Evidence map›Paper›PMID 41164423›Full record

ArticlePrecision radiation oncology2025

Optimized fiducial marker placement using B-spline surface modeling and graph theory for Cyberknife stereotactic body radiotherapy for superficial tumors.

Jing Huang, Xianlong Xiong, Cheng Chen, Yuhan Li, Ruijie Wang, Zhitao Dai

Abstract read
In one paragraph

Article in Precision radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Jing HuangSchool of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China.
Xianlong XiongSchool of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China.
Cheng ChenDepartment of Radiation and Medical Oncology Zhongnan Hospital of Wuhan University Wuhan Hubei China.
Yuhan LiSchool of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China.
Ruijie WangSchool of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China.
Zhitao DaiNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital Chinese Academy, of Medical Sciences and Peking Union Medical College Shenzhen China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CyberKnife, an established noninvasive stereotactic radiotherapy technology, has been extensively utilized to treat various malignancies because of its high precision and conformal dose delivery. The success of CyberKnife treatment is crucially dependent on optimal fiducial marker placement. This study introduces a novel fiducial marker placement planning algorithm tailored for superficial tumors, which are located 20-50 mm beneath the epidermis. A retrospective analysis was performed on the data collected from three patients with thymus, breast, and submandibular gland tumors. This algorithm generated potential implantation sites by constructing and optimizing a B-spline surface around the tumor. Candidate points were filtered using multi-criteria constraints: (1) a minimum of 18-mm inter-marker distance, (2) angular separation >30°, and (3) nonoverlapping visibility in 45° oblique digital reconstructed radiographs. To enhance the computational efficiency, a kd-tree spatial indexing structure was integrated with graph theory, specifically the Bron-Kerbosch algorithm for maximal clique detection. The proposed method achieved a time complexity of

Indexed as

CyberKnifeFiducial Marker PlacementStereotactic Body RadiotherapySuperficial Tumor

Identifiers

PMID41164423
PMCPMC12559914

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Registered trials

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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.