AI, Geometry, or Blood Flow? Study Pits Three Models Against Each Other in Liver Surgery Planning

Liver cancer remains one of the most formidable challenges in modern oncology. Primary liver tumors, including hepatocellular carcinoma and intrahepatic cholangiocarcinoma, are now the sixth most commonly diagnosed cancer worldwide, with more than 800,000 new cases recorded in 2022, and they rank as the third leading cause of cancer-related death, claiming over 700,000 lives each year. For many patients, surgical resection or transplantation offers the only realistic chance of a cure. Yet removing part of the liver is a delicate balancing act: surgeons must excise the tumor with a sufficient safety margin while preserving enough healthy tissue to prevent postoperative liver failure. A new study published in the International Journal of Computer Assisted Radiology and Surgery takes a major step toward making that balance computable, comparing three fundamentally different ways of predicting exactly which tissue needs to be removed.

The research, led by Janine Rothert and colleagues spanning the University Hospital Schleswig-Holstein in Kiel, Otto-von-Guericke-University Magdeburg, and the University Medical Center Mainz, addresses a concept known as parenchyma-sparing hepatectomy. Unlike major resections that remove entire liver segments or lobes, parenchyma-sparing surgery minimizes the volume of liver tissue removed while maintaining oncological safety. This approach expands eligibility for curative surgery, reduces operative stress and morbidity, and preserves liver function for potential future resections. The stakes are quantifiable: clinicians must preserve a future liver remnant of at least 20 to 30 percent in healthy livers, and at least 40 percent in patients with impaired hepatic reserve. Accurate preoperative planning therefore has to integrate tumor location, arterial and portal venous perfusion, hepatic venous drainage, and biliary anatomy all at once.

To test how different levels of anatomical and functional information shape surgical predictions, the team built three distinct modeling paradigms, each operating on segmentation-derived representations of the liver, tumor, and vessels. The first is a purely geometric, distance-based approach designed for non-anatomical, surface-near tumor resections such as atypical wedge procedures. It uses an Euclidean distance transform to find the shortest path from tumor to liver surface, generates a global resection direction from averaged normalized direction vectors, and then dilates the tumor mask by an 8-millimeter margin, a figure matching the dataset’s average planned minimal resection margin of 7.63 millimeters, before extending the zone toward the liver surface. The result is a cylindrical, maximally tissue-sparing resection proposal that requires no knowledge of blood vessels at all.

The second paradigm is explicitly perfusion-aware. Starting from the same 8-millimeter dilation, the algorithm converts skeletonized portal and hepatic vein segmentations into directed graphs, placing nodes at bifurcations and inferring flow direction from the largest-radius node. Any vessel intersecting the initial resection zone is marked as transected, and all downstream branches are classified as non-perfused. Each liver voxel is then assigned to its nearest perfused or non-perfused vessel following a classical method from Selle and colleagues, defining the viable remnant and the final predicted resection zone. In effect, the model approximates Couinaud segments and simulates the physiological consequence of cutting a specific vessel, something purely geometric tools cannot do.

The third paradigm abandons hand-crafted rules entirely. The researchers formulated resection zone prediction as a volumetric segmentation task and trained a deep learning model on the nnU-Net framework, the self-configuring architecture that has repeatedly set benchmarks in medical image segmentation. Input consisted of three channels, a liver mask, a tumor mask, and a combined portal and hepatic vein mask, with the resection zone as the target. Training used 3D full-resolution patches of 160 by 192 by 224 voxels, a residual large encoder with six down- and upsampling stages, and a combined Dice and cross-entropy loss, run over 1000 epochs with fivefold cross-validation balanced for tumor and resection types.

