UC Office of the President Recent Work Title Dynamic CT imaging of volumetric changes in pulmonary nodules correlates with physical measurements of stiffnes Permalink

Background and purpose: A major challenge in CT screening for lung cancer is limited specificity when distinguishing between malignant and non-malignant pulmonary nodules (PN). Malignant nodules have different mechanical properties and tissue characteristics (‘stiffness’) from non-malignant nodules. This study seeks to improve CT specificity by demonstrating in rats that measurements of volumetric ratios in PNs with varying composition can be determined by respiratory-gated dynamic CT imaging and that these ratios correlate with direct physical measurements of PN stiffness. Methods and materials: Respiratory-gated MicroCT images acquired at extreme tidal volumes of 9 rats with PNs from talc, matrigel and A549 human lung carcinoma were analyzed and their volumetric ratios (d) derived. PN stiffness was determined by measuring the Young’s modulus using atomic force microscopy (AFM) for each nodule excised immediately after MicroCT imaging. Results: There was significant correlation (p = 0.0002) between PN volumetric ratios determined by respiratory-gated CT imaging and the physical stiffness of the PNs determined from AFM measurements. Conclusion: We demonstrated proof of concept that PN volume changes measured non-invasively correlate with direct physical measurements of stiffness. These results may translate clinically into a means of improving the specificity of CT screening for lung cancer and/or improving individual prognostic assessments based on lung tumor stiffness. ! 2016 Elsevier Ireland Ltd. All rights reserved. Radiotherapy and Oncology 122 (2017) 313–318 Although advances in surgery, radiation therapy, and systemic therapies in lung cancer have improved survival, clinical stage at diagnosis remains the major determinant of survival after therapy [1]. Screening for lung cancer with low-dose computed tomography (CT) is a highly sensitive, non-invasive imaging modality that has demonstrated mortality reduction through early detection of the disease [2]. CT imaging has also been used to delineate lung lesions by deformable image registration for more accurate radiation treatment planning and therapy delivery [3–5]. However, the key challenge for CT as a screening tool is limited specificity to distinguish between malignant and non-malignant pulmonary nodules (PN) leading to a high false positive rate. Imaging, particularly ultrasound, has improved the ability to differentiate between malignant and benign tumors based on mechanical properties in breast carcinoma [6,7], metastatic melanoma [8,9], head and neck carcinoma [10], and colorectal carcinoma [6]. Differences in trajectory distortion can detect the subtle changes in mechanical properties between malignant and non-malignant tumors. Ultrasound imaging, however, is technically challenging for lung tissue, due to the opacity of air in the lungs. In addition, MRI-based imaging techniques that assess tissue stiffness in hepatic fibrosis [11,12] are also susceptible to artifacts from air in the lung. We previously developed a non-invasive CT-based imaging methodology that differentiated the volumetric changes in malignant PN from those of the surrounding lung tissue [13,14]. High resolution dynamic CT imaging, in contrast to static imaging, captures temporal changes in lung and PN volumes during the respiratory cycle. We hypothesize that dynamic CT images acquired at http://dx.doi.org/10.1016/j.radonc.2016.11.019 0167-8140/! 2016 Elsevier Ireland Ltd. All rights reserved. ⇑ Corresponding authors at: Stanford Cancer Center, 875 Blake Wilbur Dr., Stanford, CA 94305, United States. E-mail addresses: bwloo@stanford.edu (B.W. Loo), pmaxim@stanford.edu (P.G. Maxim). 1 Co-first authors. Radiotherapy and Oncology 122 (2017) 313–318