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

cris.virtualsource.author-orcid52a804c7-c914-445b-961d-2168688b5acd
cris.virtualsource.author-orcid2c5fdebc-e3da-4b03-b5fe-5a667be92d35
cris.virtualsource.author-orcide918fa71-6dfb-4387-aeb5-77696cc9bc6c
datacite.rightsopen.access
dc.contributor.authorGabryś, Hubert S
dc.contributor.authorSchniering, Janine
dc.contributor.authorBrunner, Matthias
dc.contributor.authorBogowicz, Marta
dc.contributor.authorBlüthgen, Christian
dc.contributor.authorFrauenfelder, Thomas
dc.contributor.authorGuckenberger, Matthias
dc.contributor.authorMaurer, Britta
dc.contributor.authorTanadini-Lang, Stephanie
dc.date.accessioned2024-10-14T22:43:12Z
dc.date.available2024-10-14T22:43:12Z
dc.date.issued2022
dc.description.abstractBACKGROUND 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.
dc.description.sponsorshipUniversitätsklinik für Rheumatologie und Immunologie
dc.identifier.doi10.48350/175525
dc.identifier.pmid36465941
dc.identifier.publisherDOI10.3389/fmed.2022.988927
dc.identifier.urihttps://boris-portal.unibe.ch/handle/20.500.12422/115889
dc.language.isoen
dc.publisherFrontiers
dc.relation.ispartofFrontiers in medicine
dc.relation.issn2296-858X
dc.relation.organizationClinic of Rheumatology and Immunology
dc.subjectbleomycin interstitial lung disease lung fibrosis preclinical imaging radiomics systemic sclerosis
dc.subject.ddc600 - Technology::610 - Medicine & health
dc.titleTransferability of radiomic signatures from experimental to human interstitial lung disease.
dc.typearticle
dspace.entity.typePublication
dspace.file.typetext
oaire.citation.issue988927
oaire.citation.startPage988927
oaire.citation.volume9
oairecerif.author.affiliationUniversitätsklinik für Rheumatologie und Immunologie
oairecerif.author.affiliationUniversitätsklinik für Rheumatologie und Immunologie
oairecerif.author.affiliationUniversitätsklinik für Rheumatologie und Immunologie
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unibe.date.licenseChanged2022-12-08 03:06:58
unibe.description.ispublishedpub
unibe.eprints.legacyId175525
unibe.refereedtrue
unibe.subtype.articlejournal

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