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

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

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.

Suggested Citation

  • 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," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 113226, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
  • Handle: RePEc:dar:wpaper:113226
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/113226/
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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. Li Shao & Xiaohui Fan & Ningtao Cheng & Leihong Wu & Yiyu Cheng, 2013. "Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-9, July.
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    1. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.

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