Abstract | PURPOSE: MATERIALS AND METHODS: Raman spectra are detected from PC cell lines (LNCaP and C4-2) and tissues using a Labram HR 800 RS. Then, principal component analysis (PCA) and support vector machine (SVM) are applied for prediction. A leave-one-out cross-validation is used to train and test the SVM. RESULTS: There are 50 qualified patients, including 33 with androgen-dependent prostate cancer (ADPC) and 17 with CRPC. The spectral changes at 1126, 1170, 1315 to 1338, and 1447 cm-1 between CRPC and ADPC are detected in both cells and tissues models, which are assigned to specific amino acids and DNA. PCA/SVM algorithm provided a sensitivity of 88.2% and a specificity of 87.9% for diagnosing CRPC tissues. Furthermore, 14 patients with ADPC progressed to CRPC within 12 months. These patients are separated into two groups depending on whether their cancers progressed to CRPC within 12 months. PCA/SVM could differentiate these two groups with a sensitivity of 85.7% and a specificity of 88.9%. CONCLUSIONS: RS has the potential in diagnosis and prognosis of CRPC in clinical practice.
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Authors | Lei Wang, Dalin He, Jin Zeng, Zhenfeng Guan, Qiang Dang, Xinyang Wang, Jun Wang, Liqing Huang, Peilong Cao, Guanjun Zhang, JerTong Hsieh, Jinhai Fan |
Journal | Journal of biomedical optics
(J Biomed Opt)
Vol. 18
Issue 8
Pg. 87001
(Aug 2013)
ISSN: 1560-2281 [Electronic] United States |
PMID | 23907278
(Publication Type: Evaluation Study, Journal Article, Research Support, Non-U.S. Gov't)
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Topics |
- Algorithms
- Cell Line, Tumor
- Data Interpretation, Statistical
- Diagnosis, Computer-Assisted
(methods)
- Feasibility Studies
- Humans
- Male
- Principal Component Analysis
- Prognosis
- Prostatic Neoplasms, Castration-Resistant
(diagnosis)
- Reproducibility of Results
- Sensitivity and Specificity
- Spectrum Analysis, Raman
(methods)
- Support Vector Machine
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