# Probabilistic machine learning methods for automated radiation therapy treatment planning

**Time: **
Wed 2021-12-15 14.00

**Location: **
Sal F3 och , Lindstedtsvägen 26

**Video link: **
https://kth-se.zoom.us/j/68119542297

**Language: **
English

**Subject area: **
Applied and Computational Mathematics, Mathematical Statistics

**Doctoral student: **
Tianfang Zhang
, Matematisk statistik

**Opponent: **
Professor Steve Jiang, UT Southwestern Medical Center

**Supervisor: **
Professor Jimmy Olsson, Matematisk statistik

## Abstract

In this thesis, different parts of an automated process for radiation therapy treatment planning are investigated from a mathematical and computational perspective. Whereas traditional inverse planning is labor-intensive, often comprising several reiterations between treatment planner and physician before a plan can be approved, much of recent research have been aimed at using a data-driven approach by learning from historically delivered plans. Such an automated planning pipeline is commonly divided into a first part of predicting achievable values of dose-related quantities, and a second part of finding instructions to the treatment machine mimicking as best as possible the predicted values. Challenges associated with this type of prediction–mimicking workflow exist, however—for example, in typical applications, patient data is high-dimensional, scarce and has relatively low signal-to-noise ratio due to inter-planner variations, and significant information may be lost in the transition between prediction and mimicking.

We propose to address these challenges through better probabilistic modeling of the predictive inferences of dose-related quantities and increased accuracy of the optimization functions used for dose mimicking. In particular, starting with the disconnect between conventional planning objectives and evaluation metrics, in the first paper, we establish a framework for handling dose statistics as optimization function constituents. Subsequently, in the second and fourth papers, we present ways of predicting spatial dose and dose statistics, respectively, in a probabilistically rigorous fashion, the latter application relying on the similarity-based mixture-of-experts model developed in the third paper. As a nonparametric Bayesian regression model, equipped with a mean-field and stochastic variational inference algorithm, this mixture-of-experts model is suitable for managing complex input–output relationships and skewed or multimodal distributions. The second and fourth papers also introduce dose mimicking objectives able to leverage predictive distributions of spatial dose and dose statistics. In the fifth paper, we further build upon the probabilistic paradigm, merging the fields of multicriteria optimization and automated planning to create a semiautomatic alternative workflow in which certain manual intervention is possible. Lastly, in the sixth paper, we present a means of incorporating robustness against geometric uncertainties into an automated planning pipeline.