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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 (2019)
Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD.
In: Diagnostics, 9 (1)
doi: 10.3390/diagnostics9010033
Artikel, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags: Artikel
Erschienen: 2019
Autor(en): Gawlitza, Joshua ; Sturm, Timo ; Spohrer, Kai ; Henzler, Thomas ; Akin, Ibrahim ; Schönberg, Stefan ; Borggrefe, Martin ; Haubenreisser, Holger ; Trinkmann, Frederik
Art des Eintrags: Bibliographie
Titel: Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD
Sprache: Englisch
Publikationsjahr: 21 März 2019
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Diagnostics
Jahrgang/Volume einer Zeitschrift: 9
(Heft-)Nummer: 1
DOI: 10.3390/diagnostics9010033
URL / URN: https://www.mdpi.com/2075-4418/9/1/33
Kurzbeschreibung (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.

Fachbereich(e)/-gebiet(e): 01 Fachbereich Rechts- und Wirtschaftswissenschaften
01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete
01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Software Business & Information Management
Hinterlegungsdatum: 15 Mai 2019 08:38
Letzte Änderung: 15 Mai 2019 08:38
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