# A Novel Method to Compute the Contact Surface Area Between an Organ and Cancer Tissue

**Authors:** Alessandra Bulanti, Alessandro Carfì, Paolo Traverso, Carlo Terrone, Fulvio Mastrogiovanni

PMC · DOI: 10.3390/jimaging11030078 · Journal of Imaging · 2025-03-06

## TL;DR

This paper introduces a new method to accurately calculate the contact surface area between a tumor and an organ using 3D CT scans, improving reliability over manual methods.

## Contribution

A novel, automated CSA computation method using 3D CT reconstructions and an open-source implementation with a GUI.

## Key findings

- The algorithm showed minimal error when tested on synthetic data.
- Our method demonstrated reliability and consistency compared to expert radiologist measurements.
- The method outperformed a less experienced radiologist in terms of deviation from expert values.

## Abstract

The contact surface area (CSA) quantifies the interface between a tumor and an organ and is a key predictor of perioperative outcomes in kidney cancer. However, existing CSA computation methods rely on shape assumptions and manual annotation. We propose a novel approach using 3D reconstructions from computed tomography (CT) scans to provide an accurate CSA estimate. Our method includes a segmentation protocol and an algorithm that processes reconstructed meshes. We also provide an open-source implementation with a graphical user interface. Tested on synthetic data, the algorithm showed minimal error and was evaluated on data from 82 patients. We computed the CSA using both our approach and Hsieh’s method, which relies on subjective CT scan measurements, in a double-blind study with two radiologists of different experience levels. We assessed the correlation between our approach and the expert radiologist’s measurements, as well as the deviation of both our method and the less experienced radiologist from the expert’s values. While the mean and variance of the differences between the less experienced radiologist and the expert were lower, our method exhibited a slight deviation from the expert’s, demonstrating its reliability and consistency. These findings are further supported by the results obtained from synthetic data testing.

## Linked entities

- **Diseases:** kidney cancer (MONDO:0002367)

## Full-text entities

- **Diseases:** Cancer (MESH:D009369), kidney cancer (MESH:D007680)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11942950/full.md

## References

15 references — full list in the complete paper: https://tomesphere.com/paper/PMC11942950/full.md

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Source: https://tomesphere.com/paper/PMC11942950