IMRT optimization including random and systematic geometric errors based on the expectation of TCP and NTCP

Abstract

The purpose of this work was the development of a probabilistic planning method with biological cost functions that does not require the definition of margins. Geometrical uncertainties were integrated in tumor control probability (TCP) and normal tissue complication probability (NTCP) objective functions for inverse planning. For efficiency reasons random errors were included by blurring the dose distribution and systematic errors by shifting structures with respect to the dose. Treatment plans were made for 19 prostate patients following four inverse strategies: Conformal with homogeneous dose to the planning target volume (PTV), a simultaneous integrated boost using a second PTV, optimization using TCP and NTCP functions together with a PTV, and probabilistic TCP and NTCP optimization for the clinical target volume without PTV. The resulting plans were evaluated by independent Monte Carlo simulation of many possible treatment histories including geometrical uncertainties. The results showed that the probabilistic optimization technique reduced the rectal wall volume receiving high dose, while at the same time increasing the dose to the clinical target volume. Without sacrificing the expected local control rate, the expected rectum toxicity could be reduced by 50% relative to the boost technique. The improvement over the conformal technique was larger yet. The margin based biological technique led to toxicity in between the boost and probabilistic techniques, but its control rates were very variable and relatively low. During evaluations, the sensitivity of the local control probability to variations in biological parameters appeared similar for all four strategies. The sensitivity to variations of the geometrical error distributions was strongest for the probabilistic technique. It is concluded that probabilistic optimization based on tumor control probability and normal tissue complication probability is feasible. It results in robust prostate treatment plans with an improved balance between local control and rectum toxicity, compared to conventional techniques.

Comparisons of treatment optimization directly incorporating random patient setup uncertainty with a margin‐based approach

“…The 3 mm random setup uncertainty is representative of those used in related literature 4,6,15,17,30,32 and is consistent with, though somewhat larger than, what is observed in inhouse internal marker-based daily alignment protocol. Reported random setup uncertainty values have significant variation, ranging from 0.9 to 5.1 mm.…”

“…Several authors have studied PTP. [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] The general finding of these studies is that PTP reduces normal tissue and organ at risk ͑OAR͒ doses while maintaining the same target coverage as marginbased plans.…”

Radiobiologically guided optimisation of the prescription dose and fractionation scheme in radiotherapy using BioSuite

“…In all cases, one must keep in mind that radiobiological optimisation of a given treatment plan by means of changing the prescription dose and/or fractionation schedule is not a substitute for good planning in the first place. Ideally, inverse planning based on radiobiological criteria [46,47] could yield true radiobiologically optimal plans which, by definition, could not be further improved in BioSuite.…”

Coverage optimized planning: Probabilistic treatment planning based on dose coverage histogram criteria

“…Baum et al 19 and Sir et al 20 explored objective functions in which each voxel is weighted by a coverage probability, i.e., a probability that the voxel will be included in a target or organ at risk ͑OAR͒ structure. In contrast, Yang et al 21 and Witte et al 22 employed objective functions that are probabilistically weighted functions of biological metrics: Equivalent uniform dose, tumor control probability, and normal tissue complication probability. In simple cases, Unkelbach and Oelfke 24 showed that the above two PTP approaches, i.e., the PWDD and PWOF approaches, are mathematically related.…”

“…A number of authors have explored PWOF approaches, including Löf et al, 18 Baum et al, 19 Sir et al, 20 Yang et al, 21 and Witte et al 22 Löf et al 18 proposed a comprehensive adaptive control framework for fractionated radiotherapy that incorporated random setup uncertainties, and demonstrated the occurrence of horns in the resulting dose distributions. This work was pursued by Rehbinder et al, 23 who used fluence modulation to compensate for random errors and couch corrections for systematic errors.…”