Artificial Intelligence in Radiotherapy: Probabilistic Deep Learning for Dose Prediction and Anatomy Modeling

This thesis addresses two major challenges in modern radiotherapy workflows: the slow computation speed of dose prediction algorithms and the insufficient modeling of anatomical variations during and between treatment fractions. Current photon and proton therapy plans rely on pre-treatment computed tomography (CT) scans obtained days before the start of treatment. Inter-fraction anatomical changes, intra-fraction organ motion, and setup errors compromise treatment accuracy and may unnecessarily irradiate healthy tissue. Existing mitigation strategies—such as target margins in photon therapy and robust optimization in proton therapy—only partially address these uncertainties and are limited by the lack of realistic anatomical models and fast dose prediction methods.

The first part of this work presents millisecond-scale dose prediction algorithms for proton pencil beams and photon beams using deep learning. Chapter 2 introduces the Dose Transformer Algorithm (DoTA), a model that predicts proton beamlet doses by combining convolutional neural networks with a transformer backbone that captures both spatial features and beam energy information. DoTA achieves gamma pass rates above 99% while reducing computation time by four orders of magnitude compared to Monte Carlo simulations. Chapter 3 extends this approach to photons with the improved Dose Transformer Algorithm (iDoTA), which maps projected beam geometries to 3D dose distributions. iDoTA estimates full VMAT dose distributions in seconds with state-of-the-art accuracy, significantly accelerating conventional photon treatment planning.

The second part focuses on anatomical variations. Chapter 4 presents the Daily Anatomy Model (DAM), a probabilistic deep learning framework that generates patient-specific inter-fraction deformations of planning CT images based on population data. DAM captures correlated movements with few latent variables, accurately reproducing prostate volume and center-of-mass variations observed in repeat CT scans, and enabling robust treatment planning against daily anatomical changes. Chapter 5 models intra-fraction respiratory motion using variational and adversarial autoencoders, including a semi-supervised extension for joint signal classification and generation. A novel time-series compression method reduces multi-dimensional breathing cycles to low-dimensional vectors while preserving high-resolution reconstruction. These models generate realistic, class-specific breathing signals, supporting simulation of target motion during radiation delivery.

Chapter 6 applies these anatomical models to simulate interplay effects in Intensity Modulated Proton Therapy (IMPT), arising from interactions between tumor motion and scanning beam movement. Using both simple sinusoidal and deep learning-generated breathing signals, the analysis quantifies how small variations in respiratory period affect local dose distributions. The results highlight that conventional planning approaches, including 4DCT and Internal Target Volume (ITV) plans, often fail to achieve clinically required robustness, underscoring the need for individualized modeling.

In conclusion, this thesis provides methods to predict dose deposition with millisecond speed and simulate realistic anatomical variations for both inter- and intra-fraction motion. These contributions enable more accurate robustness evaluation, support future online adaptive workflows, and offer a foundation for integrating deep learning-based dose and anatomy models into clinical radiotherapy. Future research should focus on coupling these algorithms with existing treatment planning systems and validating their performance in diverse clinical scenarios.