TY  - JOUR
AU  - Wahl, Niklas
AU  - Hennig, Philipp
AU  - Wieser, Hans-Peter
AU  - Bangert, Mark
TI  - Analytical probabilistic modeling of dose-volume histograms.
JO  - Medical physics
VL  - 47
IS  - 10
SN  - 2473-4209
CY  - College Park, Md.
PB  - AAPM
M1  - DKFZ-2020-01567
SP  - 5260-5273
PY  - 2020
N1  - #EA:E040#LA:E040#2020 Oct;47(10):5260-5273
AB  - Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, e. g., dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data.We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, i. e., α-DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and singlefraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling.The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90
LB  - PUB:(DE-HGF)16
C6  - pmid:32740930
DO  - DOI:10.1002/mp.14414
UR  - https://inrepo02.dkfz.de/record/157338
ER  -