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Xin H, Zhang Y, Lai Q, Liao N, Zhang J, Liu Y, Chen Z, He P, He J, Liu J, Zhou Y, Yang W, Zhou Y. Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study. EClinicalMedicine 2024; 69:102464. [PMID: 38333364 PMCID: PMC10847157 DOI: 10.1016/j.eclinm.2024.102464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/22/2023] [Accepted: 01/17/2024] [Indexed: 02/10/2024] Open
Abstract
Background Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow. Methods We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed. Findings ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability. Interpretation Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images. Funding National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.
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Affiliation(s)
- Hongjie Xin
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yiwen Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianwei Lai
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Naying Liao
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jing Zhang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanping Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, The Second Affiliated Hospital, University of South China, Hengyang, China
| | - Zhihua Chen
- Department of Radiology, The Second Affiliated Hospital, University of South China, Hengyang, China
| | - Pengyuan He
- Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Jian He
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Junwei Liu
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuchen Zhou
- Department of General Surgery, Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuanping Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
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