Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT
Abstract Background Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD). Purpose To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the...
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| Vydané v: | RöFo : Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebende Verfahren Ročník 195; číslo 1; s. 47 - 54 |
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| Jazyk: | English |
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Rüdigerstraße 14, 70469 Stuttgart, Germany
Georg Thieme Verlag KG
01.01.2023
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| Abstract | Abstract
Background
Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD).
Purpose
To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns.
Materials and Methods
We retrospectively extracted between 15–25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results.
Results
The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73–1.06;
p
= 0.187). Furthermore, the consultants’ odds of correct pattern recognition was 78 % higher than the residents’ odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62–5.06;
p
= 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ± 0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ± 0.17/κ = 0.38 ± 0.17.
Conclusion
There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification.
Key points:
There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in interstitial lung disease.
There are even non-significant tendencies that the use of soft kernels lead to a higher probability of correct pattern identification.
These results challenge the current recommendations and the routinely performed separate lung kernel reconstructions for lung parenchyma analysis.
Citation Format
Klaus JB, Christodoulidis S, Peters AA et al. Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT. Fortschr Röntgenstr 2023; 195: 47 – 54 |
|---|---|
| AbstractList | Abstract
Background
Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD).
Purpose
To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns.
Materials and Methods
We retrospectively extracted between 15–25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results.
Results
The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73–1.06;
p
= 0.187). Furthermore, the consultants’ odds of correct pattern recognition was 78 % higher than the residents’ odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62–5.06;
p
= 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ± 0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ± 0.17/κ = 0.38 ± 0.17.
Conclusion
There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification.
Key points:
There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in interstitial lung disease.
There are even non-significant tendencies that the use of soft kernels lead to a higher probability of correct pattern identification.
These results challenge the current recommendations and the routinely performed separate lung kernel reconstructions for lung parenchyma analysis.
Citation Format
Klaus JB, Christodoulidis S, Peters AA et al. Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT. Fortschr Röntgenstr 2023; 195: 47 – 54 Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD).To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns.We retrospectively extracted between 15-25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results.The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73-1.06; p = 0.187). Furthermore, the consultants' odds of correct pattern recognition was 78 % higher than the residents' odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62-5.06; p = 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ± 0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ± 0.17/κ = 0.38 ± 0.17.There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification. · There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in interstitial lung disease.. · There are even non-significant tendencies that the use of soft kernels lead to a higher probability of correct pattern identification.. · These results challenge the current recommendations and the routinely performed separate lung kernel reconstructions for lung parenchyma analysis.. CITATION FORMAT: · Klaus JB, Christodoulidis S, Peters AA et al. Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT. Fortschr Röntgenstr 2023; 195: 47 - 54.Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD).To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns.We retrospectively extracted between 15-25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results.The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73-1.06; p = 0.187). Furthermore, the consultants' odds of correct pattern recognition was 78 % higher than the residents' odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62-5.06; p = 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ± 0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ± 0.17/κ = 0.38 ± 0.17.There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification. · There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in interstitial lung disease.. · There are even non-significant tendencies that the use of soft kernels lead to a higher probability of correct pattern identification.. · These results challenge the current recommendations and the routinely performed separate lung kernel reconstructions for lung parenchyma analysis.. CITATION FORMAT: · Klaus JB, Christodoulidis S, Peters AA et al. Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT. Fortschr Röntgenstr 2023; 195: 47 - 54. Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD).To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns.We retrospectively extracted between 15-25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results.The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73-1.06; = 0.187). Furthermore, the consultants' odds of correct pattern recognition was 78 % higher than the residents' odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62-5.06; = 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ± 0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ± 0.17/κ = 0.38 ± 0.17.There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification. · There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in interstitial lung disease.. · There are even non-significant tendencies that the use of soft kernels lead to a higher probability of correct pattern identification.. · These results challenge the current recommendations and the routinely performed separate lung kernel reconstructions for lung parenchyma analysis.. CITATION FORMAT: · Klaus JB, Christodoulidis S, Peters AA et al. Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT. Fortschr Röntgenstr 2023; 195: 47 - 54. |
| Abstract_FL | Zusammenfassung
Hintergrund
Trotz den aktuellen Empfehlungen gibt es keine aktuelle wissenschaftliche Studie, welche den Einfluss von CT-Rekonstruktionskernels auf die Erkennung von Mustern der interstitiellen Lungenerkrankungen (ILD) vergleicht.
Ziel
Untersuchung der Sensitivität von scharfen Lungen- (i70) und weichen Weichteil- (i30) CT-Rekonstruktionskernels zur Diagnose von ILD-Mustern.
Material und Methoden
Retrospektiv wurden von 23 Probanden 15–25 Muster annotiert (1 Annotation = 15 Schichten à 1 mm), was 408 Annotations-Stapel pro Lungen- und Weichteilkernel ergab. 2 subspezialisierte Thorax-Radiologen definierten den Referenzstandard im Konsens. 4 Assistenzärzte, 2 Thorax-Fellows und 2 Fachärzte mit 3–13 Jahren Erfahrung in der Radiologie beurteilten die Daten verblindet. Aufgrund der mehrfach geclusterten Daten wurde ein generalisiertes lineares gemischtes Modell (GLMM) mit den Interaktionstermen Kernel/Erfahrung zur Analyse verwendet.
