Resection zone prediction for parenchyma-sparing hepatectomy planning: a comparative study of three modeling paradigms.

purposeParenchyma-sparing hepatectomy planning depends on accurate resection zones that preserve functional liver tissue without compromising oncological margins. This work investigates how different levels of anatomical and functional information complexity influence resection zone prediction for parenchyma-sparing surgical planning for primary liver cancer.

methodsWe compare three modeling paradigms: a geometric distance-based approach, an explicit perfusion-based method using vascular anatomy, and a deep learning-based model built on the U-Net architecture. All methods operate on segmentation-derived representations of the liver, tumor, and vessels. Performance is evaluated using overlap- and distance-based metrics.

resultsThe distance-based model produces predictions with limited surface deviation (HD

conclusionNone of the models is universally outperforming the other two for parenchyma-sparing resection planning. Deep learning shows strong predictive performance, particularly for larger resection volumes, while geometric and perfusion-based models offer greater interpretability and clinical controllability. The distance-based approach is well suited for maximal parenchyma-sparing resections, whereas perfusion-based modeling is advantageous for tumors near vessels with potential perfusion loss. Our results highlight variations due to different surgical strategies and can thus provide valuable guidance for individual patient's surgery planning.