Autofluorescence of Normal, Benign, and Malignant Ovarian Tissues: A Pilot Study

被引:27
作者
Kamath, Sudha D. [1 ]
Bhat, Rani A. [2 ]
Ray, Satadru [3 ]
Mahato, K. K. [1 ]
机构
[1] Manipal Univ, Manipal Life Sci Ctr, Div Laser Spect, Manipal 576104, Karnataka, India
[2] Manipal Univ, Kasturba Med Coll, Dept Obstet & Gynecol, Manipal 576104, Karnataka, India
[3] Manipal Univ, Kasturba Med Coll, Dept Surg Oncol, Manipal 576104, Karnataka, India
关键词
MULTIVARIATE STATISTICAL ALGORITHM; INDUCED FLUORESCENCE SPECTROSCOPY; IN-VIVO CHARACTERIZATION; ORAL-TISSUE; CLASSIFICATION; SPECTRA; CANCER; DIAGNOSIS; MUCOSA;
D O I
10.1089/pho.2008.2261
中图分类号
R61 [外科手术学];
学科分类号
摘要
Objective: The objective of this study is to evaluate the efficacy of laser-induced fluorescence (LIF) data obtained at 325-nm pulsed laser excitation for the discrimination of normal, benign, and malignant ovarian tissues. Background Data: Several studies have reported that the autofluorescence technique has a high specificity and sensitivity for discrimination between diseased and non-diseased tissues of various cancers, and also has the advantages of being non-invasive and producing a real-time diagnosis. When using this technique on ovarian tissues in most of the previously reported studies, multivariate statistical tools were used and classification analyses were carried out. Materials and Methods: Autofluorescence spectra of normal, benign, and malignant ovarian tissues were recorded with 325-nm pulsed laser excitation in the spectral region from 350-600 nm in vitro. The spectral analysis for discrimination between the different types of tissues was carried out using principal component analysis (PCA)-based non-parametric k-nearest neighbor (k-NN) analysis. Results: A total of 97 (34 normal, 33 benign, and 30 malignant) spectra were obtained from 22 subjects with normal, benign, and malignant tissues. The discrimination analysis of data using a PCA-based k-NN algorithm showed very good discrimination. The performance of the analysis was evaluated by calculating statistical parameters, specificity, sensitivity, and accuracy and were found to be 100%, 90.90%, and 94.2%, respectively. Conclusion: The results show that the discrimination of normal, benign, and malignant ovarian conditions can be achieved quite successfully using LIF.
引用
收藏
页码:325 / 335
页数:11
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