Classification of contrast-enhanced ultrasonograms in rectal cancer according to tumor inhomogeneity using machine learning-based texture analysis.

Background: Inhomogeneity within tumors can reflect tumor angiogenesis. Existing research into the quantization of angiogenesis mainly focuses on time-intensity curve parameters but has produced inconsistent results. In clinical work, it is difficult to achieve standardization and consistency for manual judgement of the inhomogeneity of contrast-enhanced images, while the artificial intelligence technology may be helpful. The aim of this study was to assess whether computers can assist in the artificial classification of tumor inhomogeneity in contrast-enhanced ultrasound (CEUS) images of rectal cancer. Methods: A total of 500 contrast-enhanced ultrasonograms were retrospectively collected, which was verified of rectal cancer pathologically from 2016 to 2018 as training set. All images are from 18-80 years old patients with rectal cancer in our hospital. These tumors are usually located in the middle and lower segment of the rectum, which can be completely observed on ultrasound. The images were divided into 3 categories according to the inhomogeneous distribution of contrast agents inside the tumors. Computing methods were used to simulate manual classification. Computer processing steps included segmentation, gray level quantization, dimension reduction, and classification. The results of 6 different gray level quantization, 2 dimensionality reduction methods, and 3 classifiers were compared, from which the optimal parameters were selected in each step. The performance of computer classification was evaluated using manual classification results as the reference. Ninety-seven ultrasonograms of contrast-enhanced rectal tumors were collected as validation set from 2018.1 to 2018.6. Results: The optimal gray level was set at 32. Principal component analysis (PCA) was the first choice for dimensionality reduction. The best classifier was support vector machines (SVM). The accuracy of computer classification was 87.80% (439/500). The accuracy of computer classification in the validation cohort was 60.82%. The area under the curve (AUC) of class 1, 2, and 3 were 0.76, 0.41, and 0.48, respectively. Conclusions: Results showed that the computer methods are competent for classifying inhomogeneity of contrast-enhanced rectal cancers inside ultrasonograms.