Multimodality imaging-based response prediction and monitoring to improve clinical management of gastrointestinal stromal tumours

Abstract

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.