Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neurosurgical Endovascular Credentialing in Europe and the United Kingdom for the "Complete" Neurovascular Surgeon: The Time has Come.

Neurosurgery·2026
Same author

Whole genome sequencing of pre-treatment and post-treatment locally advanced rectal cancer using long and short read technologies.

Scientific reports·2026
Same author

The impact of DICOM import/export on radiotherapy structures in commercial systems.

Physics and imaging in radiation oncology·2026
Same author

"Kusa Ikivi": Death as an Accomplishment and End-of-Life Experience of Cancer Patients in Rwanda.

Journal of pain and symptom management·2026
Same author

Outcome predictors in older adults (≥ 65 years) with aneurysmal subarachnoid haemorrhage.

Neurosurgical review·2026
Same author

Between the ambulance and academia: rethinking identity and competence in paramedics with reduced clinical exposure.

British paramedic journal·2026

Related Experiment Video

Updated: Dec 11, 2025

Stereotactic Radiosurgery for Gynecologic Cancer
10:35

Stereotactic Radiosurgery for Gynecologic Cancer

Published on: April 17, 2012

18.5K

Determining normal tissue dose in intracranial stereotactic radiosurgery: A diameter-based predictive nomogram.

Donal Cummins1, Siobhra O'Sullivan1,2, Mary Dunne1

  • 1St Luke's Radiation Oncology Network, Dublin 6, Ireland.

Journal of Radiosurgery and SBRT
|August 18, 2020
PubMed
Summary

This study developed a predictive model to estimate normal tissue volume (NTV) in stereotactic radiosurgery (SRS) based on tumor size. The model accurately predicts NTV, aiding in selecting appropriate dose-fractionation to minimize radiation necrosis risk.

Keywords:
SRSbrainnomogramnormal tissuepredictionradiation necrosis

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.0K
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

16.1K

Related Experiment Videos

Last Updated: Dec 11, 2025

Stereotactic Radiosurgery for Gynecologic Cancer
10:35

Stereotactic Radiosurgery for Gynecologic Cancer

Published on: April 17, 2012

18.5K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.0K
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

16.1K

Area of Science:

  • Radiotherapy and Oncology
  • Medical Physics
  • Neurosurgery

Background:

  • Dose-fractionation in stereotactic radiosurgery (SRS) for intracranial metastases is influenced by target lesion size and normal tissue volume (NTV).
  • Increased NTV is correlated with the risk of brain radiation necrosis (RN).

Purpose of the Study:

  • To develop and validate a predictive model for estimating NTV based on target lesion diameter from MRI.
  • To provide a tool for selecting optimal SRS dose-fractionation schemes to mitigate RN risk.

Main Methods:

  • Extracted conformity index, gradient index, and a scaling factor from historical SRS treatment plans.
  • Approximated target lesions as spherical volumes and defined NTV as a function of target diameter and dose fall-off.
  • Validated the predictive model using linear regression against actual NTV from 39 independent SRS plans.

Main Results:

  • The predictive model accurately relates lesion diameter to NTV volume (R²=0.942 for 3D, R²=0.911 for 2D).
  • Both 3D and 2D diameter estimates significantly predicted actual NTV, demonstrating model efficacy.
  • The model provides a direct link between lesion size, NTV, and potential RN risk.

Conclusions:

  • A knowledge-based method for NTV prediction in intracranial SRS was successfully developed.
  • The model serves as a valuable decision support tool for clinicians during SRS treatment planning.
  • This approach aids in appropriate dose-fractionation selection to minimize normal brain tissue complications.