CyberKnife; GTV median dose; Lung SBRT; Monte-Carlo re-prescription; Predictive nomogram; Humans; Linear Models; Lung/radiation effects; Monte Carlo Method; Multivariate Analysis; Organs at Risk; Radiation Dosage; Radiotherapy Planning, Computer-Assisted/methods; Algorithms; Radiosurgery/methods; Radiotherapy Dosage; Lung; Radiosurgery; Radiotherapy Planning, Computer-Assisted; Biophysics; Radiology, Nuclear Medicine and Imaging; Physics and Astronomy (all); General Physics and Astronomy; General Medicine
Abstract :
[en] 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.
Disciplines :
Physics
Author, co-author :
Dechambre, D; Liege University Hospital, Department of Radiation Oncology, Liège, Belgium. Electronic address: ddechambre@chu.ulg.ac.be
Janvary, L Z; Debrecen University Hospital, Department of Radiation Oncology, Debrecen, Hungary
Jansen, Nicolas ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
Berkovic, P; University of Leuven, Department of Oncology, Leuven, Belgium
MIEVIS, Carole ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
Baart, Véronique ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
CUCCHIARO, Séverine ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
Coucke, Philippe ; Université de Liège - ULiège > Département des sciences cliniques > Radiothérapie
Gulyban, A; Liege University Hospital, Department of Radiation Oncology, Liège, Belgium
Language :
English
Title :
Prediction of GTV median dose differences eases Monte Carlo re-prescription in lung SBRT.
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