Being among the most common cancers worldwide screening and early diagnosis of colorectal cancer is of high interest for the health system, the patients and for research. Raman microspectroscopy as a label-free, non-invasive and non-destructive technique is a promising tool for an early diagnosis. However, to ensure a reliable diagnosis specially designed statistical analysis workflows are required. Several statistical approaches have been introduced leading to varying results in the overall accuracy, sensitivity and specificity. In this study a systematic evaluation of different statistical analysis approaches has been performed using a colon cancer mouse model with genotypic identical individuals. Based on the inter-individual Raman spectral variances a measure for the biological variance can be estimated. By applying a leave-one-individual-out cross-validation a clinically relevant discrimination of healthy tissue versus adenoma and carcinoma with an accuracy of 95% is shown. Furthermore, the transfer of a model from tissue to biopsy specimen is demonstrated.
Keywords: Raman microspectroscopy; cancer diagnosis; chemometrics.
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