Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network. Issue 7 (5th April 2021)

• Record Type: Journal Article
• Authors: Wu, Qing-Yao; Liu, Shang-Long; Sun, Pin; Li, Ying; Liu, Guang-Wei; Liu, Shi-Song; Hu, Ji-Lin; Niu, Tian-Ye; Lu, Yun
• Editors: Lyu, Peng
• Is Part Of: Chinese medical journal. Volume 134:Issue 7(2021)
• Page Start: 821
• Page End: 828
• Publication Date: 2021-04-05
• Subjects: Magnetic resonance imaging -- Rectal neoplasm -- TNM staging -- Artificial intelligence -- Convolutional neural networks; Medicine -- Periodicals; Medicine, Oriental -- Periodicals; Medicine; Medicine, Oriental; Medicine; Medicine, East Asian Traditional; Periodicals; Electronic journals; 610.5
• DOI: 10.1097/CM9.0000000000001401
• Languages: English
• ISSNs: 0366-6999

Abstract

Abstract: Background: Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic resonance imaging (MRI) played an important role in the preoperative examination of patients with rectal cancer, and artificial intelligence (AI) in the learning of images made significant achievements in recent years. Introducing AI into MRI recognition, a stable platform for image recognition and judgment can be established in a short period. This study aimed to establish an automatic diagnostic platform for predicting preoperative T staging of rectal cancer through a deep neural network. Methods: A total of 183 rectal cancer patients' data were collected retrospectively as research objects. Faster region-based convolutional neural networks (Faster R-CNN) were used to build the platform. And the platform was evaluated according to the receiver operating characteristic (ROC) curve. Results: An automatic diagnosis platform for T staging of rectal cancer was established through the study of MRI. The areas under the ROC curve (AUC) were 0.99 in the horizontal plane, 0.97 in the sagittal plane, and 0.98 in the coronal plane. In the horizontal plane, the AUC of T1 stage was 1, AUC of T2 stage was 1, AUC of T3 stage was 1, AUC of T4 stage was 1. In the coronal plane, AUC of T1 stage was 0.96, AUC of T2 stage was 0.97, AUC of T3 stage was 0.97, AUC of T4 stage was 0.97. In the sagittal plane, AUC of T1 stage was 0.95, AUC of T2 stage was 0.99, AUC of T3 stage was 0.96, and AUC of T4 stage was 1.00. Conclusion: Faster R-CNN AI might be an effective and objective method to build the platform for predicting rectal cancer T-staging. Trial registration: chictr.org.cn: ChiCTR1900023575; http://www.chictr.org.cn/showproj.aspx?proj=39665 .