Tumor volume doubling time and growth index of tumors detected in RNR transgenic mice by micro-CT.

<p>NOTE: mice were first scanned at the indicated age and then subjected to a series of sequential scans to monitor tumor growth. The slope of the growth curve was converted to tumor doubling time and growth index to indicate the rate of tumor growth.</p

Comparison of soft tissue and lung parenchyma densities in a micro-CT scan and a human whole lung CT scan.

<p>Distribution of densities in the lung parenchyma (white) and soft tissue (gray) in (A) a mouse micro-CT scan with adaptive threshold of −190 HU and (B) a human whole lung CT scan with no need for adaptive threshold. The mouse micro-CT scan was obtained at 50 µm with 720 projections. The human whole lung CT scan was from the Weill Cornell Medical College Lung CT database. It was obtained using a GE LightSpeed Ultra scanner at 120 kVp and 80 mA, with 0.7×0.7×1.25 mm³ resolution. The peaks in (A) were not as sharp as those in (B), indicating that the mouse micro-CT scans were noisier than human CT scans. Magnified regions of the lung from (C) a micro-CT scan (yellow circle indicates tumor) and (D) a whole-lung CT scan (red arrow points to tumor) are shown to visualize the difference in scan quality. No scaling was done to the images and each image was windowed for viewing. The scale bars represent 5.0 mm (mouse micro-CT image) or 70.3 mm (human CT image). The color bar range is −750 to 849 HU (mouse micro-CT image) or −1400 to 100 HU (human CT image).</p

Micro-CT and histological analyses of an RNR transgenic mouse lung tumor.

<p>(A) Micro-CT image of lung (sagittal view) from an RNR transgenic mouse with a tumor (red circle generated manually). Image was derived from a scan taken at 50 µm with 220 projections. The scale bar represents 5.0 mm. (B) H&E stained lung tissue from the same mouse. The scale bar represents 1000 µm. Normal and tumor tissues are also shown at a higher magnification. The scale bar represents 40 µm. The tumor diameter was measured to be 1.94 mm by histological analysis. (C) Black and white panels: Several slices (every 4th slice shown) through a small region of interest including the tumor in (A). The scale bar represents 2.5 mm. Color panels: The same tumor separated by the semi-automated segmentation algorithm from other soft tissue structures such as blood vessels and the chest wall. The result is shown with the tumor in red and other soft tissue structures in green. The color bar range is −725 to 625 HU. (D) A 3D visualization of the segmented tumor in (C) showing axial, sagittal, and dorsal views. The volume equivalent diameter of the tumor was calculated by the semi-automated algorithm to be 2.03 mm.</p

Sequential micro-CT scans over time to measure lung tumor growth rate in four RNR transgenic mice.

<p>(A) Images of sequential micro-CT scans of an RNR transgenic mouse (mouse #3 tumor A; red circle generated manually). Images were acquired at 50 µm with 720 projections. The scale bars represent 5.0 mm. The color bar range is −700 to 400 HU. (B) Gross image of the lungs at necropsy showing the tumor (black arrow) after the last scan. (C) H&E stained section from lungs shown in (B). The scale bar represents 1000 µm. Normal and tumor tissues are also shown at a higher magnification. The scale bar represents 40 µm. (D, E) Growth curves of lung tumors from four RNR transgenic mice. Fold change in lung tumor volume was plotted against time from the first micro-CT scan. A best-fit exponential curve was used to model the growth of each tumor. Note that Mouse 1 showed very slow growth, which could be due to inconsistency in tumor volume measurement because different scan parameters were used for mouse 1 time point 3 and this was the first live mouse scanned, when the micro-CT instrument was not calibrated for each scan.</p

Flowchart showing the major steps of the semi-automated segmentation algorithm.

<p>Flowchart showing the major steps of the semi-automated segmentation algorithm.</p

Comparison of lung tumor growth measured manually by an observer and by the semi-automated algorithm.

<p>Best linear fit growth curves were plotted for tumors from mouse 2 (left) and mouse 4 tumor A (right) based on measurements by a manual approximation method and by the semi-automated algorithm. The slopes of the best-fit lines for the manual and semi-automated measurements were compared by Student's t-test, and no significant differences were observed between the two slopes (P = 0.62 for mouse 2 and P = 0.57 for mouse 4 tumor A).</p

Phantoms and tissues show variation in densities across different scans.

<p>(A) Distribution of densities of three phantoms, air, water, and bone (left to right peaks), with mean densities −927.6 HU, 92.2 HU, and 2612.6 HU, respectively, in one scan. (B) Distribution of densities of the same phantoms as in (A) in a repeated scan six weeks later with mean densities −931.8 HU, 66.5 HU, and 2592.0 HU, respectively. (C) Distribution of densities in the lung parenchyma (white) and soft tissue (gray) from one mouse in one scan with an adaptive threshold at −155 HU. (D) Distribution of densities of the same tissues as in (C) of the same mouse in a repeated scan six weeks later with an adaptive threshold at −190 HU. All scans were acquired at 50 µm with 720 projections. Variations in the density distribution of the phantoms and tissues were observed in repeated scans.</p