Diagnosis of primary clear cell carcinoma of the liver based on Faster region-based convolutional neural network.
10.1097/CM9.0000000000002853
- VernacularTitle:Diagnosis of primary clear cell carcinoma of the liver based on Faster region-based convolutional neural network
- Author:
Bin LIU
1
;
Jianfei LI
2
;
Xue YANG
1
;
Feng CHEN
1
;
Yanyan ZHANG
1
;
Hongjun LI
1
Author Information
1. Department of Radiology, Beijing YouAn Hospital Capital Medical University, Beijing 100069, China.
2. Extenics Specialized Committee, Chinese Association of Artificial Intelligence, Beijing 100876, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Liver Neoplasms/pathology*;
Retrospective Studies;
Carcinoma, Hepatocellular/pathology*;
Neural Networks, Computer
- From:
Chinese Medical Journal
2023;136(22):2706-2711
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Distinguishing between primary clear cell carcinoma of the liver (PCCCL) and common hepatocellular carcinoma (CHCC) through traditional inspection methods before the operation is difficult. This study aimed to establish a Faster region-based convolutional neural network (RCNN) model for the accurate differential diagnosis of PCCCL and CHCC.
METHODS:In this study, we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020. A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients' data in the training validation set, and established a convolutional neural network model to distinguish PCCCL and CHCC. The accuracy, average precision, and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.
RESULTS:A total of 4392 images of 121 patients (1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC) were uesd in test set for deep learning and establishing the model, and 1072 images of 30 patients (320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC) were used to test the model. The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962 (95% confidence interval [CI]: 0.931-0.992). The average precision of the model for diagnosing PCCCL was 0.908 (95% CI: 0.823-0.993) and that for diagnosing CHCC was 0.907 (95% CI: 0.823-0.993). The recall of the model for diagnosing PCCCL was 0.951 (95% CI: 0.916-0.985) and that for diagnosing CHCC was 0.960 (95% CI: 0.854-0.962). The time to make a diagnosis using the model took an average of 4 s for each patient.
CONCLUSION:The Faster RCNN model can accurately distinguish PCCCL and CHCC. This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.
