The strategy currently used to treat glioblastoma multiforme (GBM) patients, which mostly relies on population-based failure patterns, does not consider the important variability in such patterns reported in the literature. As part of the multidisciplinary efforts being made to develop personalized therapeutic approaches, numerical models of tumor growth and treatment are increasingly being used by different groups around the world. In this study, a new formalism relying on the proliferation-invasion model is developed to identify potential locations of GBM recurrences. The authors assess the sensitivity of the location of potential tumor recurrences to the input parameter values predicted for a given patient by varying those values using a Monte-Carlo based approach. Our approach is designed to be prospective in the sense that it relies on patient-specific imaging data that can be gathered in one single preradiotherapy imaging session.Methods:
The authors modeled the infiltration paths of glial cells using patient-specific diffusiontensorimaging (DTI) data. Nine GBM patients with preradiotherapy DTI data are considered in this study. The possible locations of tumor recurrences are determined by randomly selecting many ensembles of values for each of the growth and radiobiological parameters in the GBM growth model. A novel concept, the occurrence probability (OP), is introduced to assess the sensitivity of potential tumor recurrence locations to the input parameter values. For a given patient, the OP map is derived from a superposition of all potential tumor recurrence locations obtained with all sets of parameter values.Results:
For eight out of nine of patients, the authors have identified a statistically significant region where the OP is above 50%. For two patients, these high risk regions are found to be located at a distance greater than 3.9 cm from the border of the gross tumor volume highlighting the inaccuracy of current margins for some patients. The exact location and size of these volumes with OP > 50 % are, however, sensitive to the numberN of ensembles of parameter values for N ≲ 400. On the other hand, the authors have identified for each patient a threshold OP, the OPT, which defines a volume that converges more rapidly with increasing N. The OPT for each patient varies between 20% and 40%. The volume defined by OP > OPT may be an adequate candidate to define a personalized margin for radiotherapy treatment planning of GBM patients.Conclusions:
A new Monte-Carlo based formalism was described and used to assess the variability of sites of potential recurrence predicted by the proliferation-invasion model to input parameter values. The authors have shown that high risk areas could be consistently identified with a limited number of sets (N ≲ 400) of randomly chosen parameter values. A major strength of this formalism is its potential prospective nature. Although a validation of the accuracy of the model-predicted tumor recurrence location still remains to be done, our method is potentially applicable to orient patient-specific definition of margins.
This work was supported by the Fonds québécois de la recherche sur la nature et les technologies (FQRNT) and the Natural Sciences and Engineering Research Council of Canada (NSERC grant 357402). All simulations were run using the computing facilities of the Réseau québécois de calcul de haute performance (RQCHP). RQCHP also provided support in writing our current numerical code. The authors finally thank Professor Paul Charbonneau (Université de Montréal) for providing an early version of our numerical code.
II.A. Model description
II.A.1. Growth model
II.A.2. Treatment model
II.A.3. Monte Carlo method
II.A.4. Numerical aspects
II.B.3. Radiotherapy treatment plan
II.C. Assessment of the sensitivity of tumor recurrence location to input parameter values
II.C.1. Site of potential recurrence
II.C.2. Occurrence probability
II.C.3. Model-based progression volume
II.D. Assessment of convergence
III.A. Sensitivity of SPR location to input parameter values
III.B. Assessment of results convergence with increasing number N of simulations
- Medical imaging
- Radiation therapy
- Magnetic resonance imaging
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