Related Experiment Video
Updated: May 3, 2026

Stereotactic Radiosurgery for Gynecologic Cancer
Published on: April 17, 2012
Isotoxic stereotactic reirradiation for recurrent pelvic cancers
Christopher J H Pagett1, John Lilley1, Christopher O'Hara1
1Department of Medical Physics, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Background And Purpose:
Reirradiation is clinically challenging, requiring a balance between delivery of dose to tumour while respecting cumulative organ at risk (OAR) dose constraints. Standard prescriptions are often conservative, ignoring patient variability in achievable OAR doses. Isotoxic radiotherapy individualises treatment by delivering the highest equieffective dose in 2 Gy per fraction (EQD2Gy) while meeting OAR constraints. This technical feasibility study assessed isotoxic pelvic reirradiation using cumulative OAR constraints, the original dose distribution as background, and voxel-by-voxel EQD2Gy optimisation.
Materials And Methods:
Data from 30 patients previously treated with pelvic stereotactic body radiotherapy (SBRT) at three UK centres were included. OARs were delineated on both previous and reirradiation image sets and deformably registered. Previous dose was mapped to the current image set and used as background dose for SBRT planning, following published methods. Initial 25 Gy in five fractions (25 Gy/5#) plans were generated for all patients, with further isotoxic dose escalation conducted up to a maximum of 50 Gy (fraction number fixed) until cumulative EQD2Gy constraints were reached.
Results:
For 25 of 30 patients, clinically acceptable isotoxic plans were obtained, with 23 exceeding the standard UK reirradiation prescription dose of 30 Gy/5#. The median isotoxic prescription was 42 Gy/5#, with four patient plans reaching the upper evaluated limit of 50 Gy. Vessels and the sacral plexus were most frequently dose limiting.
Conclusion:
This study highlighted the feasibility of isotoxic pelvic reirradiation and supports further investigation into automation and prediction models to streamline implementation in clinical practice.

