University of Arizona, Biomedical Engineering, 1657 E. Helen Street, Building 240, P.O. Box 210240, Tucson, Arizona 85721, USA.
J Biomed Opt. 2012 Jul;17(7):076002. doi: 10.1117/1.JBO.17.7.076002.
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.
采用二次谐波（SHG）成像技术对离体小鼠卵巢进行成像，以检测伴随卵巢癌发展的胶原结构变化。用 4-乙烯环己烯二环氧和 7,12-二甲基苯并[a]蒽进行给药，导致组织学上确认了正常、良性异常、发育不良和癌病例。使用傅里叶变换矩阵和灰度共生矩阵（GLCM）计算每个 SHG 图像的参数。使用来自最高频率区域的功率和 GLCM 能量的参数，对癌症与正常和癌症与所有其他诊断的分离效果最佳。混合效应模型表明，这些参数在癌症与正常之间存在显著差异（P<0.008）。使用支持向量机对图像进行分类，使用 25%的数据进行训练，75%的数据进行测试。利用所有信号大于噪声水平的图像，将癌症与非癌症标本分类的敏感性为 81.2%，特异性为 80.0%，将癌症与正常标本分类的敏感性为 77.8%，特异性为 79.3%。仅利用包含信号的视场大于 75%的图像，将癌症与正常的敏感性和特异性提高到 81.5%和 81.1%。这些结果表明，使用 SHG 可视化卵巢中的胶原结构可能有助于早期癌症检测。
