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

Comparing Three Modeling Approaches to Predict Resection Zones for Parenchyma-Sparing Hepatectomy Planning

By
Janine Rothert
Joy Rakshit
Judith L. Salz
Florentine Huettl
Viola Ehses
Tobias Huber
Hauke Lang
Sylvia Saalfeld
Georg Hille
October 7, 2026

Int. Journal Of Computer Assisted Radiology And Surgery

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1

The study compared distance-based, perfusion-based, and deep learning models for predicting parenchyma-sparing liver resection zones.
2

The retrospective dataset included 104 patients and 104 resection zones, represented by rasterized segmentation masks at 0.4-mm isotropic resolution.
3

The mean in silico minimum resection margin was 7.63 ± 6.70 mm, and the distance-based model used an 8-mm tumor dilation.
4

The distance-based model used liver boundaries and tumor location to predict nonanatomical resections of peripheral tumors.
5

The perfusion-based model incorporated liver, tumor, and vascular structures and modeled downstream perfusion loss after vessel transections.

Original Source(s)

Int. Journal Of Computer Assisted Radiology And Surgery

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

by Janine Rothert, Joy Rakshit, Judith L. Salz, Florentine Huettl, Viola Ehses, Tobias Huber, Hauke Lang, Sylvia Saalfeld, Georg Hille
October 7, 2026

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