Open AccessEstablishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network
Author(s) -
Qingyao Wu,
Shanglong Liu,
Peng Sun,
Ying Li,
Guangwei Liu,
Shisong Liu,
Jilin Hu,
Tianye Niu,
Yun Lu
Publication year - 2021
Publication title -
chinese medical journal/chinese medical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 63
eISSN - 2542-5641
pISSN - 0366-6999
DOI - 10.1097/cm9.0000000000001401
Subject(s) - receiver operating characteristic , coronal plane , sagittal plane , stage (stratigraphy) , colorectal cancer , medicine , magnetic resonance imaging , convolutional neural network , radiology , cancer , area under the curve , artificial intelligence , nuclear medicine , computer science , paleontology , biology
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.