학술논문
Modeling gamma knife radiosurgical toxicity for multiple brain metastases.
Document Type
Academic Journal
Author
Hsu EJ; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA. Electronic address: Eric.Hsu@UTSouthwestern.edu.; Yan Y; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA.; Timmerman RD; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA.; Wardak Z; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA.; Dan TD; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA.; Patel TR; Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, USA.; Vo DT; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA.; Stojadinovic S; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA. Electronic address: Strahinja.Stojadinovic@UTSouthwestern.edu.
Source
Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8407192 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0887 (Electronic) Linking ISSN: 01678140 NLM ISO Abbreviation: Radiother Oncol Subsets: MEDLINE
Subject
Language
English
Abstract
Background and Purpose: Radiation oncology protocols for single fraction radiosurgery recommend setting dosing criteria based on assumed risk of radionecrosis, which can be predicted by the 12 Gy normal brain volume (V12). In this study, we show that tumor surface area (SA) and a simple power-law model using only preplan variables can estimate and minimize radiosurgical toxicity.
Materials and Methods: A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rxn , and tumor longest axial dimension LAD (V12 ∼ Rx n 1.5 *LAD 2 ), was independently validated using a secondary 63-patient cohort with 302 brain metastases.
Results: Surface area was the best univariate linear predictor of V12 (adjR2 = 0.770), followed by longest axial dimension (adjR 2 = 0.755) and volume (adjR 2 = 0.745). The power-law model accounted for 90% variance in V12 for 1217 metastatic lesions (adjR 2 = 0.906) and 245 patients (adjR 2 = 0.896). The average difference ΔV 12 between predicted and measured V12s was (0.28 ± 0.55) cm 3 per lesion and (1.0 ± 1.2) cm 3 per patient. The power-law predictive capability was validated using a secondary 63-patient dataset (adjR 2 = 0.867) with 302 brain metastases (adjR 2 = 0.825).
Conclusion: Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Published by Elsevier B.V.)
Materials and Methods: A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rx
Results: Surface area was the best univariate linear predictor of V12 (adjR
Conclusion: Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Published by Elsevier B.V.)