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

Abstract

Objective: The clinical aim of this work is to predict intraoperative LRC from preoperative CT scans only.

Summary of background data: Liver resection (LR) is the most prevalent curative treatment for primary liver cancer, yet overall mortality/morbidity rates remain elevated. The conventional definition and classification of LR complexity (LRC) lack inclusion of the disease-induced 3D anatomical surgery complexity.

Methods: 3D models of the organ, tumors and blood vessels were generated from Deep Learning models trained on patients CT scans. The surgeons' expertise on which anatomical factors lead to LRC was translated into a new anatomical frame of reference around the Hepatic Central Zone (HCZ). A fully automatic pipeline to generate the HCZ and quantify the tumors position relative to it was assessed. An AI model was then trained to predict LRC from a patient cohort for whom LRC was annotated at the end of each surgery. The AI-prediction was finally compared to prediction of surgeons that only saw the patient preoperative CT scan.

Results: The 3D reconstructions are successfully evaluated on benchmark datasets. The HCZ is accurately generated for a variety of atypical vascular anatomies (dice score 82±4.6%). The automatic pipeline is successfully run on a 145 HCC patient cohort. The predicted LRC outperforms the surgeons' individual and combined anticipated complexities (accuracy and AUC scores: 79.4±3.4% and 85.1±3.2% respectively).

Conclusion: This automatic digital tool accurately predicts intraoperative LRC and paves the way for an innovative oncology surgery planning. This tool could help orient patients towards appropriate medical centers depending on the predicted LRC level.

Keywords: 3D reconstructions; deep learning; hepatectomy; hepatic vessels; surgery complexity; topological vessel analysis; tumors.