CoRe: An Automated Pipeline for The Prediction of Liver Resection Complexity from Preoperative CT Scans
This work addresses the problem of objectively anticipating surgical complexity for liver cancer patients, potentially improving care and reducing complications, but it is incremental as it builds on existing deep learning methods for medical imaging.
The paper tackles predicting liver resection complexity from preoperative CT scans using an automated pipeline called CoRe, which extracts imaging biomarkers including a novel hepatic central zone feature, achieving an accuracy of 77.3%, F1 of 75.4%, and AUC of 84.1%.
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.