Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features
Lei Yuan,
Mengyao Ji,
Shanshan Wang,
Xuefang Lu,
Yong Li,
Pingxiao Huang,
Cheng Lu,
Lei Shen,
Jun Xu
Affiliations
Lei Yuan
School of Automation, Nanjing University of Information Science and Technology, Nanjing, China; Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei, China; Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China; Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China
Mengyao Ji
Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China; Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China
Shanshan Wang
Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China; Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China
Xuefang Lu
Department of Radiology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
Yong Li
Department of Radiology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
Pingxiao Huang
Department of Radiology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
Cheng Lu
Department of Radiology, Guangdong Provincial People’s Hospital, Guangzhou, China
Lei Shen
Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China; Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China; Corresponding author
Jun Xu
School of Automation, Nanjing University of Information Science and Technology, Nanjing, China; Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China; Corresponding author
Summary: Early prediction of acute pancreatitis (AP) with acute kidney injury (AKI) using abdominal contrast-enhanced CT could effectively reduce the mortality and the economic burden on patients and society. However, this challenge is limited by the imaging manifestations of early-stage AP that are not clearly visible to the naked eye. To address this, we developed a machine learning model using imperceptible variations in the structural changes of pancreas and peripancreatic region, extracted by radiomics and artificial intelligence technology, to screen and stratify the high-risk AP patients at the early stage of AP. The results demonstrate that the machine learning model could screen the high-risk AP with AKI patients with an area under the curve (AUC) of 0.82 for the external cohort, superior to the human radiologists. This finding confirms the significant potential of machine learning in the screening of acute pancreatitis and contributes to personalized treatment and management for AP patients.
