Crafting and refining high-dose-rate brachytherapy treatment plans for cervical cancer is a time-consuming process. In recent years, BRIGHT was developed, an AI-based automated treatment planning method that provides not just one, but a set of optimized, patient-specific treatment plans, each with a different trade-off between objectives of interest. BRIGHT's plans are optimized using protocols that define guidelines on the delivered doses. In this thesis, we explore an alternative approach using dose-response models. These models provide insights into the estimated outcomes and risks of a treatment plan. Additionally, they offer great adaptability by including external patient characteristics. Currently, the use of these models remains limited to a feedback role. However, we instead include these models in BRIGHT to optimize them directly. Using one Tumor Control Probability (TCP) model and five Normal-Tissue Complication Probability (NTCP) models, we designed and tested several dose-response objective formulations and optimization techniques. We found that with the current models, these new objectives are insufficient as a replacement for BRIGHT's protocol-based coverage and sparing objectives. The produced plans have greatly improved dose-response outcomes but fall short in protocol compliance. Extending the existing objectives rather than replacing them proved more favorable. Average model improvements around 0.004 for NTCP are observed among the best coverage-sparing plans satisfying the protocol. Additionally, by sacrificing some sparing in the protocol-satisfaction range, improvements around 0.005 are possible for TCP and NTCP. Moreover, these dose-response-focused plans show distinct differences in their dose distribution favoring the dose-response targets compared to regular BRIGHT. Ultimately, the improvements we obtained are only marginal, and the clinical implications of this are unclear. The covariates of the models used in this thesis mostly overlapped with BRIGHT's objectives and did not fully extract their potential. Nevertheless, this thesis proves that the concept is viable and builds a foundation for this technique for when more and better dose-response models become available.

Multimodal machine learning models can exploit complementary information from multiple data modalities. MultiFIX (Multimodal Feature engIneering eXplainable artificial intelligence) is a framework designed to construct partially interpretable multimodal models, providing explanations for both modality-specific features and each modality its contribution to the final prediction. However, it was shown to not scale effectively for tasks with extreme joint-modality dependence.

This thesis proposes an alternative training strategy that integrates knowledge of the features to be engineered, expressed as feature targets that guide the learning process. The strategy improves upon baseline performance, even when the feature targets are non-ideal. Since ground-truth feature targets are typically unavailable in real-world settings, the feature targets are optimised using the Gene-pool Optimal Mixing Evolutionary Algorithm. The optimised feature targets, though only loosely aligned with the ground-truth features, enables the alternative training method to surpass baseline MultiFIX performance on a three-gated XOR task.

The same approach was evaluated on simpler tasks, such as the single XOR and AND problems, where it achieved slightly lower but still comparable performance to the already strong baselines. Results indicate that this computationally intensive approach is most beneficial for problems characterised by high joint-modality dependence and complex feature interactions. Interestingly, closer alignment between the optimised and ground-truth feature targets did not consistently lead to higher MultiFIX performance. Consequently, future improvements are likely to stem from refining how feature targets are integrated into the training process, rather than from further optimisation of the targets themselves.

Cervical cancer affects about half a million women globally every year. The treatment of cervical cancer with the aim of healing mainly consists of surgery, radiation treatment, or a combination of radiation treatment with chemotherapy or hyperthermia. Radiation treatment is a type of treatment wherein a high dose of ionizing radiation is used to kill the tumor cells. The radiation dose is usually delivered in the form of External Beam Radiation Treatment (EBRT) with a linear accelerator followed by internal radiation treatment (brachytherapy) during which a small radioactive source is passed through an applicator and needles that are placed temporarily nearby the cervix. EBRT typically spans several weeks with daily sessions (often referred to as fractions), whereas brachytherapy typically consists of three or four fractions based on one to three implantations. The aim of the radiation treatment is to provide effective radiation to kill the tumor cells while sparing the nearby healthy tissue or Organs At Risk (OARs) as much as possible. This is achieved by treatment planning following the contouring of target volumes and OARs, on medical imaging scans, which typically are Computed Tomography (CT) and/orMagnetic Resonance Imaging (MRI)....

Solutions to many real-life optimization problems take a long time to evaluate. This limits the number of solutions we can evaluate. When optimizing with an Evolutionary Algorithm (EA) a frequently used approach is to approximate the objective using a surrogate function, replacing the time-consuming real evaluation. This surrogate model is combined with a so-called acquisition function, to select promising candidate solutions. The acquisition function balances the trade-off between exploration of parameter space and the exploitation of the surrogate. These candidates are subject to an expensive evaluation with the true objective function and are used to update the surrogate model. Iteratively applying this process can effectively optimize global optimization problems. In this work, we propose a new multi-objective optimization algorithm with inverse distance weighting as surrogate function, which we call IDW-SAEA (inverse distance weighting surrogate assisted evolutionary algorithm). We introduce a new objective to the optimization problem to improve exploration and reduce the complexity of the acquisition function. We show this algorithm is competitive with state-of-the-art kriging-based surrogate-assisted EAs on certain benchmark problems. Additionally, we use the algorithm to optimize a practical problem: a Finite Element Method simulation of the cervix region with applications in radiotherapy for cervical cancer.