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Automated Tool for Determining Pulmonary Nodule Elasticity to Distinguish Malignant Nodules

M Negahdar

M Negahdar*, B Loo, M Diehn, L Tian, D Fleischmann, P Maxim, Stanford University, Palo Alto, CA

WE-C-103-8 Wednesday 10:30AM - 12:30PM Room: 103

To develop and validate an automated method of determining pulmonary nodule (PN) elasticity against a manual contouring method, and preliminarily assess its ability to distinguish malignant tissue by comparing the elasticities of malignant PNs treated with stereotactic ablative radiotherapy (SABR) with those of the lung.

We analyzed breath-hold images of 30 patients with malignant PNs who underwent SABR in our department. A parametric nonrigid transformation model based on multi-level B-spline guided by Sum of Squared Differences similarity metric was applied on breath-hold images to determine the deformation map. The Jacobian of the calculated deformation map, which is directly related to the volume changes between the two respiratory phases, was calculated. Next, elasticity parameter will be derived by calculating the ratio of the Jacobian of the PN to the Jacobian of a 1cm region of lung tissue surrounding the tumor (E-ROI) as well as the Jacobian of the whole lung (E-Lung).

For the first group of 15 patients we evaluated the volumetric changes of PNs and the lung from the maximum exhale phase to the maximum inhale phase, whereas the reverse was done for the second group of 15 patients. For the first group, mean and standard deviation for E-ROI and E-Lung were 0.91±0.09 and 0.86±0.18, respectively, which was verified by the manual method. For the second group, E-ROI and E-Lung were 1.34±0.27 and 1.57±0.51, respectively. These results demonstrate that the elasticity of the PNs was less than that of the surrounding lung (p<0.0037).

We developed an automated tool to determine the elasticity of PNs based on deformable image registration of breath-hold images. The tool was validated against manual contouring. Preliminarily, PN elastometry distinguishes proven malignant PNs from normal tissue of lung, suggesting its potential utility as a non-invasive diagnostic tool to differentiate malignant from benign PN.

Funding Support, Disclosures, and Conflict of Interest: This Study is suuported by DoD LCRP 2011, Grant# W81XWH-12-1-0286

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