Please use this identifier to cite or link to this item: https://doi.org/10.1039/b811008e
Title: Spectroscopic diagnosis of laryngeal carcinoma using near-infrared Raman spectroscopy and random recursive partitioning ensemble techniques
Authors: Teh, S.K. 
Zheng, W. 
Lau, D.P.
Huang, Z. 
Issue Date: 2009
Source: Teh, S.K., Zheng, W., Lau, D.P., Huang, Z. (2009). Spectroscopic diagnosis of laryngeal carcinoma using near-infrared Raman spectroscopy and random recursive partitioning ensemble techniques. Analyst 134 (6) : 1232-1239. ScholarBank@NUS Repository. https://doi.org/10.1039/b811008e
Abstract: In this work, we evaluated the diagnostic ability of near-infrared (NIR) Raman spectroscopy associated with the ensemble recursive partitioning algorithm based on random forests for identifying cancer from normal tissue in the larynx. A rapid-acquisition NIR Raman system was utilized for tissue Raman measurements at 785 nm excitation, and 50 human laryngeal tissue specimens (20 normal; 30 malignant tumors) were used for NIR Raman studies. The random forests method was introduced to develop effective diagnostic algorithms for classification of Raman spectra of different laryngeal tissues. High-quality Raman spectra in the range of 800-1800 cm-1 can be acquired from laryngeal tissue within 5 seconds. Raman spectra differed significantly between normal and malignant laryngeal tissues. Classification results obtained from the random forests algorithm on tissue Raman spectra yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification. The random forests technique also provided variables importance that facilitates correlation of significant Raman spectral features with cancer transformation. This study shows that NIR Raman spectroscopy in conjunction with random forests algorithm has a great potential for the rapid diagnosis and detection of malignant tumors in the larynx. © 2009 Royal Society of Chemistry.
Source Title: Analyst
URI: http://scholarbank.nus.edu.sg/handle/10635/88136
ISSN: 00032654
DOI: 10.1039/b811008e
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