Evidence map›Paper›PMID 41756315›Full record

ArticleFrontiers in oncology2026

Post-TPS needle placement planning for robotic-assisted LDR seed brachytherapy.

Yilun Fan, Xuehe Zhang, Lichen Liu, Changle Li, Chong Yao, Jie Zhao

Abstract read
In one paragraph

Article in Frontiers in oncology, 2026. 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. Review
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.

Yilun FanState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Xuehe ZhangState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Lichen LiuHarbin Institute of Technology Hospital, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Changle LiState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Chong YaoState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Jie ZhaoState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Low-dose-rate (LDR) seed brachytherapy treatment planning systems (TPSs) can generate dose-optimized seed distributions; however, translating these plans into robot-executable needle insertions introduces additional geometric, anatomical, and robotic constraints that may limit clinical feasibility. Methods: This study presents an optimization framework for robotic-assisted LDR seed brachytherapy that refines needle trajectories based on preplanned seed distributions. The framework explicitly incorporates anatomical safety constraints and robotic feasibility requirements while aiming to preserve the original dosimetric quality. Results: Simulation studies using thoracic and upper abdominal anatomical models show that the proposed method can generate clinically executable needle placements with minimal deviation from the planned seed positions. Phantom experiments further demonstrate the practical feasibility and placement accuracy of the optimized trajectories under robotic operating conditions. Discussion: The proposed framework improves the feasibility and reproducibility of robotic-assisted LDR seed implantation in thoracic and related anatomical settings, offering a practical pathway toward safer and more reliable clinical deployment.

Indexed as

Hough transformLDR brachytherapyoptimizationrobot surgerytreatment planning

Identifiers

PMID41756315
PMCPMC12932239

What OpenQuestion holds

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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.