University of North Carolina at Chapel Hill, Department of Physics and Astronomy, Phillips Hall, Chapel Hill, North Carolina 27599-3255, USA.
J Biomed Opt. 2011 Jun;16(6):066010. doi: 10.1117/1.3590746.
The accurate and rapid assessment of tumor margins during breast cancer resection using optical coherence tomography (OCT) has the potential to reduce patient risk. However, it is difficult to subjectively distinguish cancer from normal fibroglandular stromal tissues in OCT images, and an objective measure is needed. In this initial study, we investigate the potential of a one-dimensional fractal box-counting method for cancer classification in OCT. We computed the fractal dimension, a measure of the self-similarity of an object, along the depth axis of 44 ultrahigh-resolution OCT images of human breast tissues obtained from 4 cancer patients. Correlative histology was employed to identify distinct regions of adipose, stroma, and cancer in the OCT images. We report that the fractal dimension of stroma is significantly higher than that of cancer (P < 10(-5), t-test). Furthermore, by adjusting the cutoff values of fractal dimension between cancer, stroma, and adipose tissues, sensitivities and specificities of either 82.4% and 88.9%, or 88.2% and 81.5%, are obtained, respectively, for cancer classification. The use of fractal analysis with OCT could potentially provide automated identification of tumor margins during breast-sparing surgery.
利用光学相干断层扫描（OCT）在乳腺癌切除术中准确快速地评估肿瘤边缘，有可能降低患者的风险。然而，在 OCT 图像中很难主观地区分癌症与正常纤维腺体基质组织，因此需要一种客观的测量方法。在这项初步研究中，我们研究了一维分形盒计数方法在 OCT 中用于癌症分类的潜力。我们沿着来自 4 名癌症患者的 44 张超高分辨率 OCT 图像的深度轴计算了分形维数，这是一种衡量物体自相似性的度量。相关组织学用于识别 OCT 图像中脂肪、基质和癌症的不同区域。我们报告说，基质的分形维数明显高于癌症（P < 0.001，t 检验）。此外，通过调整分形维数在癌症、基质和脂肪组织之间的截断值，可以分别获得 82.4%和 88.9%，或 88.2%和 81.5%的癌症分类的灵敏度和特异性。OCT 中使用分形分析可能有潜力提供在保乳手术中自动识别肿瘤边缘。
