Improving IMRT dose accuracy via deliverable Monte Carlo optimization for the treatment of head and neck cancer patients

Abstract

The purpose of this work is to investigate the effect of dose-calculation accuracy on head and neck (H&N) intensity modulated radiation therapy (IMRT) plans by determining the systematic dose-prediction and optimization-convergence errors (DPEs and OCEs), using a superposition/convolution (SC) algorithm. Ten patients with locally advanced H&N squamous cell carcinoma who were treated with simultaneous integrated boost IMRT were selected for this study. The targets consisted of gross target volume (GTV), clinical target volume (CTV), and nodal target volumes (CTV nodes). The critical structures included spinal cord, parotid glands, and brainstem. For all patients, three IMRT plans were created: A: an SC optimized plan (SCopt), B: an SCopt plan recalculated with Monte Carlo [MC(SCopt)], and C: an MC optimized plan (MCopt). For each structure, DPEs and OCEs were estimated as DPE(SC)=D(B)-D(A) and OCE(SC)=D(C)-D(B) where A, B, and C stand for the three different optimized plans as defined above. Deliverable optimization was used for all plans, that is, a leaf-sequencing step was incorporated into the optimization loop at each iteration. The range of DPE(SC) in the GTV D98 varied from -1.9% to -4.9%, while the OCE(SC) ranged from 0.9% to 7.0%. The DPE(SC) in the contralateral parotid D50 reached 8.2%, while the OCE(SC) in the contralateral parotid D50 varied from 0.91% to 6.99%. The DPE(SC) in cord D2 reached -3.0%, while the OCE(SC) reached to -7.0%. The magnitude of the DPE(SC) and OCE(SC) differences demonstrate the importance of using the most accurate available algorithm in the deliverable IMRT optimization process, especially for the estimation of normal structure doses.

Dosimetrischer Vergleich eines erweiterten Collapsed-Cone-Algorithmus mit einem neuen kommerziell verfügbaren Monte-Carlo-Algorithmus anhand klinisch relevanter Fälle

“…Several studies showed the impact of highly accurate MC dose calculations for lung and IMRT treatments [1,6,7,34]. However in most of these studies, MC dose calculation was performed with research tools and taken as a benchmark for the clinical dose computations.…”

“…From obtained dose values, the percentage dose prediction error (%DPE) was calculated [7,26]. Assuming that the MC algorithm is more accurate, %DPE ecc was defined as (3) where x is the given dose-volume index (maximum, minimum, or mean dose).…”

The effect of gantry spacing resolution on plan quality in a single modulated arc optimization

“…One of the studies ( 14 ) utilized two‐stage optimization process, where fluence maps optimization and their conversion into multileaf collimator (MLC) trajectories are separated. The other work utilized deliverable ( 15 – 18 ) optimization, where the fluence‐map‐to MLC trajectories conversion is incorporated into the optimization process, thereby explicitly accounting for MLC leaf position weights.…”

A GPU‐accelerated Monte Carlo dose calculation platform and its application toward validating an MRI‐guided radiation therapy beam model

“…In the clinic, g should be applicable to nearly any application requiring high dose accuracy, such as beam modeling, 21 IMRT optimization, 42 dosimeter response modeling, 28,43 dose validation, 19 dose accumulation, 44 among others. As an example, due to the three-source nature of the MRIdian system, quasi-3D dosimeters, such as ArcCHECK (Sun Nuclear Corp., Melbourne, FL), are quite useful for dosimetry measurements.…”