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

Gawlitza, Joshua ; Sturm, Timo ; Spohrer, Kai ; Henzler, Thomas ; Akin, Ibrahim ; Schönberg, Stefan ; Borggrefe, Martin ; Haubenreisser, Holger ; Trinkmann, Frederik

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URN: urn:nbn:de:bsz:180-madoc-562131
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: Diagnostics : Open Access Journal
Volume: 9
Issue number: 1
Page range: 1-33
Place of publication: Basel
Publishing house: MDPI
ISSN: 2075-4418
Publication language: English
Institution: Business School > ABWL, Organisation u. Wirtschaftsinformatik I (Heinzl)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
610 Medicine and health
Keywords (English): chronic obstructive pulmonary disease , machine learning , thorax
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.
Additional information: Online-Ressource

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