Intensity-Modulated Radiation Therapy Dose Prescription, Recording, and Delivery: Patterns of Variability Among Institutions and Treatment Planning Systems

Self Cite

The purpose was to study correlations amongst IMRT DVH evaluation points and how their relaxation impacts the overall plan. 100 head‐and‐neck cancer cases, using the Eclipse treatment planning system with the same protocol, are statistically analyzed for PTV, brainstem, and spinal cord. To measure variations amongst the plans, we use (i) interquartile range (IQR) of volume as a function of dose, (ii) interquartile range of dose as a function of volume, and (iii) dose falloff. To determine correlations for institutional and ICRU goals, conditional probabilities and medians are computed. We observe that most plans exceed the median PTV dose (average normalD50 = 104% prescribed dose). Furthermore, satisfying normalD50 reduced the probability of also satisfying normalD98, constituting a negative correlation of these goals. On the other hand, satisfying normalD50 increased the probability of satisfying normalD2, suggesting a positive correlation. A positive correlation is also observed between the PTV normalV105 and normalV110. Similarly, a positive correlation between the brainstem normalV45 and normalV50 is measured by an increase in the conditional median of normalV45, when normalV50 is violated. Despite the imposed institutional and international recommendations, significant variations amongst DVH points can occur. Even though DVH aims are evaluated independently, sizable correlations amongst them are possible, indicating that some goals cannot be satisfied concurrently, calling for unbiased plan criteria.PACS number(s): 87.55.dk, 87.53.Bn, 87.55.Qr, 87.55.de.

“…Equivalent uniform dose (EUD) is computed as

EUD = {false(\sum_{i} {normalv}_{i} {normalD}_{i}^{α} false)}^{1 / α}

, where

{normalv}_{normali}

is the i‐th partial volume receiving the dose

{normalD}_{i}

and an exponent of

α = ‐ 10

for the tumor (1) . The EUD was computed for equal dose per fraction schedules based on the biologically effective dose method (1) .…”

Section: Appendicesmentioning

confidence: 99%

“…The EUD was computed for equal dose per fraction schedules based on the biologically effective dose method (1) . Figure A.2(right) shows the EUD to PTV for all patients.…”

Section: Appendicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On correlations in IMRT planning aims

Roy

Das

Nohadani

2016

Self Cite

“…This is because the final dose distribution is dependent on the geometry of the organs at risk (OAR) with respect to the target. Other factors that can potentially affect the quality of the final plan are differences in dose prescription,10 treatment technique, and planner experience 11. It is because of these reasons that plan quality evaluation has been based on user experience primarily making the development of quantitative tools necessary.…”

Section: Introductionmentioning

confidence: 99%

Creation of knowledge‐based planning models intended for large scale distribution: Minimizing the effect of outlier plans

Aviles

Marcos²,

Sasaki

et al. 2018

Knowledge‐based planning (KBP) can be used to estimate dose–volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not properly addressed. This work used RapidPlan™ to create models for the prostate and head and neck intended for large‐scale distribution. Potential outlier plans were identified by means of regression analysis scatter plots, Cook's distance, coefficient of determination, and the chi‐squared test. Outlier plans were identified as falling into three categories: geometric, dosimetric, and over‐fitting outliers. The models were validated by comparing DVHs estimated by the model with those from a separate and independent set of clinical plans. The estimated DVHs were also used as optimization objectives during inverse planning. The analysis tools lead us to identify as many as 7 geometric, 8 dosimetric, and 20 over‐fitting outliers in the raw models. Geometric and over‐fitting outliers were removed while the dosimetric outliers were replaced after re‐planning. Model validation was done by comparing the DVHs at 50%, 85%, and 99% of the maximum dose for each OAR (denoted as V50, V85, and V99) and agreed within −2% to 4% for the three metrics for the final prostate model. In terms of the head and neck model, the estimated DVHs agreed from −2.0% to 5.1% at V50, 0.1% to 7.1% at V85, and 0.1% to 7.6% at V99. The process used to create these models improved the accuracy for the pharyngeal constrictor DVH estimation where one plan was originally over‐estimated by more than twice. In conclusion, our results demonstrate that KBP models should be carefully created since their accuracy could be negatively affected by outlier plans. Outlier plans can be addressed by removing them from the model and re‐planning.

“…Generally speaking, radiotherapy centers worldwide will implement a commercial treatment planning system (TPS) from a small number of vendors that employ one of several different methods of dose calculation. Although this commercialization of software has undoubtedly improved consistency across centers, the above factors still exist alongside clinically significant variations, as shown by the results of large‐scale independent auditors1, 2, 3, 4, 5 and publication of interinstitutional comparisons 6. The systems themselves are also highly dependent on user‐defined reference data that is not immune to inadvertent change and/or corruption.…”

Section: Introductionmentioning

confidence: 99%

A clinical database to assess action levels and tolerances for the ongoing use of Mobius3D

Jolly

Dunn

Kenny

2016

In radiation therapy, calculation of dose within the patient contains inherent uncertainties, inaccuracies, limitations, and the potential for random error. Thus, point dose‐independent verification of such calculations is a well‐established process, with published data to support the setting of both action levels and tolerances. Mobius3D takes this process one step further with a full independent calculation of patient dose and comparisons of clinical parameters such as mean target dose and voxel‐by‐voxel gamma analysis. There is currently no published data to directly inform tolerance levels for such parameters, and therefore this work presents a database of 1000 Mobius3D results to fill this gap. The data are tested for normality using a normal probability plot and found to fit this distribution for three sub groups of data; Eclipse, iPlan and the treatment site Lung. The mean (μ) and standard deviation (σ) of these sub groups is used to set action levels and tolerances at μ ± 2σ and μ ± 3σ, respectively. A global (3%, 3 mm) gamma tolerance is set at 88.5%. The mean target dose tolerance for Eclipse data is the narrowest at ± 3%, whilst iPlan and Lung have a range of −5.0 to 2.2% and −1.8 to 5.0%, respectively. With these limits in place, future results failing the action level or tolerance will fall within the worst 5% and 1% of historical results and an informed decision can be made regarding remedial action prior to treatment.