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Automatic Generation of Liver Virtual Models with Artificial Intelligence: Application to Liver Resection Complexity

Omar Ali1,2,3,4, Alexandre Bône1, Caterina Accardo5,6

  • 1Guerbet Research, Villepinte, 93420, France.

Annals of Surgery
|April 11, 2025
PubMed
Summary

This study introduces an AI tool that predicts intraoperative liver resection complexity (LRC) using only preoperative CT scans. The AI model accurately forecasts surgical difficulty, outperforming human surgeons and aiding in oncology surgery planning.

Keywords:
3D reconstructionsdeep learninghepatectomyhepatic vesselssurgery complexitytopological vessel analysistumors

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Area of Science:

  • Hepatobiliary surgery
  • Medical imaging
  • Artificial intelligence in medicine

Background:

  • Liver resection (LR) is a primary treatment for liver cancer, but high mortality/morbidity rates persist.
  • Current methods for classifying liver resection complexity (LRC) do not account for 3D anatomical complexity induced by disease.

Purpose of the Study:

  • To develop and validate an AI-driven tool for predicting intraoperative liver resection complexity (LRC) using only preoperative CT scans.
  • To create a novel anatomical framework for assessing surgical complexity based on the Hepatic Central Zone (HCZ).

Main Methods:

  • 3D organ, tumor, and vessel models were generated using deep learning on patient CT scans.
  • An automated pipeline was developed to define the HCZ and quantify tumor proximity.
  • An AI model was trained on 145 HCC patients to predict LRC, comparing its performance against surgeon predictions.

Main Results:

  • Accurate 3D reconstructions and HCZ generation (Dice score 82±4.6%) were achieved, even with atypical vasculature.
  • The AI model demonstrated superior LRC prediction accuracy (79.4±3.4%) and AUC (85.1±3.2%) compared to surgeons.
  • The automated pipeline successfully processed a cohort of 145 HCC patients.

Conclusions:

  • An automated digital tool accurately predicts intraoperative LRC from preoperative CT scans.
  • This technology offers innovative potential for oncology surgery planning and patient referral.
  • The tool can assist in directing patients to specialized medical centers based on predicted surgical complexity.