# Evaluation of daily residual pancreatic tumor motion for deep‐inspiration breath‐hold radiotherapy

## TL;DR

This study evaluates residual pancreatic tumor motion during breath-hold radiotherapy using automated fiducial marker detection, finding significant motion that could impact treatment accuracy.

## Contribution

The study introduces an automated method for daily residual tumor motion assessment during breath-hold radiotherapy using CBCT scans.

## Key findings

- Intrafraction target motion averaged 1.9 ± 2.2 mm with a maximum displacement of 8 mm.
- Residual motion within a single CBCT scan reached up to 7.3 mm with an average drift of 3.8 ± 1.1 mm.
- Significant drift (1.3 ± 1.2 mm) and inter-breath-hold gaps (2.6 ± 2.0 mm) were observed in CBCT scans.

## Abstract

Breath‐hold techniques are widely used in radiation therapy to minimize respiratory‐induced tumor or organ‐at‐risk motion. However, residual motion persists, necessitating a reliable daily evaluation method.

At our institution, fiducial markers serve as surrogates for target localization in pancreatic cancer treatment. We developed an automated method to detect fiducial markers in every projection image of cone‐beam computed tomography (CBCT) scans acquired for patient setup and positioning verification. This method was retrospectively validated using data from nine pancreatic cancer patients.

Residual motion was observed in all patients during breath‐hold maneuvers. Intrafraction target motion in repeated breath‐hold simulation CT scans averaged 1.9 ± 2.2 mm, with a maximum displacement of 8 mm in the superior‐inferior direction. Within a single CBCT scan, residual motion reached up to 7.3 mm, with an average drifting range of 3.8 ± 1.1 mm across 94 CBCT scans. The average standard deviation of drift was 1.5 ± 0.5 mm. Significant drift (1.3 ± 1.2 mm) and inter‐breath‐hold gaps (2.6 ± 2.0 mm) were detected within the same CBCT scan.

Our method enables daily residual motion assessment without additional equipment or extra radiation exposure. This information is critical for refining planning margins in online adaptive radiation therapy, improving treatment precision and patient safety.