Evidence map›Paper›PMID 41409185›Full record

ArticleJournal of radiosurgery and SBRT2025

Optimizing Gamma Knife radiosurgery: Analysis of gains achieved through upgradation of technology.

Arun Thimmarayappa, Nishanth Sadashiva, Subhas Kanti Konar, Abhinith Shashidhar, Vikas Vazhayil, A R Prabhuraj, Manish Beniwal, Arivazhagan Arimappamagan, Dwarakanath Srinivas, Harsh Deora and 5 more

Abstract read
In one paragraph

Article in Journal of radiosurgery and SBRT, 2025. 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

15 authors.

Arun ThimmarayappaDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Nishanth SadashivaDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Subhas Kanti KonarDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Abhinith ShashidharDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Vikas VazhayilDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
A R PrabhurajDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Manish BeniwalDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Arivazhagan ArimappamaganDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Dwarakanath SrinivasDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Harsh DeoraDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Bhanumathy GovindaswamyDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Gyani Jail Singh BiruaDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Ponnusamy NatesanDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Jeeva BalakrishnanDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Nandakarthik GovindarajanDepartment of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Gamma Knife radiosurgery (GKRS) using the 4C system faces challenges with treatment cancellations due to clearance issues, frame complications, and limited treatment space. Understanding cancellation factors is crucial for optimizing patient selection and workflow, particularly with technological advancements in newer systems. Aims and objectives: This study analyzed the factors associated with treatment feasibility and cancellation in Gamma Knife 4C (GK4C) radiosurgery and explored potential benefits of upgrading to Gamma Knife Icon (GKI) technology, incorporating evidence on clearance mechanisms and treatment improvements. Materials and Methods: A retrospective analysis was conducted on 108 consecutive patients planned for GK4C treatment between 2006 and 2020. Data on demographics, tumor characteristics, treatment parameters, and reasons for cancellation were collected. Statistical analyses examined associations between variables and treatment feasibility. Hypothetical GKI plans assessed potential improvements. A PubMed review identified 69 studies that addressed treatment cancellation and technological improvements. Results: The main reasons for GK4C cancellation were clearance issues (41.7%), high organ-at-risk doses (35.2%), and large target volumes (23.1%). Associations were found between reason for cancellation and tumor volume, diagnosis, location, and prescribed dose. Multivariate analysis showed skull base and peripheral lesions had higher odds of clearance issues. Literature confirms that frame distortion, limited space, and inadequate collision detection are primary GK4C system limitations. GKI plans demonstrated improved target coverage and reduced organ-at-risk doses. Conclusion: Clearance is a major limitation of GK4C radiosurgery, particularly for certain tumor locations and volumes. Literature supports that GKI technology improves treatment feasibility through frameless options, cone-beam CT guidance, enhanced collision detection, and improved stability.

Indexed as

clearance issuescollision detectionGamma Knife radiosurgeryIcon technologyradiosurgery feasibilitytreatment cancellation

Identifiers

PMID41409185
PMCPMC12708000

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.