Bachl M, Rüttinger T, Siegler L, Schöniger M, Arndt S, Kapsner L, Uder M, May M (2026)
Publication Type: Journal article
Publication year: 2026
DOI: 10.1055/a-2818-5917
Purpose Pulmonary diseases can be analyzed with computed tomography (CT) with regard to their severity, distribution, and morphology. Up to now, this has mainly been done descriptively by radiologists, resulting in a high variance of results. Standardized structured reporting has the great potential to systematically record pulmonary changes and make them available for machine-readable reuse. However, it is still unclear how these structured findings can be analyzed in a simple and communicable way. The aim of this study was therefore to develop a method for comprehensible visualization and to apply it to a quality-assured data set. Materials and Methods For the retrospective evaluation, chest CTs of patients from a total of 15 different disease groups such as infectious diseases, structural and vascular lung diseases, malignant lung diseases, and pleural diseases were included monocentrically. Structured reporting was performed in the RACOON infrastructure using the RECO (Phase 1) template. The systematically reported changes were evaluated both for relative frequency and for location, severity, distribution, and specific additional criteria. The results were summarized into a “CT fingerprint” using a self-developed visual RGB schema and systematically evaluated regarding the differences across the disease groups. Results A total of 359 patients were included. The relative frequency of pathological CT changes shows some overlap between the disease groups. In particular, the differentiation of subgroups is therefore limited. The use of fingerprints allows for better differentiation, as here not only the frequency but also the severity, localization, and distribution of the CT changes and additional criteria are taken into account. For instance, pneumonias can be differentiated depending on the causative pathogen: Based on the frequency distribution of pathological CT changes alone, Covid-19 pneumonia can be quickly classified as a pneumonia, particularly as a subgroup of viral pneumonia, but it is not possible to clearly differentiate it from other viral pneumonias. In contrast, visual analysis of the CT fingerprint allows for a better distinction. Overall, each of the 15 diseases shows an individual fingerprint. Conclusion CT fingerprints enable the visualization and differentiation of pulmonary diseases based on typical CT patterns, making them a potential contribution to pandemic preparedness.
APA:
Bachl, M., Rüttinger, T., Siegler, L., Schöniger, M., Arndt, S., Kapsner, L.,... May, M. (2026). CT Fingerprinting of Pulmonary Diseases: A Visual Analysis of Structured Reporting in RACOON-RECO CT-Fingerprinting pulmonaler Erkrankungen – Eine visuelle Auswertung der strukturierten Befundung in RACOON-RECO. Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. https://doi.org/10.1055/a-2818-5917
MLA:
Bachl, Maximilian, et al. "CT Fingerprinting of Pulmonary Diseases: A Visual Analysis of Structured Reporting in RACOON-RECO CT-Fingerprinting pulmonaler Erkrankungen – Eine visuelle Auswertung der strukturierten Befundung in RACOON-RECO." Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (2026).
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