Sparing Healthy Tissue and Increasing Tumor Dose Using Bayesian Modeling of Geometric Uncertainties for Planning Target Volume Personalization

An image‐guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool

“…An alternative modeling approach to the one used in this study, where cumulative targeting errors were added to a simulated distribution based on observed prostate and PSV HD max values, is the one used by Herschtal et al . to model personalized adaptive PTV margins.…”

“…This can indicate that the states of the TV targeting error nodes or the data used to generate the CPTs need to be further evaluated. 9 An alternative modeling approach to the one used in this study, where cumulative targeting errors were added to a simulated distribution based on observed prostate and PSV HD max values, is the one used by Herschtal et al 38 to model personalized adaptive PTV margins. Instead of using a 2.5 cm radius sphere to represent the prostate and using applied couch shift corrections to update margin widths and PTV centroid positions, actual CBCT prostate and seminal vesicle contours could be modified via expanding and contracting them as per the values from the simulated distributions of intrafraction, contouring, and couch shift error.…”

On the use of Bayesian statistics in the application of adaptive setup protocols in radiotherapy

“…According to Bayes' theorem, the distribution of the probability that a given patient has a precision r () and a mean value of m after measuring n fractions () is given by the product of the prior distribution containing the previous data from the patient population and the likelihood that, given n measurements for a specific patient, its displacement distribution is described by the parameters m and r Considering the likelihood function as a product of Gaussian distributions, it will have the following expression:…”

“…Lam et al 9 presented a method to use Bayesian statistics to adapt treatment margins to the geometrical uncertainties of each patient. Herschtal et al 10 developed a method of margin adaptation including time trends present during the course of the treatment. The method was applied to two patient cohorts, concluding that, by adapting CTV margins after eight fractions, population coverage would remain acceptable while reducing dose to healthy tissue by 20%.…”

Margins Matter: current radiation therapy practice and future directions

“…Van Herk's formula assumes that all errors, systematic and random are distributed normally and that the random error is the same across all patients . Herschtal et al have proposed a novel statistical approach to model individualised adaptive PTV margins for prostate cancer patients, building on their previous work demonstrating random errors caused by inter‐fraction variations are better modelled using an inverse gamma distribution. Their method for creating individualised adaptive PTV margins uses a Bayesian statistical approach to facilitate the incorporation of accumulated displacement information from treated fractions to predict the magnitude of future displacements.…”