Deep learning-based system to predict hepatocellular carcinoma resection volume using contrast-enhanced CT
Xiao Wang1,2,3, Liyuan Zhang4, Pan Liu2
1Department of Hepatobiliary Surgery, Chinese PLA 970th Hospital, 7 Zhichu South Road, Zhifu District, Yantai, 264001, People's Republic of China.
Scientific Reports
|January 27, 2026
Summary
An AI system accurately calculates liver resection volume from CT scans, significantly reducing planning time and variability in surgical preparation for hepatocellular carcinoma patients.
Area of Science:
- Medical Imaging Analysis
- Artificial Intelligence in Surgery
- Hepatobiliary Surgery
Background:
- Accurate liver resection volume calculation is crucial for preoperative surgical planning in hepatocellular carcinoma (HCC).
- Current manual methods are time-consuming, labor-intensive, and suffer from inter-observer variability.
- Precise volumetric analysis aids in radical resection and improves patient outcomes.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI)-based system for accurate and efficient calculation of liver resection volume.
- To compare the AI system's performance against current manual 3D simulation methods.
- To assess the clinical applicability of the AI system in surgical planning workflows.
Main Methods:
- A deep learning system, Liver Resection Volume Calculation with Deep learning, was developed.
- The system was trained and tested on contrast-enhanced computed tomography (CT) images from 990 HCC patients.
- Data was collected from two tertiary hospitals between January 2012 and December 2022.
Main Results:
- The AI system achieved high accuracy and efficiency in calculating planned liver resection volume.
- It reduced calculation time by approximately twenty-fold compared to manual methods.
- Results demonstrated consistency with the planning outcomes of experienced surgeons.
Conclusions:
- The AI-powered system offers a highly accurate and efficient solution for liver resection volume calculation.
- It significantly streamlines the preoperative surgical planning process for HCC.
- The system's consistency and speed show strong potential for integration into clinical practice.
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