Gastrointestinal stromal tumours (GISTs) are rare mesenchymal neoplasms with a worldwide incidence of one or two per 100,000. The tumours affect the entire gastrointestinal tract, but most commonly the stomach and small intestine. Neoadjuvant tyrosine kinase inhibitors (TKI) are administered in a selection of GIST patients to attain size reduction of the primary tumour and improve chances of complete resection. Response monitoring in GISTs is complex due to the presence of intra- and intertumoral heterogeneity and lack of pathological criteria to define response. Nonetheless, it is of importance to evaluate the efficacy of TKI treatment at an early stage in order to optimise treatment. In particular, early cessation of ineffective treatment is of importance in these patients, preventing unnecessary side-effects and healthcare costs. Medical imaging plays an important role to non-invasively predict and monitor treatment response of GIST patients undergoing TKI-treatment. This thesis aims to improve understanding of contrast-enhanced computed tomography (CE-CT) and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT imaging parameters to allow prediction and monitoring of neoadjuvant treatment response in GIST patients.
In Chapter 1, the added value of CE-CT and [18F]FDG-PET imaging for early prediction and monitoring of treatment response in GISTs is investigated by means of a systematic literature review. Results of this study show that heterogeneous enhancement patterns on baseline CE-CT imaging were considered to be predictive for high-risk GISTs, reflecting neovascularisation and the presence of necrosis. Current CE-CT radiographic response criteria (i.e., RECIST 1.1 and Choi) are still lacking sensitivity and are prone to errors when predicting or monitoring treatment response. Metabolic changes on [18F]FDG-PET imaging seem to precede morphological changes in size in GIST lesions and were more strongly correlated with tumour response. Although CE-CT and [18F]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended.
Chapter 2 evaluates the efficacy of current radiological response criteria (RECIST 1.1, Choi and tumour volumetry) in predicting response to neoadjuvant systemic therapy, by comparing radiological response criteria with the achieved surgical benefit. Results show that size-based criteria (RECIST 1.1 and volumetry) accurately reflect surgical benefit in GIST patients treated with neoadjuvant systemic therapy (accuracy of 76.3% and 86.6% respectively) and are less prone to scanner and imaging protocol variabilities, when compared to the Choi criteria (68.4%).
In addition to volumetry, quantitative radiomic models using CE-CT and [18F]FDG-PET imaging features were trained to predict response at baseline. Preliminary results of this study are described in Chapter 3. The radiomic models presented in this study generally had a poor performance and can therefore not yet be applied in a clinical setting. To improve performance and generalisability, future research should focus on a bigger patient population and harmonisation of acquisition protocols. The conclusion of this thesis supports the utilisation of tumour diameters for radiological response assessment, where RECIST 1.1 response criteria had an accuracy of 80.0% to correctly predict volumetric response after the first response follow-up CE-CT scan. When properly executed, these manual measurements could aid in early surgical decision making.

Vertebral fractures are the most common osteoporotic fractures, with a prevalence of 12-20% in Europe, making them a major health problem because of the associated morbidity, mortality and costs. Without adequate treatment, vertebral fractures are often followed by subsequent fractures, leading to further invalidation and deterioration of health. Therefore, early detection of vertebral fractures is important so preventative treatment can be initiated.

Vertebral fracture assessment (VFA) using Dual-Energy X-ray Absorptiometry (DXA) equipment is an imaging technique in which a lateral image of the spine is made. With vertebral morphometry, vertebral heights are measured and fractures are identified when vertebral height is lower than expected. However, vertebral morphometry is time and labor-intensive, and is subject to inter-operator variability. Automating VFA using Artificial Intelligence (AI) could help to overcome these limitations. We employed a co-development approach, aiming to create an AI-based tool to automatically perform vertebral morphometry on VFA images to identify vertebral fractures.

Firstly, we conducted a literature review to investigate the current use of AI in quantitative DXA imaging in a broad sense (Chapter 2). Besides VFA, other quantitative parameters describing bone macrogeometry and microgeometry can be extracted from DXA images. Incorporating these risk factors into multivariate prediction models could improve the identification of those at risk of fracture and help in clinical decision making. Although still in development, AI has been successfully applied in aid of fracture risk assessment, showing promising results.

In Chapter 3, the results of our reader study are described, evaluating VFA with manual vertebral morphometry as it is currently performed. This study served as a baseline measurement to quantify the effort needed to perform manual VFA and assess the potential value of automating VFA. The average annotation time per VFA image was 259 seconds. Although the intraclass correlation for vertebral height measurements between different readers was high, inter-observer agreement for fracture classification was only poor to moderate.

Together with our industry partner, we developed an AI-based software tool to perform vertebral morphometry. This tool is still in development, and we evaluated its current performance and potential impact in the study described in Chapter 4. Although its current standalone performance is suboptimal and shows room for improvement, this initial investigation showed that automated VFA has the potential to significantly reduce the required reader time.

Finally, in Chapter 5 we reflect on our user-centered co-development process and the next steps to bring our AI tool to market. We believe that collaboration between academic healthcare institutions and industry are essential for successful development of AI products. Validation of these products throughout the development process and actively involving intended users should be done. In the near future, vertebral fracture assessment can be supported by our AI-based application, potentially leading to lower annotation times and improving clinical workflow. However, further improvements have to be made and independent validation is required before market access.

In medical images intratumour heterogeneity well translates specific cancer cells properties in a fast and non-invasive way. Medical community and researchers have developed proper tools (texture features, shape features…) permitting the quantification and the analysis of this tumour heterogeneity in particular in PET images. Yet so far, both doctors and researchers have mainly focused on the use of those tools before assessing their relevance and robustness. In this study, a new heterogeneous PET phantom allowing an extensive understanding of the key concept of heterogeneity quantification in PET images is designed and partially developed. This phantom aims at representing a complex enough environment mimicking clinical conditions to properly challenge PET heterogeneity data extraction and quantification methods further than commercial phantom already do. This study focuses, first, on defining sound specifications, design methodology and manufacturing method for this new object. Second, the study focuses on designing and developing the defined solution. Third, a proof-of-concept analysis is conducted within the study to test and validate the developed PET phantom prototype. It can be concluded that the produced PET phantom was successfully designed and developed and can be used by other operators; yet, there is still room for improvement. Finally, besides developing a novel PET phantom, this study yields recommendations to improve the work done toward a more handy, flexible and realistic tool.