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 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

Publication History

Received: 16 November 2021

Accepted: 09 May 2022

Article published online:
06 September 2022

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