Comparison between inverse and convolution plan in treatment of cavernous malformations by ICON gamma knife stereotactic radiosurgery

Abstract

Author(s): Safaa Sam

Background: This research aims to evaluate and compare the effectiveness and accuracy of the Inverse and Convolution planning strategies for the treatment of cavernous malformations via the use of ICON Gamma Knife stereotactic radiosurgery. Methods: A retrospective cohort research was undertaken at the Al-Taj Centre of Gamma Knife in Baghdad, Iraq, spanning the period from January to August 2023. A cohort of forty individuals diagnosed with cavernous malformations were chosen using a random stratified sample method and then underwent treatment use the ICON iteration of the Gamma Knife. Every individual participant got a 3 Tesla Magnetic Resonance iImaging (MRI) scan in order to get comprehensive anatomical mapping. The generation of treatment plans included the use of both Inverse and Convolution methodologies. The assessment criteria included many characteristics, including the Paddick Conformity Index (PCI), Homogeneity Index (HI), treatment duration, Selectivity, Coverage, Gradient Index (GI), and dosage to risk tissues. The statistical analysis was conducted using the software SPSS-28. Results: The research cohort exhibited an average age of 63.12 years ± 13.42 years, with a little preponderance of female participants. The Inverse planning strategy exhibited improved brain stem protection, higher PCI, and faster beam-on time, but the Convolution plan revealed superiority in terms of Selectivity and GI. There was no statistically significant difference in the integral dose between the two designs, indicating that the total radiation exposure was the same. Conclusion: This research demonstrates how inverse planning and convulsion methods may be used to treat cavernous malformations using Gamma Knife radiosurgery. While convulsion planning improves selectivity and dosage gradient, inverse planning protects and conforms brain structures better.

Share this article

Awards Nomination

Editors List

  • Prof. Elhadi Miskeen

    Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia

  • Ahmed Hussien Alshewered

    University of Basrah College of Medicine, Iraq

  • Sudhakar Tummala

    Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh

     

     

     

  • Alphonse Laya

    Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis

     

  • Fava Maria Giovanna

     

Google Scholar citation report
Citations : 208

Onkologia i Radioterapia received 208 citations as per Google Scholar report

Onkologia i Radioterapia peer review process verified at publons
Indexed In
  • Directory of Open Access Journals
  • Scimago
  • SCOPUS
  • EBSCO A-Z
  • MIAR
  • Euro Pub
  • Google Scholar
  • Medical Project Poland
  • PUBMED
  • Cancer Index
  • Gdansk University of Technology, Ministry Points 20