Exploring trade‐offs in treatment planning for brain tumor cases with a probabilistic definition of the clinical target volume

Abstract

This study demonstrates how a novel probabilistic clinical target volume (CTV) concept-the clinical target distribution (CTD)-can be used to navigate the trade-off between target coverage and organ sparing with a semi-interactive treatment planning approach. Methods: Two probabilistic treatment planning methods are presented that use tumor probabilities to balance tumor control with organ-at-risk (OAR) sparing. The first method explores OAR dose reduction by systematically discarding x% of CTD voxels with an unfavorable dose-to-probability ratio from the minimum dose coverage objective. The second method sequentially expands the target volume from the GTV edge, calculating the CTD coverage versus OAR sparing trade-off after dosing each expansion. Each planning method leads to estimated levels of tumor control under specific statistical models of tumor infiltration: an independent tumor islets model and contiguous circumferential tumor growth model. The methods are illustrated by creating proton therapy treatment plans for two glioblastoma patients with the clinical goal of sparing the hippocampus and brainstem. For probabilistic plan evaluation, the concept of a dose-expected-volume histogram is introduced, which plots the dose to the expected tumor volume ⟨v⟩ considering tumor probabilities. Results: Both probabilistic planning approaches generate a library of treatment plans to interactively navigate the planning trade-offs. In the first probabilistic approach, a significant reduction of hippocampus dose could be achieved by excluding merely 1% of CTD voxels without compromising expected tumor control probability (TCP) or CTD coverage: the hippocampus D 2% dose reduces with 9.5 and 5.3 Gy for Patient 1 and 2, while the TCP loss remains below 1%. Moreover, discarding up to 10% of the CTD voxels does not significantly diminish the expected CTD dose, even though evaluation with a binary volume suggests poor CTD coverage. In the second probabilistic approach, the expected CTD D ⟨98%⟩ and TCP depend more strongly on the extent of the high-dose region: the target volume margin cannot be reduced by more than 2 mm if one aims at keeping the expected CTD D ⟨98%⟩ loss and TCP loss under 1 Gy and 2%, respectively. Therefore, there is less potential for improved OAR sparing without compromising TCP or expected CTD coverage. Conclusions: This study proposes and implements treatment planning strategies to explore trade-offs using tumor probabilities.

Robust optimization strategies for contour uncertainties in online adaptive radiation therapy

“…For example, the meaning of the D30% is unclear when the structure volume is unknown. We therefore employ the concept of dose-at-expected-volume-histograms (Buti et al 2023) to introduce the notion of DEVH objectives, with the expected volume of a contour defined as the sum of its voxels' marginal probabilities. When imposing for instance a DEVH objective with volume level 30% we, instead of neglecting 30% of the contour volume, neglect 30% of the expected contour volume.…”

“…For example, the CTD was accounted for in this way combined with a conventional worst-case optimization method for set-up and range uncertainties in proton therapy (Buti et al 2021). In a later study, the CTD was used in the objective function by heuristically allowing the optimizer to select a subset of the voxels for which to penalize underdosage, based on their tumor probabilities (Buti et al 2023). In addition, integer programming methods have been proposed to mitigate contouring uncertainties in the optimization of high-dose-rate brachytherapy (Balvert et al 2019).…”