RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026
CT Fingerprinting of Pulmonary Diseases: A Visual Analysis of Structured Reporting in RACOON-RECO.
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Bachl Maximilian, Rüttinger Theresa, Siegler Lisa, Schöniger Maximilian, Arndt Sebastian, Kapsner Lorenz A, Uder Michael, May Matthias Stefan
Аннотация
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.
KEY POINTS: · CT fingerprints allow for a visually clear presentation of structured reports from chest CTs.. · Pulmonary disease groups can thus be compared and categorized based on CT characteristics.. · The retrospective analysis of Covid-19 pneumonia shows that even previously unknown lung diseases could be quickly assigned to known disease groups.. · Pneumonias show pathogen-specific patterns..
CITATION FORMAT: · Bachl M, Rüttinger T, Siegler L etal. CT Fingerprinting of Pulmonary Diseases: A Visual Analysis of Structured Reporting in RACOON-RECO. Rofo 2026; DOI 10.1055/a-2818-5917.
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.
KEY POINTS: · CT fingerprints allow for a visually clear presentation of structured reports from chest CTs.. · Pulmonary disease groups can thus be compared and categorized based on CT characteristics.. · The retrospective analysis of Covid-19 pneumonia shows that even previously unknown lung diseases could be quickly assigned to known disease groups.. · Pneumonias show pathogen-specific patterns..
CITATION FORMAT: · Bachl M, Rüttinger T, Siegler L etal. CT Fingerprinting of Pulmonary Diseases: A Visual Analysis of Structured Reporting in RACOON-RECO. Rofo 2026; DOI 10.1055/a-2818-5917.