normal lung density among the three groups, indicating the value is associated with the severity of illness and thus prognosis. The similar LAV values of the three COVID-19 pneumonia groups to the normal CT groups indicated that no obvious sign of emphysema observed in pneumonia at the initial CT scan, as the setting of the LAV threshold for emphysema was -950 HU 30 . The HAV values increased in more severe cases, indicating an increase in high-density lesions and providing evidence that the total score for crazy-paving and consolidation could be as a qualitative indicator for evaluating disease progression.…”

“…The evaluation index method was displayed by quantifying the percentage of the voxel below the low attenuation value (LAV) (threshold of -950HU) and above the high attenuation value (HAV) (threshold of -200 HU). The FWHM parameter marks the width of frequency distribution at half of the maximum CT value, representing the heterogeneity of lung tissue density 30 .…”

What’s New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review

“…Among quantitative CT methods, those related to thoracic imaging are the most studied [20,22,23,[31][32][33][34][35][36][37][38][39][40][41][42][43]. In particular, the applications related to the classification and management of lung nodules are the most well-known and are used in both clinical practice and lung cancer screening programs [20,22,23,[31][32][33][34].…”

Quantitative CT Analysis for Predicting the Behavior of Part-Solid Nodules with Solid Components Less than 6 mm: Size, Density and Shape Descriptors

“…Methods for the quantitative analysis of medical CT images are constantly expanding, and the applications of such methods in the thoracic field are increasing [4,5,9,14,[17][18][19][20][21][22]28,[30][31][32][33][34].…”