Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT

Jeremias B. Klaus; Stergios Christodoulidis; Alan A. Peters; Cynthia Hourscht; Laura I. Loebelenz; Jaro Munz; Christophe Schroeder; Dominik Sieron; Dionysios Drakopoulos; Severin Stadler; Johannes T. Heverhagen; Helmut Prosch; Adrian Huber; Moritz Pohl; Stavroula G. Mougiakakou; Andreas Christe; Lukas Ebner

doi:10.1055/a-1901-7814

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Table of Contents

CC BY-NC-ND 4.0 · Rofo 2023; 195(01): 47-54
DOI: 10.1055/a-1901-7814

Chest

Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT

Einfluss von Lungenrekonstruktionsalgorithmen auf die Erkennung von interstitiellen Lungenmustern in der Computertomografie

Authors

Jeremias B. Klaus

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland

²Institute of Forensic Medicine, University of Bern, Bern, Switzerland
Stergios Christodoulidis

³ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
Alan A. Peters

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Cynthia Hourscht

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Laura I. Loebelenz

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Jaro Munz

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Christophe Schroeder

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Dominik Sieron

⁴Department of Radiology, Division City and County Hospitals, INSELGROUP, Bern University Hospital, University of Bern, Bern, Switzerland
Dionysios Drakopoulos

⁴Department of Radiology, Division City and County Hospitals, INSELGROUP, Bern University Hospital, University of Bern, Bern, Switzerland
Severin Stadler

⁵Bern University, University of Bern, Switzerland
Johannes T. Heverhagen

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Helmut Prosch

⁶Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
Adrian Huber

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
Moritz Pohl

⁷Institute of Medical Biometry, University of Heidelberg, Heidelberg, Germany
Stavroula G. Mougiakakou

³ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
Andreas Christe

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland

⁴Department of Radiology, Division City and County Hospitals, INSELGROUP, Bern University Hospital, University of Bern, Bern, Switzerland
Lukas Ebner

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland

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

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.

