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May 1, 2022Article of the Month – May 2022
Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network...
Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network
Background
Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer. This study was performed to develop an automatic and accurate imaging processing technique system, allowing this system to read computed tomography (CT) images correctly and make diagnosis of pancreatic cancer faster.
Methods
The establishment of the artificial intelligence (AI) system for pancreatic cancer diagnosis based on sequential contrast-enhanced CT images were composed of two processes: training and verification. During training process, our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set. Additionally, we used VGG16, which was pre-trained in ImageNet and contained 13 convolutional layers and three fully connected layers, to initialize the feature extraction network. In the verification experiment, we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolutional network (Faster R-CNN) model that had completed training. In total, 1699 images from 100 pancreatic cancer patients were included for clinical verification.
Results
A total of 338 patients with pancreatic cancer were included in the study. The clinical characteristics (sex, age, tumor location, differentiation grade, and tumor-node-metastasis stage) between the two training and verification groups were insignificant. The mean average precision was 0.7664, indicating a good training effect of the Faster R-CNN. Sequential contrast-enhanced CT images of 100 pancreatic cancer patients were used for clinical verification. The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632. It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image, which is much faster than the time required for diagnosis by an imaging specialist.
Conclusions
Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.
Relevance to Healthcare Field
According to the American Cancer Society, pancreatic cancer accounts for about 3% of all cancers in the US and about 7% of all cancer deaths. The Faster region-based convolution network (R-CNN) aims to detect and stage pancreatic cancer to establish a plan. The earlier the diagnosis, the better the prognosis. This study showed high accuracy and will help more patients once it can be used worldwide. AI has proven to be effective in diagnosing different conditions and has proven to be time-effective to determine quicker patient management.