Transferability of radiomic signatures from experimental to human interstitial lung disease

Hubert S Gabryś; Janine Gote-Schniering; Matthias Brunner; Marta Bogowicz; Christian Blüthgen; Thomas Frauenfelder; Matthias Guckenberger; Britta Maurer; Stephanie Tanadini-Lang

doi:10.3389/fmed.2022.988927

Transferability of radiomic signatures from experimental to human interstitial lung disease

Front Med (Lausanne). 2022 Nov 17:9:988927. doi: 10.3389/fmed.2022.988927. eCollection 2022.

Authors

Hubert S Gabryś¹, Janine Gote-Schniering^{2

3}, Matthias Brunner³, Marta Bogowicz¹, Christian Blüthgen⁴, Thomas Frauenfelder⁴, Matthias Guckenberger¹, Britta Maurer³, Stephanie Tanadini-Lang¹

Affiliations

¹ Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.
² Comprehensive Pneumology Center, Institute of Lung Health and Immunity, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany.
³ Department of Rheumatology and Immunology, University Hospital Bern, University of Bern, Bern, Switzerland.
⁴ Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.

Abstract

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.

Keywords: bleomycin; interstitial lung disease; lung fibrosis; preclinical imaging; radiomics; systemic sclerosis.