Ergebnisse
Die Resultate des GLMM deuteten eine um 12 % niedrigere Treffsicherheit für die korrekte Mustererkennung an beim Verwenden des Lungenkernels im Vergleich zum Weichteilkernel, jedoch erreichten die Resultate keine statistische Signifikanz (OR 0.88; 95%-CI, 0.73–1.06;
p
= 0.187). Des Weiteren zeigten die Fachärzte eine um 78 % höhere Wahrscheinlichkeit der korrekten Mustererkennung im Vergleich zu den Assistenzärzten, doch auch dieses Resultat war nicht statistisch signifikant (OR 1.78; 95%-KI 0.62–5.06;
p
= 0.283). Die Intra-rater-Übereinstimmung war substantiell (κ = 0.63 ± 0.19), die gemittelte Inter-rater-Übereinstimmung für Lungen-/Weichteilkernel betrug κ = 0.37 ± 0.17/κ = 0.38 ± 0.17.
Schlussfolgerung
Insgesamt gab es keinen signifikanten Einfluss von CT-Kernel oder Erfahrung des befundenden Radiologen auf die korrekte Erkennung von ILD-Mustern. Es gibt nicht-signifikante Trends, dass die Verwendung eines Weichteilkernels und eine größere Erfahrung zu einer höheren Wahrscheinlichkeit der korrekten Mustererkennung führen.
Kernaussagen:
Es besteht kein signifikanter Unterschied zwischen mit Lungen- und Weichteilkernel-rekonstruierten CT-Bildern für die korrekte Erkennung von ILD-Mustern.
Es gibt sogar nicht-signifikante Trends, dass die Verwendung des Weichteilkernels mit höherer Wahrscheinlichkeit zu einer korrekten Mustererkennung führt.
Diese Ergebnisse stellen die aktuellen Empfehlungen und die routinemässig durchgeführten separaten Lungenkernelrekonstruktionen für die Analyse des Lungenparenchyms in Frage. |
| Author | Loebelenz, Laura I. Christe, Andreas Prosch, Helmut Mougiakakou, Stavroula G. Pohl, Moritz Hourscht, Cynthia Stadler, Severin Klaus, Jeremias B. Drakopoulos, Dionysios Munz, Jaro Christodoulidis, Stergios Peters, Alan A. Ebner, Lukas Schroeder, Christophe Heverhagen, Johannes T. Huber, Adrian Sieron, Dominik |
| Author_xml | – sequence: 1 givenname: Jeremias B. orcidid: 0000-0002-2367-0184 surname: Klaus fullname: Klaus, Jeremias B. email: jeremias.klaus@insel.ch organization: Department of Interventional, Pediatric and Diagnostic Radiology, Inselspital Bern – sequence: 2 givenname: Stergios surname: Christodoulidis fullname: Christodoulidis, Stergios organization: ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland – sequence: 3 givenname: Alan A. surname: Peters fullname: Peters, Alan A. organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 4 givenname: Cynthia surname: Hourscht fullname: Hourscht, Cynthia organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 5 givenname: Laura I. orcidid: 0000-0003-4625-6565 surname: Loebelenz fullname: Loebelenz, Laura I. organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 6 givenname: Jaro surname: Munz fullname: Munz, Jaro organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 7 givenname: Christophe surname: Schroeder fullname: Schroeder, Christophe organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 8 givenname: Dominik surname: Sieron fullname: Sieron, Dominik organization: Department of Radiology, Division City and County Hospitals, INSELGROUP, Bern University Hospital, University of Bern, Bern, Switzerland – sequence: 9 givenname: Dionysios surname: Drakopoulos fullname: Drakopoulos, Dionysios organization: Department of Radiology, Division City and County Hospitals, INSELGROUP, Bern University Hospital, University of Bern, Bern, Switzerland – sequence: 10 givenname: Severin surname: Stadler fullname: Stadler, Severin organization: Bern University, University of Bern, Switzerland – sequence: 11 givenname: Johannes T. surname: Heverhagen fullname: Heverhagen, Johannes T. organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 12 givenname: Helmut orcidid: 0000-0002-6119-6364 surname: Prosch fullname: Prosch, Helmut organization: Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria – sequence: 13 givenname: Adrian surname: Huber fullname: Huber, Adrian organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland – sequence: 14 givenname: Moritz surname: Pohl fullname: Pohl, Moritz organization: Institute of Medical Biometry, University of Heidelberg, Heidelberg, Germany – sequence: 15 givenname: Stavroula G. surname: Mougiakakou fullname: Mougiakakou, Stavroula G. organization: ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland – sequence: 16 givenname: Andreas surname: Christe fullname: Christe, Andreas organization: Department of Radiology, Division City and County Hospitals, INSELGROUP, Bern University Hospital, University of Bern, Bern, Switzerland – sequence: 17 givenname: Lukas surname: Ebner fullname: Ebner, Lukas organization: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland |
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Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern... Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in... |
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| SubjectTerms | Algorithms Chest Humans Lung - diagnostic imaging Radiographic Image Enhancement - methods Retrospective Studies Tomography, X-Ray Computed - methods |
| Title | Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT |
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