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  3. Transferability of radiomic signatures from experimental to human interstitial lung disease.
 

Transferability of radiomic signatures from experimental to human interstitial lung disease.

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BORIS DOI
10.48350/175525
Date of Publication
2022
Publication Type
Article
Division/Institute

Universitätsklinik fü...

Contributor
Gabryś, Hubert S
Schniering, Janine
Universitätsklinik für Rheumatologie und Immunologie
Brunner, Matthias
Universitätsklinik für Rheumatologie und Immunologie
Bogowicz, Marta
Blüthgen, Christian
Frauenfelder, Thomas
Guckenberger, Matthias
Maurer, Britta
Universitätsklinik für Rheumatologie und Immunologie
Tanadini-Lang, Stephanie
Subject(s)

600 - Technology::610...

Series
Frontiers in medicine
ISSN or ISBN (if monograph)
2296-858X
Publisher
Frontiers
Language
English
Publisher DOI
10.3389/fmed.2022.988927
PubMed ID
36465941
Uncontrolled Keywords

bleomycin interstitia...

Description
BACKGROUND

Interstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD.

METHODS

Bleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores.

RESULTS

Predictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model's performance to AUC = 0.912 ± 0.058.

CONCLUSION

Radiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts.
Handle
https://boris-portal.unibe.ch/handle/20.500.12422/115889
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fmed-09-988927.pdftextAdobe PDF3.12 MBpublishedOpen
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