Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study

Hongjie Xin; Yiwen Zhang; Qianwei Lai; Naying Liao; Jing Zhang; Yanping Liu; Zhihua Chen; Pengyuan He; Jian He; Junwei Liu; Yuchen Zhou; Wei Yang; Yuanping Zhou

doi:10.1016/j.eclinm.2024.102464

Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study

EClinicalMedicine. 2024 Feb 1:69:102464. doi: 10.1016/j.eclinm.2024.102464. eCollection 2024 Mar.

Authors

Hongjie Xin¹, Yiwen Zhang², Qianwei Lai¹, Naying Liao¹, Jing Zhang³, Yanping Liu^{1

4}, Zhihua Chen⁵, Pengyuan He⁶, Jian He¹, Junwei Liu⁷, Yuchen Zhou⁸, Wei Yang², Yuanping Zhou¹

Affiliations

¹ Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
² School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
³ Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
⁴ Department of Gastroenterology, The Second Affiliated Hospital, University of South China, Hengyang, China.
⁵ Department of Radiology, The Second Affiliated Hospital, University of South China, Hengyang, China.
⁶ Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
⁷ Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China.
⁸ Department of General Surgery, Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.

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.

Keywords: Artificial intelligence; Computed tomography; Liver metastases; Primary liver cancer; Primary origin.