Animals
Male beagle dogs aged 1 year and 10–11 months and weighing 9–12 kg were used in this study. This study was carried out in compliance with the ARRIVE guidelines (http://www.nc3rs.org.uk/page.asp?id=1357). All animals received care in compliance with the Principles of Laboratory Animal Care (National Society for Medical Research) and the Guide for the Care and Use of Laboratory Animals (National Institutes of Health Publication No. 86 − 23, revised 1996, Bethesda, MD). All surgical procedures were performed by Japanese board-certified thoracic surgeons in accordance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (NIH Publication No. 85 − 23, revised 1985). The experimental protocol was approved by the Animal Experimental Committee of Kyoto University.
Study Design
The animals were randomly assigned into one of the following three groups: (i) the Sham group, where only right thoracotomies were performed, (ii) the Bilobectomy group, where only resections of the right lower and cardiac lobes were performed, and (iii) the Pneumonectomy group, where only right pneumonectomies were performed (n = 5 in each group). The mean ages of the Sham, Bilobectomy, and Pneumonectomy groups at the time of surgery were 22.2 ± 0.4 months, 22.6 ± 0.5 months, and 22.0 ± 0 months, respectively. The mean body weights of each group were 10.4 ± 0.2 kg, 10.5 ± 1.3 kg, and 10.2 ± 1.0 kg, respectively. There was no difference between three groups in the ages or body weights.
Chest CT scans to all animals preceded surgical procedures in order to establish a baseline. CT scans were also taken at the postoperative time-points of 1, 3, and 6 months after surgery. Following 6 months of postoperative scanning, all animals were euthanized, and the lung-heart blocks were procured. Radiologic evaluations and histological findings at the postoperative month six were then compared to assess the validity of radiologic evaluations. (Fig. 5)
CT Scan
All examinations were performed using a multi-detector CT scanner (Aquilion 64; Canon Medical Systems, Tochigi, Japan) and the images were archived in Digital Imaging and Communications in Medicine format. Axial CT scans were taken with 1-mm thick slices.
Animals were fasted overnight, and they were anesthetized with a subcutaneous administration of 15 mg/kg ketamine hydrochloride, 7 mg/kg xylazine, and 0.5 mg/body atropine sulfate. Subsequently, they were intubated with an 8.5-mm cuffed endotracheal tube and were mechanically ventilated. A 500-mg dose of ampicillin was subcutaneously injected and general anesthesia was maintained with inhalational sevoflurane. CT scans were performed in the supine position, and a pressure-limited respirator was used to maintain the animals’ breath at transpulmonary pressure of 20 cm H2O (Evita 4; Drӓger, Lubeck, Germany).
Operation Procedure
After the preoperative CT scan, the right 5th intercostal thoracotomy was performed in the lateral decubitus position. In the Bilobectomy and Pneumonectomy groups, resection of right lower and cardiac lobes and of the right pneumonectomy were performed, respectively; pulmonary arteries and veins were dissected and ligated with 3–0 silk sutures and bronchus with No. 2 silk suture. Anterior mediastinal tissue was dissected to facilitate postoperative midline shift in all groups. The bony thorax was closed with No. 2 polyglactin stiches (Vicryl®, Ethicon, Inc., NJ, USA) and the muscles and skin were closed in layers with 3–0 polyglactin stiches. An additional 500-mg dose of ampicillin was injected subcutaneously at the end of surgery.
Radiologic Evaluation
CT images at transpulmonary pressure of 20 cm H2O were transferred to the AZE Virtual Place Lexus workstation (Canon Medical Systems, Tochigi, Japan). The workstation automatically executed the segmentation of the left lung and calculated the volume (ml) and the mean CT number (HU) of the extracted volume. The trachea and the next three generations of large conducting airways were excluded in the lung segmentation. Then, manual measurement of the CT number of tracheal air and skeletal muscle was performed to determine the estimated air and soft tissue CT number, respectively. The estimated air CT number was calculated by averaging the CT number of three separate regions of the tracheal lumen in each animal. The estimated air-free lung tissue CT number, i.e., estimated soft tissue CT number, was calculated by averaging the CT number of the infraspinatus, supraspinatus, and pectoralis muscles at the level of the carina.
1. Radiologic tissue volume
The fractional tissue volume and the radiologic tissue volume were calculated with the average CT numbers of the lung, lung volume, and estimated air density and estimated soft tissue density7,8,9,17,18. The fractional tissue volume, which represents a tissue density of lung tissue, was determined as follows:
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)
In contrast, the CT-derived radiologic tissue volume (ml), which represents the amount of lung tissue, was determined as follows:
Radiologic tissue volume = radiologic lung volume × fractional tissue volume
2. Radiologic lung weight
Radiologic lung weight was calculated with the modified protocol that was described in a previous report10, in which, the CT number of air of each scan was used for standardization. The average radiologic lung density (g/ml), which represents an estimated tissue density of lung tissue, was determined as follows:
![](data:image/jpeg;base64,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)
2. Radiologic lung weight
Radiologic lung weight was calculated with the modified protocol that was described in a previous report10, in which, the CT number of air of each scan was used for standardization. The average radiologic lung density (g/ml), which represents an estimated tissue density of lung tissue, was determined as follows:
![](data:image/jpeg;base64,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)
Consequently, the radiologic lung weight (g), which represents the amount of lung tissue, was determined as follows:
Radiologic lung weight = lung volume (mL) × average radiologic lung density (g/mL)
Histologic analysis
After six months of CT scanning, all animals were euthanized using an appropriate amount of anesthetic pentobarbital. The euthanized animals were then placed in the supine position to perform tracheostomy. When lungs collapsed following a small intercostal incision, we continued to instill 2.5% glutaraldehyde via airway at a 25 cm H2O pressure above the sternum in the thoracic cavity for one hour. After 1-hour of in vivo tissue fixing, the lung was extracted from the thoracic cavity and was subsequently immersed in glutaraldehyde for 24 hours.
The anatomical lung volume was then measured using the immersion method19, and it was defined as the average of the two saline volume displacement measurements.
Tissue blocks were sampled from five different parts from each left lung. The sampling parts were randomly selected and adopted for all lungs. The tissue blocks were then fixed again with formaldehyde and were stained with Hematoxylin and Eosin.
The histologic proportion of lung parenchyma in the lung was calculated using the ImageJ software20,21. In this process, we performed background subtraction to correct for uneven illuminated background, conversion to 8-bit image data and reconstruction in black and white images, and we finally measured the area of lung parenchyma (Supplemental material 2). A magnification 40x was used for evaluation. The histologic lung parenchymal amount was defined as follows:
![](data:image/jpeg;base64,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Statistical analysis
Statistical analyses were performed with EZR (Saitama Medical Center, Jichi Medical University), which is a graphical user interface for R (The R Foundation for Statistical Computing, version 2.13.0)22, and the acquired values were expressed as mean ± standard deviation. The Shapilo-Wilk test was used for testing normal distribution. All animal characteristics were analyzed using the Kruskal-Wallis test. All data were analyzed by using one-way ANOVA, and we then used the Tukey’s post-hoc test to compare the values between the three groups. Finally, the Pearson’s correlation coefficient was calculated in order to detect potential correlation between the two variables. P-values less than 0.05 were considered statistically significant.