University of Illinois, Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering, Quantitative Light Imaging Laboratory, Urbana-Champaign, Illinois, United States.
University of Illinois, Department of Pathology, Chicago, Illinois, United States.
J Biomed Opt. 2017 Mar 1;22(3):36015. doi: 10.1117/1.JBO.22.3.036015.
We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.
我们提出了一种用于组织活检自动诊断的方法。我们的方法学包括定量相位成像组织扫描仪和机器学习算法，用于处理这些数据。我们通过自动对前列腺标本进行 Gleason 分级来说明该方法的性能。该成像系统基于干涉测量原理运行，因此可以报告未经标记的样本的纳米级结构。我们使用这些数据来训练随机森林分类器，以学习前列腺样本的纹理行为，并将图像中的每个像素分类到不同的类别中。自动诊断结果是从分割区域计算得出的。通过将形态特征与腺体和基质的定量信息相结合，逻辑回归用于区分前列腺切除术组织中 Gleason 分级 3 与 4 级癌症的区域。从接收器工作曲线得出的这种分类的总体准确性为 82%，考虑到观察者间变异性时，这处于人类误差的范围内。我们预计，我们的方法将为 Gleason 分级提供一种临床客观和定量的指标，使我们能够在仪器和实验室之间验证结果，并为提高准确性提供计算机算法。
