Highlights
- •Dose discrepancies between type A and B algorithms might be relevant in lung SBRT.
- •Organs-at-risk dose showed strong linear correlation between type A and B algorithms.
- •Nomogram predicts type A to B dose calculation differences in GTV median dose.
- •Good prediction on the GTV D50% re-prescription can be achieved prior planning.
Abstract
Background and Purpose
The use of Monte Carlo (MC) dose calculation algorithm for lung patients treated with
stereotactic body radiotherapy (SBRT) can be challenging. Prescription in low density
media and time-consuming optimization conducted CyberKnife centers to propose an equivalent
path length (EPL)-to-MC re-prescription method based on GTV median dose. Unknown at
the time of planning, GTV D50% practical application remains difficult. The current
study aims at creating a re-prescription predictive model in order to limit conflicting
dose value during EPL optimization.
Material and Methods
129 patients planned with EPL algorithm were recalculated with MC. Relative GTV_D50%
discrepancies were assessed and influencing parameters identified using wrapper feature
selection. Based on best descriptive parameters, predictive nomogram was built from
multivariate linear regression. EPL-to-MC OARs near max-dose discrepancies were reported.
Results
The differences in GTV_D50% (median 10%, SD: 9%) between MC and EPL were significantly
(p < .001) impacted by the lesion’s surface-to-volume ratio and the average relative
electronic density of the GTV and the GTV’s 15 mm shell. Built upon those parameters,
a nomogram (R2 = 0.79, SE = 4%) predicting the GTV_D50% discrepancies was created.
Furthermore EPL-to-MC OAR dose tolerance limit showed a strong linear correlation
with coefficient range [0.84–0.99].
Conclusion
Good prediction on the required re-prescription can be achieved prior planning using
our nomogram. Based on strong linear correlation between EPL and MC for OARs near
max-dose, further restriction on dose constraints during the EPL optimization can
be warranted. This a priori knowledge eases the re-prescription process in limiting
conflicting dose value.
Keywords
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Article info
Publication history
Accepted:
December 3,
2017
Received in revised form:
November 8,
2017
Received:
August 18,
2017
Identification
Copyright
© 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