Key words

CT - thorax - CT-high resolution - diagnostic radiology - technical aspects

Full Text

References

References
1 Lynch DA, Sverzellati N, Travis WD. et al. Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper. Lancet Respir Med 2018; 6: 138-153
2 Lynch DA, Travis WD, Müller NL. et al. Idiopathic interstitial pneumonias: CT features. Radiology 2005; 236: 10-21
3 Ebner L, Christodoulidis S, Stathopoulou T. et al. Meta-analysis of the radiological and clinical features of Usual Interstitial Pneumonia (UIP) and Nonspecific Interstitial Pneumonia (NSIP). PLoS One 2020; 15
4 Raghu G, Collard HR, Egan JJ. et al. An Official ATS/ERS/JRS/ALAT Statement: Idiopathic pulmonary fibrosis: Evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 2011; 183: 788-824
5 Raghu G, Remy-Jardin M, Myers JL. et al. Diagnosis of idiopathic pulmonary fibrosis An Official ATS/ERS/JRS/ALAT Clinical practice guideline. Am J Respir Crit Care Med 2018; 198: e44-e68
6 Funke-Chambour M, Guler SA, Geiser T. et al. New radiological diagnostic criteria: Impact on idiopathic pulmonary fibrosis diagnosis. Eur Respir J 2019; 54
7 Mayo JR. CT evaluation of diffuse infiltrative lung disease: Dose considerations and optimal technique. J Thorac Imaging 2009; 24: 252-259
8 Bankier AA, MacMahon H, Goo JM. et al. Recommendations for measuring pulmonary nodules at CT: A statement from the Fleischner society. Radiology 2017; 285: 584-600
9 Mayo JR, Webb WR, Gould R. et al. High-resolution CT of the lungs: An optimal approach. Radiology 1987; 163: 507-510
10 Christe A, Leidolt L, Huber A. et al. Lung cancer screening with CT: Evaluation of radiologists anddifferent computer assisted detection software (CAD) as first andsecond readers for lung nodule detection at different dose levels. Eur J Radiol 2013; 82
11 Ravenel JG, Leue WM, Nietert PJ. et al. Pulmonary nodule volume: Effects of reconstruction parameters on automated measurements – A phantom study. Radiology 2008; 247: 400-408
12 Takagi S, Nagase H, Hayashi T. et al. Combined multi-kernel chest computed tomography images optimized for depicting both lung and soft tissue. Clin Imaging 2014; 38: 104-108
13 Solomon J, Mileto A, Nelson RC. et al. Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: Dependency on radiation dose and reconstruction algorithm. Radiology 2016; 279: 185-194
14 Ebner L, Roos JE, Christensen JD. et al. Maximum-intensity-projection and computer-aided-detection algorithms as stand-alone reader devices in lung cancer screening using different dose levels and reconstruction kernels. Am J Roentgenol 2016; 207: 282-288
15 Gallardo-Estrella L, Lynch DA, Prokop M. et al. Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification. Eur Radiol 2016; 26: 478-486
16 Alikhani B, Jamali L, Raatschen HJ. et al. Impact of CT parameters on the physical quantities related to image quality for two MDCT scanners using the ACR accreditation phantom: A phantom study. Radiography 2017; 23: 202-210
17 Murata K, Khan A, Rojas KA. et al. Optimization of computed tomography technique to demonstrate the fine structure of the lung. Invest Radiol 1988; 23: 170-175
18 Zwirewich CV, Terriff B, Muller NL. High-spatial-frequency (bone) algorithm improves quality of standard CT of the thorax. Am J Roentgenol 1989; 153: 1169-1173
19 Hopper KD, Kasales CJ, Mahraj R. et al. Routine use of a higher order interpolator and bone algorithm in thoracic CT. Am J Roentgenol 1996; 167: 947-949
20 Swensen SJ, Aughenbaugh GL, Myers JL. Diffuse lung disease: Diagnostic accuracy of CT in patients undergoing surgical biopsy of the lung. Radiology 1997; 205: 229-234
21 Raghu G, Mageto YN, Lockhart D. et al. The accuracy of the clinical diagnosis of new-onset idiopathic pulmonary fibrosis and other interstitial lung disease: A prospective study. Chest 1999; 116: 1168-1174
22 Hunninghake GW, Bridget Zimmerman M, Schwartz DA. et al. Utility of a lung biopsy for the diagnosis of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2001; 164: 193-196
23 Kondoh Y, Taniguchi H, Kawabata Y. et al. Acute exacerbation in idiopathic pulmonary fibrosis: Analysis of clinical and pathologic findings in three cases. Chest 1993; 103: 1808-1812
24 Collard HR, Moore BB, Flaherty KR. et al. Acute exacerbations of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2007; 176: 636-643
25 Collard HR, Ryerson CJ, Corte TJ. et al. Acute exacerbation of idiopathic pulmonary fibrosis an international working group report. Am J Respir Crit Care Med 2016; 194: 265-275
26 Sumikawa H, Johkoh T, Ichikado K. et al. Usual interstitial pneumonia and chronic idiopathic interstitial pneumonia: Analysis of CT appearance in 92 patients. Radiology 2006; 241: 258-266
27 Akira M, Inoue Y, Kitaichi M. et al. Usual interstitial pneumonia and nonspecific interstitial pneumonia with and without concurrent emphysema: Thin-section CT findings. Radiology 2009; 251: 271-279
28 Walsh SLF, Calandriello L, Sverzellati N. et al. Interobserver agreement for the ATS/ERS/JRS/ALAT criteria for a UIP pattern on CT. Thorax 2016; 71: 45-51
29 Watadani T, Sakai F, Johkoh T. et al. Interobserver variability in the CT assessment of honeycombing in the lungs. Radiology 2013; 266: 936-944
30 Siemienowicz ML, Kruger SJ, Goh NSL. et al. Agreement and mortality prediction in high-resolution CT of diffuse fibrotic lung disease. J Med Imaging Radiat Oncol 2015; 59: 555-563