Predicting pulmonary function testing from quantified computed tomography using machine learning algorithms in patients with COPD

• Verfasst von: Gawlitza, Joshua Felix Michael [VerfasserIn]
• Verfasst von: Sturm, Timo [VerfasserIn]
• Verfasst von: Spohrer, Kai [VerfasserIn]
• Verfasst von: Henzler, Thomas [VerfasserIn]
• Verfasst von: Akın, Ibrahim [VerfasserIn]
• Verfasst von: Schönberg, Stefan [VerfasserIn]
• Verfasst von: Borggrefe, Martin [VerfasserIn]
• Verfasst von: Haubenreisser, Holger [VerfasserIn]
• Verfasst von: Trinkmann, Frederik [VerfasserIn]
• Titel: Predicting pulmonary function testing from quantified computed tomography using machine learning algorithms in patients with COPD
• Verf.angabe: Joshua Gawlitza, Timo Sturm, Kai Spohrer, Thomas Henzler, Ibrahim Akin, Stefan Schönberg, Martin Borggrefe, Holger Haubenreisser and Frederik Trinkmann
• E-Jahr: 2019
• Jahr: 21 March 2019
• Umfang: 13 S.
• Fussnoten: Gesehen am 12.06.2019
• Titel Quelle: Enthalten in: Diagnostics
• Ort Quelle: Basel : MDPI, 2011
• Jahr Quelle: 2019
• Band/Heft Quelle: 9(2019,1) Artikel-Nummer 33, 13 Seiten
• ISSN Quelle: 2075-4418
• DOI: doi:10.3390/diagnostics9010033
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: chronic obstructive pulmonary disease
• Sach-SW: machine learning
• Sach-SW: thorax
• K10plus-PPN: 1667303198

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.