Evidence map›Paper›PMID 42422135›Full record

ArticleJournal of medical physics

Development of an End-to-end Phantom and Procedure for Computed Tomography and Cone-beam Computed Tomography-guided Online Adaptive Radiotherapy.

Yoshifumi Kamikubo, Masato Tsuneda, Shouji Kutsuki, Takashi Uno

Abstract read
In one paragraph

Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

4 authors.

Yoshifumi KamikuboDepartment of Central Radiology, Kyosai Tachikawa Hospital, Tachikawa, Tokyo, Japan.
Masato TsunedaDepartment of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Chuo-Ku, Chiba, Japan.
Shouji KutsukiDepartment of Radiation Oncology, Kyosai Tachikawa Hospital, Tachikawa, Tokyo, Japan.
Takashi UnoDiagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chuo-Ku, Chiba, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to develop an end-to-end (E2E) testing procedure to verify in-room computed tomography (CT)-on-rails and cone-beam CT (CBCT)-guided online adaptive radiation therapy (OART) workflows. Materials and Methods: A water tank-type lung phantom was developed to mimic the lung tissue and tumors, to evaluate deformable image registration (DIR) and dosimetry. Tumor shrinkage was mimicked by shape, position, and material changes. DIR was performed from the ellipsoidal tumor contoured on the initial CT image to multiple tumor cases on the CT and CBCT images. DIR accuracy was evaluated using the dice similarity coefficient (DSC) for manually delineated contours and those propagated by the adaptation anatomy algorithm. Dosimetric accuracy was evaluated by comparing the calculated and measured doses. An ionization chamber was used for point dose measurement, and a GafChromic film was used for gamma analysis using a 3%/2 mm criterion. Results: The averaged DSC values for the CT-guided and CBCT-guided OART workflows were 0.988 and 0.982, respectively. We confirmed high DSCs across different tumor conditions. The relative point dose differences between the measured and calculated doses for the CT-guided and CBCT-guided OART workflows were 0.02% and 0.86%, respectively. In the sagittal planes, the gamma passing rates were 97.8% and 95.6% for the CT-guided and CBCT-guided OART workflows, respectively, indicating good agreement between the measurements and calculations. Conclusion: We developed a novel lung phantom for E2E testing and established workflows for both CT-guided and CBCT-guided OART.

Indexed as

Computed tomography-on-railscone-beam computed tomographyend-to-end testin-room computed tomographyonline adaptive radiation therapy

Identifiers

PMID42422135
PMCPMC13345027

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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.