Abstract 4055: Label-free diagnosis of lung cancer subtypes with three-dimensional molecular vibrational imaging

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL For over 50 years, lung cancer has been viewed as an overwhelmingly fatal disease for which radiologic screening was of unproven utility and the pathologist had a limited role differentiating small cell from non-small cell carcinoma and staging resection specimens. The 21st century has given birth to a revolution in the treatment and classification of lung cancer that promises to radically enhance survival of patients. Particularly, the advent of molecular targeted therapies makes identification of the various histologic subtypes, especially adenocarcinoma and squamous cell carcinoma, more important. However, the majorities of lung carcinomas are not resected and are diagnosed using small biopsies or cytology specimens. Such a small biopsied tissue sample is likely to be restricted to only one or two histological tests and could take days because of the time taken for tissue processing, sectioning and staining. The ability to rapidly recognize lung cancer subtypes, with minimal tissue consumption, will thus not only facilitate the diagnostic process, but also enable maximum preservation of tissue samples for subsequent molecular testing for targeted therapy. Hereby, we focus on developing a label-free molecular imaging platform that enables diagnostic imaging of non-small cell lung carcinomas. Mouse lung cancer models were developed by injecting human lung cancer cell lines, including adenocarcinoma (A549) and squamous cell carcinoma (NCI-H226), into the lungs of nude mice (15 mice in each group). Coherent anti-Stokes Raman scattering (CARS) microscopy was used to acquire lung tissue images using intrinsic molecular contrast from symmetric CH2 bonds, thus avoiding tissue consumption or staining. CARS images showed cell nuclei as dark roughly ellipsoidal structures surrounded by brighter extracellular tissues richer in CH2 structures, and were in good correlation with H&E results. By stacking multiple image slices from one z-stack (multiple images acquired at the same field of view but different imaging depths) into a single data structure, 3-D volumes were reconstructed. A computer algorithm was subsequently developed to perform 3-D nuclear segmentation and measurements on these volumes of such disease-related features like nuclear volume, cell-cell distance, etc. The algorithm was developed using superpixel context with artificial neural networks. The measured features were used to develop a classification system for separation of adenocarcinoma from squamous cell carcinoma. Our results showed greater than 97% accuracy and specificity. Therefore, this study shows that the developed 3-D label-free molecular diagnostic platform can accurately delineate cellular structures for classification of non-small cell carcinomas, thus holding substantial potential to provide fast diagnosis while effectively preserving specimens for followed molecular tests. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4055. doi:1538-7445.AM2012-4055