Applications of Machine Learning to the High-Dose-Rate Cervical Brachytherapy Workflow: Applicator Prediction and Late Toxicity Modelling
Creators: name: Stenhouse, Kailyn
Publisher: name: Science
Year: 2024
Subjects: subject: gynecologic brachytherapy
subject: medical physics
subject: artificial intelligence
subject: machine learning
subject: toxicity modelling
subject: Artificial Intelligence
subject: Physics--Radiation
subject: Biophysics--Medical

description: Cervical cancer is a significant global health issue, with brachytherapy being crucial for treating locally advanced disease. Brachytherapy involves inserting an applicator through the vaginal cavity to escalate radiation dose to the affected areas. Applicator geometries vary and impact the achievable dose distribution. Limited criteria exist to guide applicator selection, making it dependent on physician experience, presenting challenges in selection consistency and outcome variability. Suboptimal treatment may increase the likelihood of adverse effects post-treatment that impact quality of life. Additionally, there are currently limited comprehensive predictive models for treatment toxicities based on multi-institutional datasets. Machine learning can identify complex relationships and generate predictive models, and thus this thesis explored it’s potential to enhance the cervical cancer brachytherapy workflow by developing tools that assist physicians in making more informed treatment decisions. We first developed a decision-support tool for selecting the optimal treatment applicator, including hybrid interstitial needle arrangement. Using algorithm comparison and analyzing feature importance on retrospective data, we identified that boosted tree-based models combined with geometric features of the target volume provided the highest predictive accuracy. These models, validated through a prospective study, demonstrated comparable accuracy to expert radiation oncologists, with accuracies of 70% for applicator prediction and 82.5% for hybrid interstitial needle prediction. Machine learning predictions improved organ-at-risk dose compared to clinical predictions, demonstrating potential dosimetric benefit. Using a robust, multi-institutional dataset, we developed a Bayesian network approach to model late treatment toxicities, aiding in personalized and adaptive treatment strategies. We first developed a customized simulated annealing framework for optimizing network structures, integrating expert knowledge to ensure generated models have a logical structure that represent current clinical understanding. This framework demonstrated predictive performance comparable to out-of-box optimization methods, while providing a highly interpretable network structure. We explored potential clinical applications of these networks, including risk stratification, risk factor identification, and centre bias analysis. This thesis highlights novel applications of machine learning in supporting key aspects of the brachytherapy workflow for cervical cancer, potentially enhancing treatment quality and consistency, reducing treatment errors, and providing powerful clinical decision-support tools to physicians.