The intratracheal instillation of bleomycin in mice induces early damage to alveolar epithelial cells and development of inflammation followed by fibrotic tissue changes and represents the most widely used model of pulmonary fibrosis to investigate human IPF. Histopathology is the gold standard for assessing lung fibrosis in rodents, however it precludes repeated and longitudinal measurements of disease progression and does not provide information on spatial and temporal distribution of tissue damage. Here we investigated the use of the Micro-CT technique to allow the evaluation of disease onset and progression at different time-points in the mouse bleomycin model of lung fibrosis. Micro-CT was throughout coupled with histological analysis for the validation of the imaging results. In bleomycin-instilled and control mice, airways and lung morphology changes were assessed and reconstructed at baseline, 7, 14 and 21 days post-treatment based on Micro-CT images. Ashcroft score, percentage of collagen content and percentage of alveolar air area were detected on lung slides processed by histology and subsequently compared with Micro-CT parameters. Extent (%) of fibrosis measured by Micro-CT correlated with Ashcroft score, the percentage of collagen content and the percentage of alveolar air area (r 2 = 0.91; 0.77; 0.94, respectively). Distal airway radius also correlated with the Ashcroft score, the collagen content and alveolar air area percentage (r 2 = 0.89; 0.78; 0.98, respectively). Micro-CT data were in good agreement with histological read-outs as micro-CT was able to quantify effectively and non-invasively disease progression longitudinally and to reduce the variability and number of animals used to assess the damage. This suggests that this technique is a powerful tool for understanding experimental pulmonary fibrosis and that its use could translate into a more efficient drug discovery process, also helping to fill the gap between preclinical setting and clinical practice.

Although increasing used in the preclinical testing of new anti-fibrotic drugs, a thorough validation of micro-computed tomography (CT) in pulmonary fibrosis models has not been performed. Moreover, no attempts have been made so far to define density thresholds to discriminate between aeration levels in lung parenchyma. In the present study, a histogram-based analysis was performed in a mouse model of bleomycin (BLM)-induced pulmonary fibrosis by micro-CT, evaluating longitudinal density changes from 7 to 21 days after BLM challenge, a period representing the progression of fibrosis. Two discriminative densitometric indices (i.e. 40th and 70th percentiles) were extracted from Hounsfield Unit density distributions and selected for lung fibrosis staging. The strong correlation with histological findings (r_Spearman = 0.76, p < 0.01) confirmed that variations in 70th percentile could reflect a pathological lung condition and estimate the effect of antifibrotic treatments. This index was therefore used to define lung aeration levels in mice distinguishing in hyper-inflated, normo-, hypo- and non-aerated pulmonary compartments. A retrospective analysis performed on a large cohort of mice confirmed the correlation between the proposed preclinical density thresholds and the histological outcomes (r_Spearman = 0.6, p < 0.01), strengthening their suitability for tracking disease progression and evaluating antifibrotic drug candidates.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive degenerative lung disease leading to respiratory failure and death. Although anti-fibrotic drugs are now available for treating IPF, their clinical efficacy is limited and lung transplantation remains the only modality to prolong survival of IPF patients. Despite its limitations, the bleomycin (BLM) animal model remains the best characterized experimental tool for studying disease pathogenesis and assessing efficacy of novel potential drugs. In the present study, the effects of oropharyngeal (OA) and intratracheal (IT) administration of BLM were compared in C57BL/6 mice. The development of lung fibrosis was followed in vivo for 28 days after BLM administration by micro-computed tomography and ex vivo by histological analyses (bronchoalveolar lavage, histology in the left lung to stage fibrosis severity and hydroxyproline determination in the right lung). In a separate study, the antifibrotic effect of Nintedanib was investigated after oral administration (60 mg/kg for two weeks) in the OA BLM model. Lung fibrosis severity and duration after BLM OA and IT administration was comparable. However, a more homogeneous distribution of fibrotic lesions among lung lobes was apparent after OA administration. Quantification of fibrosis by micro-CT based on % of poorly aerated tissue revealed that this readout correlated significantly with the standard histological methods in the OA model. These findings were further confirmed in a second study in the OA model, evaluating Nintedanib anti-fibrotic effects. Indeed, compared to the BLM group, Nintedanib inhibited significantly the increase in % of poorly aerated areas (26%) and reduced ex vivo histological lesions and hydroxyproline levels by 49 and 41%, respectively. This study indicated that micro-computed tomography is a valuable in vivo technology for lung fibrosis quantification, which will be very helpful in the future to better evaluate new anti-fibrotic drug candidates.

The systemic delivery of bleomycin (BLM) to mice through subcutaneously implanted osmotic minipumps may be used to experimentally mimic the typical features of systemic sclerosis and related interstitial lung diseases. The published studies on this model principally have focused on induced dermal modifications, probably because lung lesions are typically mild, subpleurally localized, and difficult to analyze. The use of high BLM doses to increase their severity has been proposed but is ethically questionable because of the compromising of animal welfare. We propose a tailored histomorphometric method suitable to detect and quantify this type of mild lung lesions. Using a two-step automated image analysis, a peripheral region of interest with a depth of 250 µm from the pleural edge was defined on whole slide images, and the fibrotic foci were histomorphometrically characterized. The effects of different BLM doses on lung alterations were evaluated in C57BL/6 mice and 60 U/kg resulted in a fair compromise between fibrotic lesions and animal welfare. This dose was also tested in time course experiments. The analysis revealed a peak of histological fibrotic-like alterations, cytokine expression, metalloprotease, and macrophagic activation between the 21st and 28th day after pump implant. The induced dermal fibrosis was characterized by the progressive loss of the white dermal adipose layer, an increase in dermal thickness, dermal hyperplasia, and more compacted collagen fibers. Despite the trend toward spontaneous resolution, our model allowed a double organ readout of the BLM effect and the identification of a therapeutic window for testing pharmacological compounds without using life-threatening doses.

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.

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings.