Interstitial lung abnormalities (ILA) on routine chest CT: Comparison of radiologists' visual evaluation and automated quantification

Purpose: We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with ra-diologists' visual analysis.Method: This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines. We developed a fully automated quantitative tool for detecting ILA by modifying deep learning-based quantification of interstitial lung disease and evaluated its performance using the radiologists' consensus for ILA as a reference standard.Results: A total of 336 participants (mean age, 70.5 +/- 6.1 years; M:F = 282:54) were included. Inter-reader agreements were substantial for the presence of ILA (weighted Kappa, 0.74) and fair for its subtypes (weighted Kappa, 0.38). The quantification system for identifying ILA using a threshold of 5 % in at least one zone showed 67.6 % sensitivity, 93.3 % specificity, and 90.5 % accuracy. Eight of 20 (40 %) false positives identified by the system were underestimated by readers for ILA extent. Contrast-enhancement in a certain vendor and suboptimal inspiration caused a true false-positive on the system (all P < 0.05). The best cut-off value of abnormality extent detecting ILA on the system was 3.6 % (sensitivity, 84.8 %; specificity 92.4 %).Conclusions: Inter-reader agreement was substantial for ILA but only fair for its subtypes. Applying an automated quantification system in routine clinical practice may aid the objective identification of ILA.

Automated summary

The study aimed to evaluate the performance of fully automated quantitative software in detecting interstitial lung abnormalities (ILA) based on the Fleischner Society guidelines. Results showed substantial agreement among readers for ILA presence but only fair agreement for its subtypes. Applying an automated quantification system may aid in the objective identification of ILA in routine clinical practice.