The study, published in the International Journal of Computer Assisted Radiology and Surgery by researchers from Eindhoven University of Technology and Catharina Hospital, tackles a problem that has long frustrated radiologists. When they examine CT scans for peritoneal metastases, subtle lesions are easily missed, and the extent of disease is frequently underestimated compared with what a surgeon actually finds. Part of the difficulty is structural: the surgical PCI system, developed by Sugarbaker, was never designed for cross-sectional imaging, so radiologists assessing the abdomen on CT have lacked standardized regional boundaries. The result is inconsistent reporting and considerable variability between readers, which may explain why only a minority of radiologists currently attempt PCI scoring on images at all.

A recent Delphi consensus study involving 88 international experts in radiology, surgery, and gynecology changed that landscape by defining thirteen radiological PCI regions, numbered 0 through 12: the central region, right and left upper abdomen, epigastrium, left and right flanks, left and right lower abdomen, pelvis, and four small-bowel regions covering the upper and lower jejunum and ileum. The new research takes those expert-defined boundaries and teaches an artificial intelligence to reproduce them automatically. Crucially, the regions are not organ segmentations; they are subdivisions of the peritoneal cavity itself, each bounded by anatomical landmarks and surrounding organ surfaces—a spatial partition that is far harder for a neural network to learn than, say, the outline of a liver.

To train and test their models, the team assembled 62 contrast-enhanced CT scans from patients with gastric, ovarian, and colorectal cancers, deliberately spanning the full PCI range from 0 to 39. Every scan was painstakingly annotated in three dimensions by clinical researchers, with each annotation reviewed by a second annotator and disagreements settled by an expert radiologist. The effort involved was enormous: annotating a single scan takes roughly five hours. The researchers then compared two of the most widely used architectures in 3D medical image segmentation—the self-configuring convolutional network nnU-Net and the transformer-based Swin UNETR—using fivefold cross-validation and standard metrics including the Dice similarity coefficient, the 95th-percentile Hausdorff distance, and average surface distance.

The head-to-head comparison delivered a clear verdict. The convolutional nnU-Net achieved an overall Dice score of 0.81, outperforming the transformer-based Swin UNETR, which reached 0.76. Beyond raw accuracy, nnU-Net proved markedly more stable across patients, with a higher median Dice in every single region, whereas the transformer showed greater sensitivity to patient-specific anatomical variability. Both models struggled most in the right flank and the small-bowel regions, where regional boundaries are defined geometrically rather than by visible tissue contrast—precisely the places where a network trained purely on image appearance has nothing to latch onto.

That observation inspired the study’s most inventive contribution: an anatomically constrained pipeline that sidesteps the need to learn ill-defined boundaries at all. Instead of asking the network to distinguish, for example, the upper jejunum from the lower jejunum on a CT image where no visible border exists, the researchers trained a second nnU-Net on merged super-regions—grouping the flank and lower regions on each side, and all four small-bowel regions together. After prediction, a deterministic post-processing step splits these super-regions back into the thirteen individual regions using anatomical landmarks detected by the established TotalSegmentator tool. A transverse plane at the top of each hip bone divides flank from lower abdomen, while anteroposterior planes radiating from the ligament of Treitz—the duodenum–small-bowel transition—carve the small-bowel super-region into four equal-volume wedges, mirroring the consensus definitions.

The results were striking. The anatomically constrained pipeline lifted the overall Dice score from 0.81 to 0.84 and reduced boundary errors substantially, with the 95th-percentile Hausdorff distance falling from 13.7 to 11.8 millimeters and average surface distance from 4.1 to 3.4 millimeters. The largest gains came exactly where the baseline had faltered: in the small-bowel regions, mean Dice improved from 0.74 to 0.82, and average surface distance dropped from 5.8 to 3.7 millimeters. Residual errors in these regions were traced mainly to the localization of the anatomical landmarks themselves rather than to the segmentation network, pointing to a clear target for future refinement.

Perhaps the most clinically meaningful comparison in the study is not model against model but machine against human. On a separate subset of ten CT scans, each independently annotated in triplicate by three clinical researchers, the interobserver Dice score—the agreement between human experts—was 0.87. The automated pipeline, evaluated on identical data, achieved 0.84, with boundary-error metrics of the same order of magnitude as human variability. In most regions, the model’s agreement with human annotators approached the level at which humans agree with one another, and in the small-bowel regions the model actually showed far less variation across patients than the human observers did. In other words, the algorithm is already performing near the ceiling set by human consistency itself.

The clinical implications extend beyond a single score. Because current radiological assessment of peritoneal disease lacks standardized regional boundaries, an automatic segmentation overlay could guide radiologists to systematically inspect each predefined region, reduce interobserver variability, standardize reporting, and support surgical planning. It could also accelerate reporting workflows and serve as a training aid for less experienced readers. Computationally, the approach is feasible for real-world use: nnU-Net inference takes roughly seven seconds per scan on a single NVIDIA A100 GPU. The researchers envision a human-in-the-loop workflow in which the model proposes regions and radiologists make minor boundary adjustments, and a longer-term extension in which lesions are localized within each region and mapped to PCI subscores automatically.

The authors are candid about the limitations. The study is single-center, with a private dataset of 62 scans, and variation in scanners, acquisition protocols, and contrast timing across institutions could affect generalizability; external validation on multi-center data is a stated priority. The interobserver analysis rested on only ten scans, because triple-annotating the full cohort at five hours per scan was infeasible. Some patients with surgically altered anatomy or large tumors that displace organs produced outlier errors, particularly affecting the rule-based post-processing in the right flank. Yet the direction of travel is unmistakable. With code and trained model weights released openly on GitHub, and the radiological PCI framework itself born from international consensus, the study offers a reproducible foundation for something oncology has wanted for decades: an objective, noninvasive, and standardized measure of peritoneal cancer burden that any hospital with a CT scanner could one day compute in seconds.

Subject of Research: Deep learning segmentation of radiological peritoneal cancer index regions on CT for noninvasive staging of peritoneal metastases

Article Title: Deep learning-based segmentation of peritoneal cancer index regions from CT imaging

Article References: Gort, P. C., Fleurkens-Ewals, L. J. S., Kampmeijer, L. D., van Herwijnen, A. F., Tops-Welten, M. W., Claessens, C. H. B., Nederend, J., De Hingh, I. H. J. T., Lahaye, M. J., Luyer, M. D. P., & van der Sommen, F. (2026). Deep learning-based segmentation of peritoneal cancer index regions from CT imaging. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03796-9

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03796-9

Keywords: peritoneal metastases, peritoneal cancer index, deep learning, CT imaging, medical image segmentation, nnU-Net, Swin UNETR, TotalSegmentator, radiology, surgical oncology, artificial intelligence, computer-assisted surgery