Dataset Information

Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network.

ABSTRACT: Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer. Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system. Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s. Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.

SUBMITTER: Zheng L

PROVIDER: S-EPMC7399625 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlthough diagnostic ultrasound can non-invasively capture the image of abdominal viscera, diagnosis of the continuous ultrasound liver images to detect a liver tumor effectively and to determine whether the detected is benign or malignant is nontrivial. In order to minimize the gaps in diagnostic accuracy depending on doctor's proficiency, we built an automated system to support the ultrasonography of liver tumors by employing deep learning technologies.MethodsWe constructed a neural network model for the automated detection of tumor tissues and blood vessels from the sequential liver ultrasound images. Faster region-based convolutional neural networks (Faster R-CNN) is employed as a base model for the object detection, which can output the detection results in 4 frames per second and enable the system to be particularly suitable for the real time ultrasonography. Moreover, we proposed a new neural network architecture feeding both the current and previous images into Faster R-CNN. For training the models, intraoperative ultrasound images obtained from one hepatocellular carcinoma (HCC) patient were used. The obtained image was a multifaceted observation of the liver and includes one HCC and some blood vessels. We labeled 91 images with the help of a liver specialist. We compared the tumor detection performance of the plain Faster R-CNN model with that of the proposed model.ResultsWe find that both the models performed well in detecting HCC and blood vessels, after training with 400 epochs using Adam. However, the mean precision of our model reaches 0.549, which is 0.019 better than that of the plain Faster R-CNN, and the mean sensitivity of our model about HCC reaches 0.623±0.385 for 30 scenes of sequential liver ultrasound images, which is also 0.146 better than that of the plain Faster R-CNN model.ConclusionsThe comparison between the proposed model and the plain Faster R-CNN model shows that we achieved better accuracy in tumor detection, in terms of the mean precision as well as the mean sensitivity, with the proposed model.

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Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network.

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