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Deep learning-based system to predict hepatocellular carcinoma resection volume using contrast-enhanced CT.

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An AI system accurately calculates liver resection volume from CT scans, significantly reducing planning time and variability in surgical preparation for hepatocellular carcinoma patients.

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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.