Martin Obert1, Marian Kampschulte2, Rebekka Limburg2, Stefan Barańczuk3, Gabriele A Krombach2. 1. Department of Radiology, University Hospital Giessen, Justus-Liebig-University Giessen, Klinikstrasse 33, 35392 Giessen, Germany Members of The German Center for Lung Research (DZL e. V.). Electronic address: martin.obert@radiol.med.uni-giessen.de. 2. Department of Radiology, University Hospital Giessen, Justus-Liebig-University Giessen, Klinikstrasse 33, 35392 Giessen, Germany Members of The German Center for Lung Research (DZL e. V.). 3. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznań, Poland.
Abstract
OBJECTIVES: To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers. MATERIALS AND METHODS: Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram's functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R2 (NR2) effect size were estimated. NR2 was used to set up a ranking list of the different methods. RESULTS: MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR2 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR2 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR2 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR2 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR2 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR2 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR2 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR2 0.48). CONCLUSION: The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas.
OBJECTIVES: To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers. MATERIALS AND METHODS: Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram's functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R2 (NR2) effect size were estimated. NR2 was used to set up a ranking list of the different methods. RESULTS: MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR2 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR2 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR2 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR2 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR2 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR2 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR2 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR2 0.48). CONCLUSION: The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas.
Authors: Andrej Romanov; Michael Bach; Shan Yang; Fabian C Franzeck; Gregor Sommer; Constantin Anastasopoulos; Jens Bremerich; Bram Stieltjes; Thomas Weikert; Alexander Walter Sauter Journal: Diagnostics (Basel) Date: 2021-04-21