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Updated: May 19, 2026
Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
Analysis of second-harmonic-generation microscopy in a mouse model of ovarian carcinoma
Jennifer M Watson1, Photini F Rice, Samuel L Marion
1University of Arizona, Biomedical Engineering, 1657 E. Helen Street, Building 240, P.O. Box 210240, Tucson, Arizona 85721, USA.
Abstract:
Second-harmonic-generation (SHG) imaging of mouse ovaries ex vivo was used to detect collagen structure changes accompanying ovarian cancer development. Dosing with 4-vinylcyclohexene diepoxide and 7,12-dimethylbenz[a]anthracene resulted in histologically confirmed cases of normal, benign abnormality, dysplasia, and carcinoma. Parameters for each SHG image were calculated using the Fourier transform matrix and gray-level co-occurrence matrix (GLCM). Cancer versus normal and cancer versus all other diagnoses showed the greatest separation using the parameters derived from power in the highest-frequency region and GLCM energy. Mixed effects models showed that these parameters were significantly different between cancer and normal (P<0.008). Images were classified with a support vector machine, using 25% of the data for training and 75% for testing. Utilizing all images with signal greater than the noise level, cancer versus not-cancer specimens were classified with 81.2% sensitivity and 80.0% specificity, and cancer versus normal specimens were classified with 77.8% sensitivity and 79.3% specificity. Utilizing only images with greater than of 75% of the field of view containing signal improved sensitivity and specificity for cancer versus normal to 81.5% and 81.1%. These results suggest that using SHG to visualize collagen structure in ovaries could help with early cancer detection.
Insights
Second-harmonic-generation (SHG) imaging detects collagen changes in ovaries, aiding early ovarian cancer detection. This method shows promise for distinguishing cancerous tissues from normal ones.
Area of Science:
- Biomedical Imaging
- Oncology
- Biophysics
Background:
- Ovarian cancer development involves significant changes in ovarian collagen structure.
- Early detection of ovarian cancer is crucial for improving patient outcomes.
- Non-invasive imaging techniques are needed to assess these structural changes.
Purpose of the Study:
- To investigate the utility of second-harmonic-generation (SHG) imaging for detecting collagen alterations in mouse ovaries during cancer development.
- To evaluate quantitative parameters derived from SHG images for differentiating normal, benign, dysplastic, and cancerous ovarian tissues.
- To assess the diagnostic performance of SHG imaging in classifying ovarian cancer specimens.
Main Methods:
- Ex vivo SHG imaging of mouse ovaries induced with carcinogens.
- Analysis of SHG images using Fourier transform and gray-level co-occurrence matrix (GLCM) parameters.
- Statistical analysis using mixed-effects models and classification using support vector machines (SVM).
Main Results:
- Specific SHG parameters, particularly those related to high-frequency power and GLCM energy, showed significant differences between cancerous and normal ovarian tissues (P<0.008).
- SVM classification achieved 81.2% sensitivity and 80.0% specificity for cancer versus non-cancer detection.
- Improved classification accuracy for cancer versus normal tissues (81.5% sensitivity, 81.1% specificity) was observed when using images with higher signal quality.
Conclusions:
- SHG imaging effectively visualizes collagen structure changes associated with ovarian cancer.
- Quantitative analysis of SHG images provides valuable biomarkers for ovarian cancer detection.
- SHG imaging holds potential as a tool for early and accurate diagnosis of ovarian cancer.
