Evidence map›Paper›PMID 41585906›Full record

ArticleAdvances in radiation oncology2026

A Smartphone-Based Motion Monitoring System for Surface Guided Radiation Therapy.

Dante P I Capaldi, Emily Hirata, Alon Witztum, Evan Porter, Amy S Yu, Lawrie B Skinner, Steve E Braunstein, Olivier Morin, Nicolas D Prionas

Abstract read
In one paragraph

Article in Advances in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Dante P I CapaldiDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.
Emily HirataDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.
Alon WitztumDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.
Evan PorterDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.
Amy S YuDepartment of Radiation Oncology, Stanford University, Stanford, California.
Lawrie B SkinnerDepartment of Radiation Oncology, Stanford University, Stanford, California.
Steve E BraunsteinDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.
Olivier MorinDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.
Nicolas D PrionasDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Surface guided radiation therapy (SGRT) improves patient setup and motion monitoring, particularly for deep-inspiratory breath-hold (DIBH) maneuvers in left-sided breast cancer treatment. However, high costs and complexity limit widespread adoption, especially in low-resource settings. In this study, the purpose is to develop and validate a smartphone-based iOS SGRT application (iSGRT) leveraging Light-Detection-and-Ranging (LiDAR) sensors on smartphone-devices for accurate, low-cost surface tracking for radiation therapy. Methods and Materials: iSGRT was developed in Xcode using Swift and Open3D, and captures 6-degrees-of-freedom (6DoF) motion for patient positioning and respiratory monitoring. Application was tested using the LiDAR camera on an Apple iPhone 15 Pro, with an Apple iPad Pro for remote monitoring. The system achieved a temporal resolution of ∼200 to 250 ms (4-5 Hz), comparable to clinical SGRT systems. Static accuracy was evaluated by comparing LiDAR-derived displacements with programmed couch movements on a Varian TrueBeam with a PerfectPitch 6DoF couch. Dynamic accuracy was assessed using a QUASAR respiratory motion phantom programmed with sinusoidal and patient-derived breathing waveforms. Motion tracking performance was analyzed using Pearson correlations and Bland-Altman agreement using GraphPad Prism. iSGRT was compared with SDX spirometry system in a healthy volunteer performing DIBH within the bore of a Varian Halcyon. Results: iSGRT demonstrated strong correlations with couch displacements across all translational ( Conclusions: Feasibility of an iOS smartphone-based SGRT application to provide real-time respiratory motion is demonstrated in this study as a viable alternative motion monitoring system. The iSGRT application's accuracy aligns with existing clinical SGRT systems while significantly reducing cost and complexity. This technology has the potential to expand SGRT accessibility, particularly in resource-limited settings.

Identifiers

PMID41585906
PMCPMC12828761

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.