The evaluation drew on a clinical database from Mainz comprising 312 patients who underwent 338 open liver tumor resections performed by seven different surgeons. After strict inclusion and exclusion criteria based on data integrity, entity integrity, and procedure type, 104 patients with 104 retrospectively defined resection zones remained for analysis. All segmentation masks, generated semiautomatically with surgical planning software, were rasterized onto a common voxel grid with 0.4-millimeter isotropic resolution, keeping boundary discretization errors below one millimeter. Predictions were scored with overlap metrics, the Dice similarity coefficient and intersection over union, and distance metrics, including the 95th-percentile Hausdorff distance, maximum Hausdorff distance, and average surface distance.

The results reveal a striking trade-off. The distance-based model produced predictions with the smallest surface deviation, a 95th-percentile Hausdorff distance of 33.89 millimeters, but suffered from systematic undersegmentation, achieving a Dice score of only 58.18 percent. The perfusion-based method struck the best overall balance, with a Dice score of 67.41 percent and a Hausdorff distance of 37.92 millimeters, though it tended to overestimate the resection region because of its strict binary assumption that tissue downstream of a cut vessel is entirely lost, whereas real hepatic perfusion is highly interconnected. The deep learning model delivered the highest overlap accuracy at 76.31 percent, but with the largest distance errors, a 95th-percentile Hausdorff distance of 65.21 millimeters, reflecting localized boundary inaccuracies and occasional spatially distant false positives that disproportionately inflated the distance metrics.

Statistical analyses added crucial nuance. Pearson correlation tests showed that all three models performed significantly better, in terms of Dice overlap, for deeper tumors, likely because larger tumors extend further into the liver and reduce the relative impact of minor boundary deviations. Higher ratios of tumor volume to resection zone volume also correlated with improved performance across models. Subgroup comparisons using the Mann-Whitney U test found that atypical, non-anatomical wedge resections were consistently harder to predict than standardized segmental resections, underscoring how individualized parenchyma-sparing surgery is, and how challenging it is to replicate algorithmically. Notably, the deep learning model’s advantage was strongest for larger and more standardized resection volumes, while the rule-based approaches retained their edge in constrained, tissue-sparing scenarios.

The authors are careful to position these tools as decision support rather than replacement for surgical judgment. Each paradigm, they argue, functions as a modular building block suited to a different clinical situation: the distance-based model is well suited to maximal parenchyma-sparing resections, particularly for patients with limited functional reserve; the perfusion-based model is advantageous when tumors sit near major vessels where downstream perfusion loss must be anticipated; and the deep learning model offers the strongest raw predictive power where larger resections are planned. Because the rule-based methods expose their logic through adjustable parameters such as the resection margin, they also offer a transparency and clinical controllability that neural networks currently lack.

Looking forward, the team proposes hybrid strategies that fuse the paradigms, integrating perfusion-aware and distance-based reasoning into deep learning either during training or as a post-processing step, adding surface-distance-aware loss functions, clinical metadata such as tumor entity and body composition, and simple connected-component cleanup to remove false positives. Extending the framework to standard segment-based hepatectomies is also on the agenda. With the code publicly released on GitHub, the study offers surgeons something genuinely new: not a single algorithmic answer, but a menu of differently nuanced resection proposals for the same patient, each grounded in a different model of liver anatomy and physiology. In a field where every spared millimeter of functional liver tissue can determine whether a patient tolerates the operation, and whether future curative options remain open, that flexibility could prove transformative.

Subject of Research: Computational modeling of resection zone prediction for parenchyma-sparing liver surgery planning

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

Article References: Rothert, J., Rakshit, J., Salz, J. L., Huettl, F., Ehses, V., Huber, T., Lang, H., Saalfeld, S., & Hille, G. (2026). Resection zone prediction for parenchyma-sparing hepatectomy planning: a comparative study of three modeling paradigms. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03798-7

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03798-7

Keywords: liver cancer, hepatectomy, surgical planning, deep learning, nnU-Net, perfusion modeling, resection zone prediction, parenchyma-sparing surgery, medical image segmentation, Hausdorff distance, Dice similarity coefficient, computational surgery