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.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging