Systematic evaluation of the biological variance within the Raman based colorectal tissue diagnostics. Issue 5 (20th December 2015)

• Record Type: Journal Article
• Authors: Vogler, Nadine; Bocklitz, Thomas; Subhi Salah, Firas; Schmidt, Carsten; Bräuer, Rolf; Cui, Tiantian; Mireskandari, Masoud; Greten, Florian R.; Schmitt, Michael; Stallmach, Andreas; Petersen, Iver; Popp, Jürgen
• Other Names: Koprowski Robert guestEditor.; Bocklitz Thomas guestEditor.
• Is Part Of: Journal of biophotonics. Volume 9:Issue 5(2016)
• Page Start: 533
• Page End: 541
• Publication Date: 2015-12-20
• Subjects: Raman microspectroscopy -- cancer diagnosis -- chemometrics; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201500237
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract : 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. Abstract : Raman microspectroscopy is combined with a specifically designed statistical analysis workflow to establish a reliable diagnosis for colon cancer. A systematic evaluation of different statistical analysis approaches while using a colon cancer mouse model with genotypic identical individuals yields a measure for the biological variance and a clinically relevant discrimination of healthy versus tumorous tissue with an accuracy of 95%.