Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical prognosis. However, there are significant differences and difficulties associated with manually identifying tumor sprouting. This study used the Faster region convolutional neural network (RCNN) model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal cancer. We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October 2019. The pathologists used LabelImg software to identify tumor buds and to count their numbers. Finally, 1,000 images were screened, and the total number of tumor buds was approximately 3,226; the images were randomly divided into a training set and a test set at a ratio of 6:4. After the images in the training set were manually identified, the identified buds in the 600 images were used to train the Faster RCNN identification model. After the establishment of the artificial intelligence identification detection platform, 400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor buds. Finally, by comparing the results of the Faster RCNN system and the identification information of pathologists, the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate treatment. The selected performance indicators include accuracy, precision, specificity, etc. ROC (receiver operator characteristic) and AUC (area under the curve) were used to quantify the performance of the system to automatically identify tumor budding areas and numbers. The AUC of the receiver operating characteristic curve of the artificial intelligence detection and identification system was 0.96, the image diagnosis accuracy rate was 0.89, the precision was 0.855, the sensitivity was 0.94, the specificity was 0.83, and the negative predictive value was 0.933. After 400 test sets, pathological image verification showed that there were 356 images with the same positive budding area count, and the difference between the positive area count and the manual detection count in the remaining images was le