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

Abstract

This work ͑i͒ proposes a probabilistic treatment planning framework, termed coverage optimized planning ͑COP͒, based on dose coverage histogram ͑DCH͒ criteria; ͑ii͒ describes a concrete proofof-concept implementation of COP within the PINNACLE treatment planning system; and ͑iii͒ for a set of 28 prostate anatomies, compares COP plans generated with this implementation to traditional PTV-based plans generated with planning criteria approximating those in the high dose arm of the Radiation Therapy Oncology Group 0126 protocol. Let D v denote the dose delivered to fractional volume v of a structure. In conventional intensity modulated radiation therapy planning, D v has a unique value derived from the static ͑planned͒ dose distribution. In the presence of geometric uncertainties ͑e.g., setup errors͒ D v assumes a range of values. The DCH is the complementary cumulative distribution function of D v . DCHs are similar to dose volume histograms ͑DVHs͒. Whereas a DVH plots volume v versus dose D, a DCH plots coverage probability Q versus D. For a given patient, Q is the probability ͑i.e., percentage of geometric uncertainties͒ for which the realized value of D v exceeds D. PTV-based treatment plans can be converted to COP plans by replacing DVH optimization criteria with corresponding DCH criteria. In this approach, PTVs and planning organ at risk volumes are discarded, and DCH criteria are instead applied directly to clinical target volumes ͑CTVs͒ or organs at risk ͑OARs͒. Plans are optimized using a similar strategy as for DVH criteria. The specific implementation is described. COP was found to produce better plans than standard PTV-based plans, in the following sense. While target OAR dose tradeoff curves were equivalent to those for PTV-based plans, COP plans were able to exploit slack in OAR doses, i.e., cases where OAR doses were below their optimization limits, to increase target coverage. Specifically, because COP plans were not constrained by a predefined PTV, they were able to provide wider dosimetric margins around the CTV, by pushing OAR doses up to, but not beyond, their optimization limits. COP plans demonstrated improved target coverage when averaged over all 28 prostate anatomies, indicating that the COP approach can provide benefits for many patients. However, the degree to which slack OAR doses can be exploited to increase target coverage will vary according to the individual patient anatomy. The proof-of-concept COP implementation investigated here utilized a probabilistic DCH criteria only for the CTV minimum dose criterion. All other optimization criteria were conventional DVH criteria. In a mature COP implementation, all optimization criteria will be DCH criteria, enabling direct planning control over probabilistic dose distributions. Further research is necessary to determine the benefits of COP planning, in terms of tumor control probability and/or normal tissue complication probabilities.

Potential pitfalls of the PTV concept in dose-to-medium planning optimization

“…The PTV assumes inherently a uniform response of all points in the PTV, which is an approximation. However, this approximation is consistent with the approximation of assuming everything as water with scaled electronic density for dose calculation, leading to homogeneous dose distributions in the CTV even An elegant way to solve this inconsistency is to use a comprehensive robust optimizer [20,21], which avoids the need of a PTV. Every iteration (or once every  iteration) of the optimizer, dose to medium distribution should be computed taking into account explicitly random and systematic errors.…”

“…1 with the TomoTherapy system and the MC model TomoPen validated elsewhere [19][20][21] instead of our simple analytical approach. The PTV geometry was contoured in the standard cylindrical Virtual Water TM phantom provided with every TomoTherapy unit.…”

Coverage‐based treatment planning to accommodate deformable organ variations in prostate cancer treatment

“…A pDVH corresponding to coverage q is a virtual DVH created by connecting all D v with coverage probability q. The steps to generate a pDVH for a target or CTV can be referred to Gordon et al 14 In this study, a pDVH is a result of "dynamic" DVHs which are different in each virtual treatment course due to the different DVFs sampled from PDFs of PCA model.…”

Fast dose algorithm for generation of dose coverage probability for robustness analysis of fractionated radiotherapy

“…Gordon et al (2010) suggested a framework for probabilistic treatment planning where the concept of CTV dose coverage was reused from the formalism of margin design for PTV based planning. By means of dose coverage probability they demonstrated that probabilistic plans yields better target dose coverage compared with margin based plans without compromising organ at risk doses for prostate patients.…”

“…Given a priori distributions for the systematic and random uncertainties, a dose volume coverage map (DVCM) can thus be determined by scoring, for every simulated scenario, whether or not a given volume receives at least a certain dose (Gordon et al 2010). A DVCM is the full representation in the dose-volume domain of dose-populationhistograms (van Herk et al 2000, Gordon et al 2007.…”