Direct optimization of the probability of lesion origin in proton treatment planning for low‐grade glioma patients

Background: In proton therapy of low-grade glioma (LGG) patients, contrastenhancing brain lesions (CEBLs) on magnetic resonance imaging are considered predictive of late radiation-induced lesions. From the observation that CEBLs tend to concentrate in regions of increased dose-averaged linear energy transfer (LET$_d$) and proximal to the ventricular system, the probability of lesion origin (POLO) model has been established as a multivariate logistic regression model for the voxel-wise probability prediction of the CEBL origin.

Purpose: To date, leveraging the predictive power of the POLO model for treat-
ment planning relies on hand tuning the dose and LET$_d$ distribution to minimize the resulting probability predictions. In this paper, we therefore propose automated POLO model-based treatment planning by directly integrating POLO calculation and optimization into plan optimization for LGG patients.

Approach: We introduce an extension of the original POLO model including a
volumetric correction factor, and a model-based optimization scheme featuring
a linear reformulation of the model together with feasible optimization functions based on the predicted POLO values. ... mehrThe developed framework is implemented in the open-source treatment planning toolkit matRad.

Results: Our framework can generate clinically acceptable treatment plans
while automatically taking into account outcome predictions from the POLO
model. It also supports the definition of customized POLO model-based objec-
tive and constraint functions. Optimization results from a sample LGG patient
show that the POLO model-based outcome predictions can be minimized under
expectable shifts in dose, LETd , and POLO distributions, while sustaining target
coverage (Δ$_{PTV}$ D95$_{RBE,fx}$ ≈ 0.00, Δ$_{GTV}$ D95$_{RBE,fx}$ ≈ 0.03), even when NTCP is strongly downregulated.

Conclusion: POLO model-based treatment plan optimization for LGG patients
can be implemented in a technically feasible way, alleviating the need to hand
tune the dose and LET$_d$ distribution. Future work should address multipatient follow-up studies.