Abstract 733: Infrared spectral imaging: a new automated diagnostic tool for colon cancer.
Authors: Jayakrupakar Nallala, Marie-Danielle Diebold, Cyril Gobinet, Olivier Bouché, Ganesh-Dhruvananda Sockalingum, Olivier Piot, Michel Manfait
Journal: Cancer Research (2013)

Abstract Purpose: Novel methods are the need of the hour that could complement the ‘gold standard’ histopathology for cancer diagnosis. In this perspective, biophotonic approach of infrared (IR) spectral micro-imaging is one of the candidates, as it provides spectral fingerprint of cell and tissue biochemistry in a non-destructive and label-free manner. This ability has been exploited: 1) to develop a new concept of spectral bar-coding for rapid characterization of biochemical alterations between normal and tumoral epithelial components of colonic tissues, and 2) to identify spectral signatures for colon histology in order to develop a prediction model comprising potential diagnostic markers for rapid and automated colorectal cancer diagnosis. Experimental procedure: Ten frozen colon tissue samples (five tumoral and non-tumoral pairs from five patients), and sixty-eight colonic samples (39 tumoral and 29 non-tumoral) from 32 patients in the form of paraffinized tissue arrays were imaged using IR spectral micro-imaging in a non-destructive manner. In case of paraffinized tissues, in order to avoid chemical deparaffinization, a mathematical deparaffinization based on extended multiplicative signal correction (EMSC) was implemented to neutralize the spectral interferences from paraffin. The spectral images were processed by a multivariate clustering method to identify the histological organization in a label-free manner. Summary: In the first part, the spectral information from the epithelial components of the frozen tissues was automatically recovered on the basis of the intrinsic biochemical composition, and compared using a statistical method (Mann-Whitney U test) to construct spectral barcodes specific to each patient. In the case of paraffinized tissue arrays, an LDA based robust prediction model (comprising 86802 spectra, constructed from 9 samples, and tested on 59 unknown samples involving a huge bank of 3620287 spectra) showed 100 % sensitivity for malignancy, while 10 out of 29 non-tumoral samples were identified as having tumor pixels. Further tests are under way to analyze these false positive samples as they were either present in the peri-tumoral regions, or appear having an inflammatory signature. Important features difficult to discern by conventional histopathology like tumor budding, tumor-stroma association, and inflammation, were easily identified by this methodology. Conclusion: The discriminant infrared spectral wavenumbers enabled characterization of some of the malignancy associated biochemical alterations associated with mucin, nucleotides, carbohydrates and protein regions. This study constituting a label-free and non-destructive approach demonstrates the potential of IR spectral micro-imaging, combined with multivariate statistical image analysis, as a complementary tool to conventional histopathology for an automated and objective cancer diagnosis. Citation Format: Jayakrupakar Nallala, Marie-Danielle Diebold, Cyril Gobinet, Olivier Bouché, Ganesh-Dhruvananda Sockalingum, Olivier Piot, Michel Manfait. Infrared spectral imaging: a new automated diagnostic tool for colon cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 733. doi:10.1158/1538-7445.AM2013-733 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.