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MU variability in CBCT-guided online adaptive radiation therapy.
Tanny, Sean; Dona Lemus, Olga M; Wancura, Joshua; Sperling, Nicholas; Webster, Matthew; Jung, Hyunuk; Zhou, Yuwei; Li, Fiona; Yoon, Jihyung; Podgorsak, Alexander; Zheng, Dandan.
Afiliação
  • Tanny S; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Dona Lemus OM; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Wancura J; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Sperling N; Department of Radiation Oncology, University of Toledo Medical Center, Toledo, Ohio, USA.
  • Webster M; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Jung H; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Zhou Y; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Li F; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Yoon J; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Podgorsak A; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
  • Zheng D; Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
J Appl Clin Med Phys ; : e14440, 2024 Jun 19.
Article em En | MEDLINE | ID: mdl-38896835
ABSTRACT

PURPOSE:

CBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large.

METHODS:

Dosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array.

RESULTS:

MU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose.

CONCLUSIONS:

MU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article