Paper: Dose-volume histogram prediction using density estimation

From the abstract, in Physics in Medicine and Biology: “Knowledge of what dose-volume histograms can be expected for a previously unseen patient could increase consistency and quality in radiotherapy treatment planning.”

“We propose a machine learning method that uses previous treatment plans to predict such dose-volume histograms. The key to the approach is the framing of dose-volume histograms in a probabilistic setting.”

Authors: Johanna Skarpman Munter and Jens Sjölund. (Elekta Instrument AB, Center for Medical Image Science and Visualization (CMIV) and Department of Biomedical Engineering, Linköping University, Sweden).

Jens Sjölund (photo) is an industrial PhD student at Elekta and a participant in the Swedish Testbed for Innovative Radiotherapy.

Jens Sjölund