Machine learning for knowledge-based dose-volume histogram prediction in prostate cancer

Machine learning for knowledge-based dose-volume histogram prediction in prostate cancer

Strijbis, Victor (TU Delft Mechanical, Maritime and Materials Engineering)

Perko, Z. (mentor)
Janssen, Tomas (mentor)
Heemink, A.W. (graduation committee)
Denkova, A.G. (graduation committee)

Delft University of Technology

Biomedical Engineering | Medical Physics

2018-12-12

Introduction:  Despite the vast amount of optimization algorithms, radiotherapy treatment planning remains a manual, time-consuming and iterative process. To increase plan standardization, we clinically use Pinnacle's autoplanner for several disease sites. However, this introduces new challenges: first, the autoplanner is not perfect and still requires substantial interaction from the radiotherapy technician (RTT). Second, it is difficult to judge whether a plan has indeed the most optimal trade-o_ between cure and toxicity, since the RTT has not worked the plan. Knowledge-based planning (KBP) could serve as a quality assurance tool to resolve these problems. It uses historical data (anatomical and dosimetric) from previous plans, to predict the likely dose distribution for the current patient. In this study, we construct an initial, simplistic KBP model that serves as the clinical practice. We then investigate of a variety of KBP modelling approaches to predict rectum dose-volume histograms (DVHs), in order to complement the current clinical practice in prostate cancer. Methods:  For model evaluation, we formulate a clinical tolerance criterion (TC) bandwidth based on a ground-truth set of existing radiotherapy plans. We evaluate on the overall prediction accuracy (RMS), the fraction of correctly predicted DVH bins (TC_α), anαd on the fraction of patients that have ≥ 90% of their DVH correctly predicted (TC_β). We use the overlap volume histogram (OVH) to encode for organ geometrical information, and use reduced order modelling (ROM) to capture the most important characteristics of the DVH and OVH. Optimization methods we use are Principal Component Analysis (PCA) eigenvalue RMS minimization, direct DVH RMS minimization, and TC_α and TC_β maximization. Results:  Analyses of the KBP clinical practice yielded training and testing errors of 81.4% and 80.8% for TC_α and 53.3% and 51.1% for TC_{β, with an RMS of 4.80 and 4.94 volume percentage [%]. Eigenvalue-optimization resulted TCα of 86.5% and 82.4%, and TCβ of 68.8% and 59.1%, with respective RMS of 2.82 % and 3.22 %. Direct DVH-optimization yielded TCα of 86.7% and 81.9%, and TCβ of 69.4% and 61.4%, with similar RMS. TCα and TCβ maximizers resulted TCα training and testing errors of 92.1% and 78.5%, and TCβ training and testing errors of and 84.3% and 53.4% respectively. Discussion:  The investigated models yielded significant improvements for direct eigenvalue- and DVH optimization methods. We have also been able to perform optimizations for the clinical goal metrics, showing promising results in training data. Because TCα- and TCβ- maximizers were unable generalize to perform well for unseen data, it is believed these metrics are too sensitive to be trained reliably, and more consistent data may be required for these optimizers to produce reliable test errors. Based on our findings, we advice the clinical practice to extend KBP-approaches to optimize for DVH-least squares.}

Radiotherapy
Machine learning
Dose-volume histogram
Overlap-volume histogram

http://resolver.tudelft.nl/uuid:1e4b0e97-1187-4a82-b562-a71b46f61f19

Student theses

master thesis

© 2018 Victor Strijbis