Purpose: Colorectal cancers are the third most common type and second leading cause of cancer-related deaths in Western world. Histopathology is the gold standard method for diagnosis of colon cancers. To improve cancer diagnosis, ensure an effective treatment and better efficacy of treatment modalities, it is important to develop effective diagnostic method more reliable in terms of biochemical changes that can be detected in a cancerous tissue. To this end, we have developed spectral histopathology based on infrared (IR) imaging. The aims of this study are therefore to: (a) examine, using FTIR spectroscopy, the molecular changes between normal and tumoral colon tissues, (b) exploit the potentials of IR imaging to identify new diagnostic markers, and (c) develop an algorithm which could be applied in routine as a diagnostic tool directly and automatically on new unknown samples.

Experimental procedure: IR imaging is a promising technique which has the potential to reveal intrinsic bio-molecular information in a tissue by probing vibrational motions of chemical bonds, thus giving a fingerprint of the composition and the structures. An IR imaging system (Spotlight 300, Perkin Elmer, Les Ulys, France) equipped with nitrogen-cooled 16-element MCT detector was calibrated to acquire images at 6.25 µm spatial and 4 cm−1 spectral resolutions averaged to 16 accumulations from 10µm thick sections of colon tissues placed on a calcium fluoride IR transparent window. These sections were embedded in paraffin block and stabilized in agarose matrix of a tissue micro array (TMA) slide consisting of 13 spots, each 3 mm in diameter. Adjacent 10 µm sections from the same TMA block were H&E stained for histological analysis. A modified Extended Multiplicative Signal Correction (EMSC) algorithm was applied to the IR images of colon tissues to neutralize spectral variability from paraffin and agarose in order to conserve spectral variability originating only from the biological tissue. For that, spectra collected from paraffin and agarose with same parameters were modelled by their first ten principal components.

Summary: Image analysis using K-means clustering on comparison with H&E stained images revealed diverse spectral features representing the biochemical make up of the tissues. Selected cluster images from normal tissue were then used as reference. To detect the presence of abnormal chemical features (cancerous) in new unknown tissues, we developed an innovative algorithm permitting to construct automatically high-contrast color-coded images based on the correlation coefficients between tissue spectral signatures.

Conclusion: IR imaging allowed differentiating and detecting underlying differences between normal and tumoral colon tissues supporting the capability of exploiting this innovative spectral histopathological method in colon cancer diagnosis.

Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2741.

©2010 American Association for Cancer Research
2010