Chan Alan, Tuszynski Jack A
Department of Mathematical and Statistical Sciences , University of Alberta, Central Academic Building , Edmonton, Alberta, Canada T6G 2G1.
Department of Oncology, University of Alberta, 11560 University Avenue, Edmonton, Alberta, Canada T6G 1Z2; Department of Physics, University of Alberta, Centennial Centre for Interdisciplinary Science, Edmonton, Alberta, Canada T6G 2E1.
R Soc Open Sci. 2016 Dec 7;3(12):160558. doi: 10.1098/rsos.160558. eCollection 2016 Dec.
Breast cancer is one of the most prevalent types of cancer today in women. The main avenue of diagnosis is through manual examination of histopathology tissue slides. Such a process is often subjective and error-ridden, suffering from both inter- and intraobserver variability. Our objective is to develop an automatic algorithm for analysing histopathology slides free of human subjectivity. Here, we calculate the fractal dimension of images of numerous breast cancer slides, at magnifications of 40×, 100×, 200× and 400×. Using machine learning, specifically, the support vector machine (SVM) method, the F1 score for classification accuracy of the 40× slides was found to be 0.979. Multiclass classification on the 40× slides yielded an accuracy of 0.556. A reduction of the size and scope of the SVM training set gave an average F1 score of 0.964. Taken together, these results show great promise in the use of fractal dimension to predict tumour malignancy.
乳腺癌是当今女性中最常见的癌症类型之一。主要的诊断途径是通过对组织病理学组织切片进行人工检查。这样的过程往往主观且容易出错，存在观察者间和观察者内的变异性。我们的目标是开发一种自动算法，用于分析组织病理学切片，避免人为主观性。在此，我们计算了众多乳腺癌切片在40倍、100倍、200倍和400倍放大倍数下的图像分形维数。使用机器学习，具体而言是支持向量机（SVM）方法，发现40倍切片分类准确率的F1分数为0.979。对40倍切片进行多类分类的准确率为0.556。减少SVM训练集的大小和范围后，平均F1分数为0.964。综上所述，这些结果表明在使用分形维数预测肿瘤恶性程度方面具有很大的前景。
