A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy

: Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory μCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications

Multiphase micro-computed tomography reconstructions provide dynamic respiratory function in a mouse lung fibrosis model

Summary: Micro-computed tomography derived functional biomarkers used in lung disease research can significantly complement end-stage histomorphometric measures while also allowing for longitudinal studies. However, no approach for visualizing lung dynamics across a full respiratory cycle has yet been described.Using bleomycin-induced lung fibrosis and the antifibrotic drug nintedanib as a test model, we implemented a four-dimensional (4D) micro-CT imaging approach consisting of 30 reconstructed volumes per respiratory cycle, coupled with deep-learning-assisted segmentation of lung volumes. 4D micro-CT provided an accurate description of inhalatory and exhalatory lung dynamics under resting conditions and revealed an inflammation-related obstructive pattern at day 7, followed by a restrictive pattern associated with fibrosis development at day 21. A milder restriction and fibrotic pathology resulted from nintedanib treatment. The similarity of 4D micro-CT data with those produced by diagnostic measurements, also points to its great potential as an exploratory tool for the discovery of clinically relevant therapeutic compounds