CoRe: An Automated Pipeline for The Prediction of Liver Resection Complexity from Preoperative CT Scans

TL;DR

This paper introduces CoRe, an automated pipeline that predicts liver resection complexity from preoperative CT scans using deep learning and novel imaging biomarkers, aiming to assist surgeons in preoperative planning.

Contribution

The study presents a new automated pipeline combining deep learning segmentation and a novel biomarker derived from the hepatic central zone to predict liver resection complexity.

Findings

Achieved up to 77.3% accuracy in predicting LR complexity.

Identified the HCZ-based biomarker as the most predictive feature.

Demonstrated the pipeline's potential to improve preoperative planning.

Abstract

Surgical resections are the most prevalent curative treatment for primary liver cancer. Tumors located in critical positions are known to complexify liver resections (LR). While experienced surgeons in specialized medical centers may have the necessary expertise to accurately anticipate LR complexity, and prepare accordingly, an objective method able to reproduce this behavior would have the potential to improve the standard routine of care, and avoid intra- and postoperative complications. In this article, we propose CoRe, an automated medical image processing pipeline for the prediction of postoperative LR complexity from preoperative CT scans, using imaging biomarkers. The CoRe pipeline first segments the liver, lesions, and vessels with two deep learning networks. The liver vasculature is then pruned based on a topological criterion to define the hepatic central zone (HCZ), a convex volume circumscribing the major liver vessels, from which a new imaging biomarker, BHCZ is derived. Additional biomarkers are extracted and leveraged to train and evaluate a LR complexity prediction model. An ablation study shows the HCZ-based biomarker as the central feature in predicting LR complexity. The best predictive model reaches an accuracy, F1, and AUC of 77.3, 75.4, and 84.1% respectively.