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|Title:||Near-infrared raman spectroscopy with recursive partitioning techniques for precancer and cancer detection||Authors:||TEH SENG KHOON||Keywords:||Raman Spectroscopy, Recursive partitioning techniques, precancer, cancer, CART, random forests||Issue Date:||20-Aug-2009||Citation:||TEH SENG KHOON (2009-08-20). Near-infrared raman spectroscopy with recursive partitioning techniques for precancer and cancer detection. ScholarBank@NUS Repository.||Abstract:||Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with disease transformation. To effectively translate molecular differences captured in Raman spectra between different tissue types into clinically valuable diagnostic information for clinicians, chemometrics would need to be deployed for developing effective diagnostic algorithms for Raman spectroscopic diagnosis of precancer and cancers. However, most of the chemometrices (principal component analysis (PCA)) applied for Raman tissue diagnosis cannot adequately provide the physical meanings of component spectra for tissue classification
This dissertation presents the investigation on the diagnostic utility of near infrared (NIR) Raman spectroscopy with recursive partitioning techniques such as classification and regression trees (CART), and random forests to construct clinically interpretable diagnostic algorithm for tissue Raman classification.
A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 146 tissue samples obtained from 70 patients who underwent endoscopy investigation or surgical operation were used in this study. The histopathogical examinations showed that 94 were gastric tissues (55 normal, 21 dysplastic, and 18 cancerous), and 50 were laryngeal tissues (20 normal, and 30 cancerous).
CART was explored to be used together with NIR Raman spectroscopy for gastric cancer diagnosis. CART achieved a predictive sensitivity and specificity of 88.9% and 92.9%, respectively, for separating cancer from normal. In addition, CART also determined tissue Raman peaks at 875 and 1745 cm-1 to be two of the most significant features in the entire Raman spectral range to discriminate gastric cancer from normal tissue. This affirmed the utility of CART to be used for NIR Raman spectroscopy detection of cancer tissues.
To improve diagnostic performance (e.g., stability) of CART, the random ensemble approach (i.e., random forests) was further utilized. Random forests yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification, and also provided variables importance plot that facilitates correlation of significant Raman spectral features with cancer transformation. These confirmed the diagnostic potential of random forests with NIR Raman spectroscopy for detection of malignancy occurring in the internal organs (i.e., larynx).
Comprehensive evaluation of the performance of the empirical approach that utilizes Raman peak intensity ratio, PCA-linear discriminant analysis (LDA), and random forests algorithm was also carried out. Raman peak intensity ratios representing biomolecular signals for collagen, proteins and lipids achieved diagnostic accuracy of approximately 88% for NIR Raman spectroscopic detection of gastric dysplasia from the normal gastric tissues. Further investigation on the use of PCA-LDA achieved obtained a diagnostic accuracy of 93%, while random forests achieved diagnostic accuracy of 90% for gastric dysplasia detection. Receiver operating characteristics (ROC) curves further confirmed that PCA-LDA and random forests techniques have comparable overall diagnostic accuracy rate which are more superior compared to the empirical approach.
Overall, this dissertation demonstrates that NIR Raman spectroscopy in conjunction with powerful chemometric techniques such as random forests have the potential to generate interpretable clinical Raman information, and to yield high diagnostic accuracy classification results for the rapid diagnosis and detection of precancer and cancer tissues.
|Appears in Collections:||Master's Theses (Open)|
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checked on Apr 12, 2019
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