# No cancer left behind: a testbed and demonstration of concept for photoacoustic tumor bed inspection

## TL;DR

This paper introduces a robotic testbed for using photoacoustic imaging during cancer surgery to detect leftover tumor tissue.

## Contribution

The novel contribution is an open-source robotic testbed for intraoperative tumor-bed inspection using photoacoustic imaging.

## Key findings

- A robotic testbed was developed for photoacoustic tumor-bed inspection during surgery.
- The testbed enables repeatable probe placement and automated cavity scanning.
- The platform can be used to optimize and evaluate tumor bed inspection strategies.

## Abstract

Cancer resection surgery is unsuccessful if tumor tissue is left behind in the surgical cavity. Identifying the residual cancer requires additional imaging or postoperative histological analysis. Photoacoustic imaging can be used to image both the surface and depths of the resection cavity; however, its performance hinges on consistent probe placement and stable acoustic and optical coupling. As intra-cavity deployment of photoacoustic imaging is largely uncharted, several potential embodiments warrant rigorous investigation. We address this need with an open-source robotic testbed for intraoperative tumor-bed inspection using photoacoustic imaging. The platform integrates the da Vinci Research Kit, depth imaging, and electromagnetic tracking to automate cavity scanning and maintain repeatable probe trajectories. Using tissue-mimicking phantoms, we (i) demonstrate a novel imaging embodiment for photoacoustic tumor-bed inspection and (ii) show how this testbed can be used to investigate and optimize tumor bed inspection strategies and configurations. This study establishes the feasibility of detecting and mapping residual cancer within a simulated surgical cavity. The primary contribution is the testbed itself, designed for integration with existing surgical navigation workflows and rapid prototyping. This testbed serves as an essential foundation for systematic evaluation of photoacoustic, robot-assisted strategies for improving intraoperative margin assessment.